udacity machine learning engineer capstone project

I just completed Udacity’s Machine Learning Engineer Nanodegree, here’s what I think of it…

Tich Mangono

Two weeks ago, I completed and submitted my capstone project for the Udacity Machine Learning Engineer Nanodegree Program , so I would like to share my experience with others considering the program. The whole program took me 5 months to complete, which is a decent run considering that I have full time job with travel. According to Udacity, the average completion time is 6 months, but they also give you up to 12 months, just in case. I will start by sharing my overall experience and then in future posts I will dive deeper into each project, explaining the more technical bits, complete with Python code and comments. Some of the projects include learning more about customers through customer segmentation; predicting house market prices; training a self-driving car; and using deep learning to classify images. I will write more about this here and on my new blog , which is still under construction.

Great Program

Overall, I highly recommend this program to anyone who is serious about data science and machine learning, especially if you are a self-motivated learner who needs the flexibility of an online platform so that you can keep your day job while you improve your knowledge. I completed all 7 parts of the core curriculum (6 projects and an original capstone of my choice). The content covered significant ground on supervised, unsupervised, reinforcement and deep learning. The material was thoroughly presented with enough background to get even a beginner up to speed. However, you should at least have taken some Python courses and/or a data analytics or statistics course beforehand, otherwise you will hit the wall quite often and quite hard given some of the more complex parts later in the course. Personally, I already had some machine learning knowledge from reading the book An Introduction to Statistical Learning in R and taking introductory python courses from MITx on EdX.Even with prerequisite knowledge, you may still have some sleepless nights, but that is pretty normal in this field, and it is always worth it if you stick with it and push through. Generally, all of the projects were well-curated and logically presented to take you from introduction to understanding and practical implementation of the models in reasonable time.

The Nanodegree also offered some career-related tools such as resume review, tips on using Kaggle and advice on interviewing and updating your likedin profile but I honestly didn’t use these much as my goal was to squeeze as much as I can from the core machine learning content. Another program feature was getting a mentor to help you chart your program completion plan and check in with you when you hit roadblocks. However, I only got a mentor in the first couple of weeks and then got a message saying that I had completed the mentorship program, but it wasn’t clear why or how. I suspect it was because I appeared quite well-prepared given my background and performance on the first couple of projects. So I can’t really say I benefited from the mentoring aspect. Again, to be honest, I don’t think I needed a mentor per se, but I did need the really good, personalized and quick feedback to improve my core skills. This is really where Udacity shines! I got feedback sometimes within 30–45 minutes and reviewers left no stone unturned in terms of pointing out areas of improvement and additional reference materials.In fact, the feedback was so good, I saved it and I go back to it frequently to brush up on tricky concepts.

My personal Frustrations

I was, however, slightly disappointed by the project on deep learning and convolutional neural networks as it was literally quite convoluted, pun intended :). The first version of the leacture materials for this project was very hard to follow and felt like a hodge-podge of different pieces strung together. Granted, this topic was and still is new and changing quickly, but it could have been presented better the first time around. However, by the time I completed the Nanodegree, there was already a new set of more homogeneous lesson materials, using Keras instead TensorFlow as the main library for the models. This was a welcome change since Keras is more intuitive than TensorFlow, but the change came a litte too late for me :( . Nevertheless, I should add that even though it was hard to sort through the some of the strung-together lessons and climb the steep TensorFlow Learning curve, I ended up learning a lot more by cross-referencing with other materials such as the Stanford class CNNs for Visual Recognition (CS231) and the Fast.ai Practical Deep Learning for Coders.

The moral of this post is that if you are thinking of starting this program, go for it! Make sure you meet the prerequisites (Python, Statistics and Linear Algebra) and you will definitely come out wiser on the other end. I got value for both time and money. There are only a few programs with this level of high-quality material, instruction and a comprehensive feedback/review system. I am glad I spent my nights watching the videos, taking notes and coding it up. I also have a cool certificate to show for it:). I see this as only getting my feet wet and I am looking for the next level material. I have already begun to use my new machine learning skills at my current job and I will share some non-sensitive examples in the future. Until next time, happy coding!

Written by Tich Mangono

Passionate about public health, python, machine learning, and data science. I also share my ideas on https://tichmangono.github.io/

Text to speech

Udacity-ML-Capstone-Kaggle-Allstate

Machine learning engineer nanodegree, capstone project.

Bryan Luke Lathrop March 6, 2017

This project encapsulates my final project for the Udacity Machine Learning Nano-degree, and is based on the Kaggle competition, Allstate Claims Severity

The project is primarily an exercise in various machine learning techniques, with a goal of demonstrating the improvement that can be acheived by their use

It is suggested to start with a brief look at either JustStacking.ipynb or JustLinear.ipynb to get a feel for the code, and then progressing to the project writeup for a full overview of the project.

I should note that input data is not included, but may be downloaded at the competition link. Intermediate cache files are available to prevent the need to re-run several calculations that may take as much as several days. The data zip includes the original data files as well as my generated data. Download each of: data / cache / output , and unzip to the top level directory of the project.

• Project Proposal
• Final Report

Project completion date: 3/14/2017

Our 1,600 Hour JavaScript Coding Curriculum

Learning to code is no easy feat. It requires dedication, perseverance, and a lot of hard work. But with the right curriculum and support community, it is possible to go from coding novice to job-ready in around 1,600 hours.

Here at [Company Name], we‘ve designed an immersive 1,600 hour curriculum to take aspiring developers with little to no coding experience and transform them into skilled full-stack JavaScript developers, equipped to build complex web applications from the ground up.

The Burgeoning Demand for Software Developers

The software developer job market is expanding rapidly. According to the Bureau of Labor Statistics, employment of software developers is projected to grow 24 percent from 2020 to 2030 – much faster than the average across all occupations.

As technology continues to evolve and software touches every industry, the demand for trained developers far exceeds supply. Employers urgently need to fill over 1 million software development jobs in the US alone.

Our 1,600 hour full stack JavaScript curriculum is designed to provide ambitious learners with the exact skills employers need to capitalize on this huge growth opportunity.

Salaries Reflect High Demand

With far more open coding jobs than trained developers, employers are willing to pay premium salaries to attract and retain talent.

The average software developer earns over $110,000 a year in the US according to Glassdoor. Experienced full stack JavaScript developers can earn up to $152,000 a year.

Our curriculum gives students the knowledge to significantly increase their earning potential. With even 1-3 years experience under your belt, six figure salaries are achievable.

Our Curriculum Methodology

Effective learning requires an engaging, structured approach tailored to adult learners. We utilize proven instructional design methodologies like problem-based and peer learning rather than traditional lecture-based teaching.

Students tackle real-world problems, receive frequent feedback from experts, and learn collaboratively from fellow students. Challenging projects stretch skills while building a strong portfolio.

This emphasis on practical application and hands-on training accelerates competency development so students can contribute professionally right away.

The Components of Our Curriculum

Our comprehensive curriculum provides over 1,600 hours of career-focused education across 4 main sections:

1. Core Curriculum (400 hours)

Our expanded 400 hour core curriculum provides a rock-solid foundation in full-stack JavaScript development through a series of hands-on lessons, exercises, and projects.

Subjects covered:

Frontend Web Development

• HTML, CSS, JavaScript
• Responsive Web Design Principles
• Accessibility Best Practices
• Frontend Frameworks (React, Vue, Angular)
• State Management (Redux)
• UI/UX Design

Backend Web Development

• REST API Design
• SQL/NoSQL Databases
• Authentication/Authorization
• Messaging/Notifications
• Containerization (Docker)
• Cloud Infrastructure (AWS/Azure)

Software Engineering

• Git Version Control
• Agile Methodologies
• Automated Testing
• Continuous Integration/Delivery
• Design Patterns
• Debugging and Profiling
• Security Best Practices

We utilize cutting-edge educational platforms like Codecademy, FreeCodeCamp, and Udacity for interactive lessons. Students reinforce skills by building real-world apps.

2. Computer Science Theory (200 hours)

While hands-on training is critical, developers also need a strong computer science foundation. Our CS module focuses on data structures, algorithms, and complexity analysis through theoretical and practical lessons.

Subjects include:

• Recursion/Dynamic Programming
• Graph Theory
• Discrete Math
• Basic Probability/Statistics
• Computational Complexity
• Advanced Data Structures
• Core Algorithms

Students also tackle 100+ algorithm coding challenges on LeetCode to prepare for developer interviews.

3. Specialized Elective Tracks (400 hours)

After the core curriculum, students can specialize by choosing one of several elective tracks tailored to in-demand tech roles.

Tracks offered:

• AI/Machine Learning Engineer
• Blockchain Developer
• DevOps Engineer
• Game Developer
• Data Engineer
• Mobile App Developer

Each track adds an additional 400 focused hours so students can hone skills for their target career path.

4. Capstone Projects (200 hours)

The curriculum culminates in two immersive capstone projects:

1. Contribute to Open Source Projects

Students reinforce collaboration skills and build their reputation by contributing to popular open source JavaScript projects.

2. Build Solutions for Nonprofit Organizations

Student teams build full-scale web apps to support real nonprofit missions like digital accessibility or community organizing.

These projects enable students to integrate all their skills in a professional context, while building portfolio pieces and networking opportunities.

Supportive Developer Community

We foster an immersive peer-to-peer learning environment with significant support resources:

• Technical Mentors – Weekly 1-1 mentoring from professional developers
• Study Groups – Collaborative problem-solving and discussion
• Chat Rooms – Get instant feedback and help
• Meetups/Events – Connect online and in-person

The shared camaraderie through the intensity of bootcamp-style training creates bonds that last across careers. Our past graduates become part of our global alumni network, offering referrals, career advice, and continuing education.

Career Development Services

As students approach course completion, we provide robust career development resources:

• Portfolio/Resume Reviews – Ensure you stand out to employers
• Mock Interviews – Practice selling your skills
• GitHub Profile Polish – Perfect your coding brand
• Job Search Strategies – Find the right openings
• Salary Negotiations – Get paid fairly
• Technical Interview Prep – Study and practice

Plus, our employer network provides talent referrals and can connect students directly with open positions.

Flexible, Affordable Tuition

We offer full and part-time schedules so you can learn while working. And tuition financing options make our curriculum accessible:

• Income Share Agreements – Pay after you‘re hired
• Loans/Grants – Invest in future income
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Total cost can be as low as $12,000 depending on eligibility. With high developer salaries, tuition pays for itself quickly while boosting long-term wealth.

Transform Your Career in Just 16 Months

Our comprehensive 1,600 hour curriculum can transform novice learners into skilled, professional-ready software developers in as little as 16 months.

Students will walk away with:

✔️ In-demand tech skills commanding high salaries

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✔️ Access to a global alumni network

The tech industry needs millions more developers. We guarantee completion of this rigorous training will position you for success in this booming field.

Ready to skill up for a lucrative new developer career? Enroll now!

Dr. Alex Mitchell is a dedicated coding instructor with a deep passion for teaching and a wealth of experience in computer science education. As a university professor, Dr. Mitchell has played a pivotal role in shaping the coding skills of countless students, helping them navigate the intricate world of programming languages and software development.

Beyond the classroom, Dr. Mitchell is an active contributor to the freeCodeCamp community, where he regularly shares his expertise through tutorials, code examples, and practical insights. His teaching repertoire includes a wide range of languages and frameworks, such as Python, JavaScript, Next.js, and React, which he presents in an accessible and engaging manner.

Dr. Mitchell’s approach to teaching blends academic rigor with real-world applications, ensuring that his students not only understand the theory but also how to apply it effectively. His commitment to education and his ability to simplify complex topics have made him a respected figure in both the university and online learning communities.

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17 Best Data Science Courses Online in 2024 [Free + Paid]

In this article, we share the 17 best data science courses in 2024. Whether you’d like to land a job as a data scientist or want to further your career by learning new skills, we’ve included data science courses for all skill levels that are both free and paid.

In 2024 and beyond, data science continues to be essential for modern businesses that want to capitalize on the hidden insights within their data, and taking one of the best data science courses is an excellent way to enter the field.

And when you consider that the Bureau of Labor Statistics reports an average salary of more than $100,000 for data scientists, taking one of the best data science courses can be lucrative for your career prospects.

If you’re asking yourself the question, how can I learn data science ? One of the best approaches is to take one of the best data science courses. You can also combine this with building data science projects to round out your learning journey.   

Taking the best data science courses can also help strengthen your skills before taking exams for data science certifications .

So, if you’re ready, let’s dive into the best data science courses in 2024 to help you learn the skills you need to enter the data science job market .

Featured Data Science Courses [Editor’s Picks]

• [Udemy] The Data Science Course: Complete Data Science Bootcamp
• [Coursera] Data Science Specialization
• [DataCamp] Introduction to Data Science in Python 
• [TripleTen] Data Science Bootcamp

CUSTOM CODE - esyoh

• 17 Best Data Science Courses in 2024

1. [Udemy] The Data Science Course: Complete Data Science Bootcamp

Why we chose this course

Our findings show that this comprehensive data science class starts with an in-depth introduction to data and the field of data science before covering the essential components of modern data science, including statistics, math, and data visualization. You’ll also delve into machine learning and deep learning.

This course also includes a section on programming with Python to help you apply Python for linear regression, logistic regression, cluster analysis, and k-means clustering.

You’ll also be diving into Python’s rich ecosystem of data science libraries by getting hands-on with essential data science tools like Pandas, NumPy, Matplotlibs, and Seaborn. You’ll also learn to use Scikit-learn, TensorFlow, and Tableau to improve your data science skills. 

We also like that this course allows you to practice the Python concepts you’ve learned with real-life case studies. Overall, this is an ideal data science course for beginners as the instructors let you begin with the basics and build up as you progress.

Another aspect we really like about this course is that the creators maintain a vibrant community of data science students where you can interact with coursemates. They also have an active Q&A support forum where students can ask questions.

• Suitable for beginners
• Includes real-life business cases
• A vibrant community of students
• Active Q&A support

2. [Coursera] Data Science Specialization

Our findings show that this data science training is designed and taught by renowned professors from John Hopkins University. With over 280 hours of content, this data science specialization path has a comprehensive curriculum made up of 10 courses.

You will begin by learning how to use R and GitHub to manage your data science projects. And then, you will delve into how to extract usable data from the web, APIs, and databases. And also learn the principles involved in organizing data for analysis.

Other sections cover various topics like statistical inference, regression models, and machine learning concepts like overfitting, classification trees, and prediction functions. If some of these areas are new to you, consider including data science books in your study regime.

We also liked that the capstone project requires you to use real-world data sets to build a usable data product. And one of the requirements for the project is to create a presentation deck to showcase your findings. 

Another unique feature we discovered about the curriculum is that you will get graded data science quizzes and assignments with feedback from peers and instructors.

• It’s a comprehensive data science course
• Taught by Instructors from John Hopkins University
• Graded data science quizzes/assignments with feedback
• Capstone project you can showcase to potential employers
• The capstone project requires skills not covered in the course

3. [DataCamp] Introduction to Data Science in Python 

This will be the ideal course for you if you are curious about data science but not yet ready to invest any huge time and effort to study. With just 4 hours of content, the entire curriculum can be completed in a day or two. And it will give you enough information to decide whether to take a more in-depth course.

Our findings revealed that the course instructor is an engineering manager at Google. Also, if you consider yourself a visual learner, this course is even more helpful as the video lessons include loads of colorful images and illustrations. 

The course provides a concise introduction to data science in Python. It begins with lessons on the basics of Python. And then, you will learn how to load data in pandas and plot data with matplotlib. For the final section, you will practice creating three plot types: scatter plots, bar plots, and histograms. 

Hackr readers can also access an exclusive 25% discount on the annual Learn Premium and Teams subscriptions.

• Ideal for complete beginners
• Video lessons include many images and illustrations
• Very short course (about 4 hours)
• The course is quite brief, so it doesn’t go into as much detail as others on our list

4. [TripleTen] Data Science Bootcamp

Next up on our list is a fully-fledged online bootcamp in the form of the Data Science Program by TripleTen.

This really stood out to us for being a comprehensive 8-month journey into data science for students from diverse technical backgrounds. Plus, you don't need any prior experience in math, stats, or coding, which is really impressive.

This program is structured to transform you into a highly paid professional by covering a vast array of subjects from Python and Pandas fundamentals to advanced topics such as Machine Learning and Neural Networks.

It's also great to see that you'll complete 16 portfolio-worthy projects to boost your portfolio and help you gain real hands-on skills. I'm also really impressed by their externship concept, where you'll carry out a range of real-world projects at real companies.

These are the types of detail that will make your resume really stand out from the crowd.

This program also emphasizes the development of both technical and soft skills, preparing you for the professional world with abilities like time management, teamwork, and industry-specific practices.

The overall curriculum is divided into sprints, simulating a real-world tech company environment, and it's recommended you spend around 20 hours of study per week.

But, if you can put in the time, this boot camp also includes a dedicated employment preparation component featuring career mentoring, a Career Prep Course, Career Acceleration Externships, and Post-Offer Career Support, ensuring you are well-equipped for your job search and early career stages.

You also get the added benefit of TripleTen's money-back guarantee, where they'll refund you the course cost if you can't land a job six months after graduating.

But given their impressive 86% employment rates, with students landing roles with major tech companies like Tesla, Google, and Spotify, this program really sets itself apart if you're really dedicated to breaking into the world of data science from a standing start.

• In-depth data science education with 16 practical projects
• Balances technical skills with essential soft skills
• Real-world, sprint-based learning approach
• Extensive career support and mentoring

5. [Coursera] Data Science Fundamentals with Python/SQL

Our analysis of this online data science training revealed that it is designed to equip you with the skills you need to tackle advanced data science projects. Through our research, we discovered that this course is taught by senior data scientists from IBM.

The course is made up of five mini-courses. By the end of this first course, you will have a working knowledge of data science tools such as Jupyter Notebooks, R Studio, and Watson Studio.

The second course will teach you how to use Python for data science. It covers data structures, calling APIs, and using libraries like Pandas and NumPy. You will then work on a data science project in the third course, where you will be required to identify patterns and trends from a real-world data set. 

The fourth and fifth mini-courses cover statistical analysis techniques and SQL for data science. Some of the topics you will learn are hypothesis testing, descriptive statistics, probability distribution, regressions, and data visualization.

• Taught by senior data scientists from IBM
• Hands-on exercises with real-world data sets
• Gain a working knowledge of various data science tools
• Some slides contain spelling mistakes  

6. [edX] Harvard Professional Certificate in Data Science

This professional certificate in Data Science is offered by the Computer Science faculty of Harvard University on edX. It starts with an introduction to the basics of R programming. And also includes lessons on data visualization, bayesian statistics, probability, data wrangling, linear regression, inference, and predictive modeling.

After completing this course, you will know how to use data science tools like Tidyverse and ggplot2. The exercises will also give you practical experience using Unix/Linux, RStudio, Git, and GitHub.

We also like that this course includes a section on machine learning where you will use the data science techniques you’ve learned in previous sections to build a movie recommendation system. 

There's also a final capstone project that requires you to build a data product that you can include in your portfolio to demonstrate your skills to potential employees.

This course requires no prior knowledge of data science or programming, making it ideal for complete beginners. Also, there is an active community of students from around the world you can network with. And it’s also easy to get help when stuck on something.

• Taught by instructors from Harvard University
• An active community of students
• Build projects for your portfolio
• Course duration may be too long for some

7. [Udemy] Data Science A-Z: Hands-On Exercises and ChatGPT Bonus

Based on our observations, this is one of the best data science programs online. The course is created by Kirill Eremenko and his team. Kirill formerly worked at Deloitte and has taught over 2 million students on Udemy. 

We like that the content is divided into four sections to cover data visualization, modeling, data preparation, and communication. By following the course sections in sequence, you’ll learn the core skills of data science, including cleaning and preparing your data for analysis, creating basic visualizations, modeling your data, and curve-fitting.

You’ll also go in-depth into data mining with Tableau, along with how to build models with linear and logistic regression. You’ll also learn how to use a Cumulative Accuracy Profile (CAP) to assess your model. We also appreciated that the instructor mimics real-life business scenarios when teaching you about data preparation. 

Other key areas covered in this include SQL programming for data science, business intelligence tools, and the importance of ETL pipelines for data science, both pre and post-transformation.

At the end of this course, you also benefit from detailed lessons in communication, including tips and tricks on presentation and storytelling in data science. We really like this, as at their core, data scientists are storytellers, making these essential skills.

• Includes data science assignments with solutions
• Suitable for both beginners and advanced learners
• Learn data science presentation skills
• Uses real-life datasets
• None  

8. [Educative] Grokking Data Science

Based on our experience with other educative courses, we know that this data science course is 100% text-based. This makes it an ideal choice for those who prefer to learn by reading. Our research also revealed that the creator of this course is a senior software engineer at Microsoft. 

In this course, you will learn Python for data science, data visualization, and the fundamentals of statistics with topics like probability, Bayesian statistics, and machine learning algorithms. And you will also learn how to use popular Python libraries like Pandas, Numpy, and Matplotlib. 

The course also includes sections on machine learning where you will learn about machine learning algorithms and evaluating a model. There is also an end-to-end machine learning project, where you will learn about exploratory data analysis techniques, data processing, and fine-tuning parameters among others.

Each section of this course includes quizzes with answers and challenges to help you practice the concepts you learn. The final part of the course provides tips for landing a high-paying data science job and overcoming imposter syndrome.

• Ideal for those who prefer learning by reading
• Created by a senior engineer at Microsoft
• No IDE setup is required for coding practice
• Each section includes quizzes with answers
• Not ideal if you prefer video lessons

9. [Udacity] Data Science Nanodegree Program

Udacity’s Data Science Nanodegree program provides a hands-on approach to learning data science. This program will help you master topics such as natural language processing (NLP), running pipelines, transforming data, building models, designing experiments, and deployment.

Some of the projects you will build in this course include a recommendation engine, a disaster response pipeline, and a final capstone project of your choosing. 

Also, as part of the curriculum, you will be required to publish a data science blog post to practice your communication and data visualization skills.

You will also complete several lessons designed to help you develop software engineering skills that are essential for data scientists, like creating unit tests, code review, building, and using classes. 

Our research revealed the instructors for this course include senior data engineers from top tech companies such as Google and Netflix. You will also have access to career services, including GitHub portfolio review and LinkedIn profile optimization, to help you land a data science job and prepare for data science interview questions .

• Hands-on approach to learning data science
• Created by Senior Data Engineers from Google and Netflix
• Suitable for intermediate and advanced learners
• Access to career services

10. [Turing College] Data Science Career Program

As we evaluated this data science bootcamp, we realized the enormous value of working with professionals in the field. Turing College offers certifications for their graduates, but the real value comes from the one-on-one mentorship, interview prep, and help finding a job with your new skills.

This deep learning bootcamp is fully online, and it offers real-world benefits after you graduate. We rarely see a data science program boast these types of hiring rates. It ranks highly for our recommended deep-learning courses.

• More than 96% of Turing College graduates get a job within 6 months of graduation
• Includes a special focus on deep learning
• Helps students prepare a portfolio of their own work
• Mentorship from industry professionals
• Help with interview prep and salary negotiations
• Because it's a data science bootcamp, it has a higher cost than others we discuss

11. [StackSocial] The A to Z Data Science & Machine Learning Bundle

With this data science bundle, you get 7 separate courses, allowing you to curate your own data science learning journey. We also like that the StackSocial platform includes a note-taking area under the videos to help you keep track of important aspects of new or challenging topics. 

If you’re brand new to data science and programming, two courses focus on the fundamentals of Python and R. If you start with Python, you get a 6-hour introduction to Python fundamentals before covering the basics of NumPy, going in-depth with Pandas and briefly covering visualization with Maptlotlib.

Alternatively, there’s a comprehensive and practical 22-hour course on the practical side of data cleaning, processing, wrangling, manipulation, and visualization with R. You’ll also cover topics like vector coercion, data frames, R markdown, and more. 

If you have Python experience, there’s a 1-hour course on NumPy skills for data scientists and a nearly 10-hour course on using the Streamlit library to create data science and machine learning apps. Here you’ll learn to integrate with Matplotlib and Plotly and create an NLP application with hugging face transformers. 

Something we really like is the 15 hours of course material on applied probability and statistics for data science, as these are essential skills to pursue a career in data science. 

These use a code-oriented approach with Python and NumPy to teach stats and probability fundamentals like random values, spread, central tendency, one-hot encoding, Bayesian inference, regression, and more.

To cap things off, there’s a 5-hour course on Deep Learning with Keras, a high-level neural network library that runs on TensorFlow. Expect to learn about artificial neurons, activation functions, optimization and loss functions, classification, and more.

• Suitable for beginners with no experience in programming or data science
• Covers the two dominant languages for data science, Python and R
• Note-taking areas under each video to keep track of challenging areas
• Includes course material on statistics and probability concepts
• Lack of test exercises compared to others in our list

12. [LinkedIn Learning] Data Science Foundations

This data science training begins with an overview of what data science is, before exploring the place of data science in artificial intelligence, machine learning, and deep learning.  Our research also discovered that the instructor is also the founder of DataLab.

Some of the topics you will learn in this course include Bayes theorem, unsupervised learning, supervised learning, mathematics for data science, and interpretability methods. You’ll also get an overview of generative methods in data science like generative adversarial networks(GANs) and reinforcement learning. 

You will not only delve into the technical aspects of data science, but this course will also teach you about the ethical and responsible use of data. You will explore concepts like bias, explainable AI, security, and legal considerations in data science.

At the end of each chapter, there is a quiz to help you gauge your level of understanding of the lesson presented in that chapter. This course is suitable for beginners as the instructor makes no assumption of prior knowledge in data science.

• Concise videos with clear explanations
• Includes quizzes for each chapter
• Lack of community

13. [Simplilearn] IBM Data Scientist Program 

Our research revealed that this IBM-partnered program offers a comprehensive learning package that includes live online classes, hackathons, webinars, and AMA sessions. 

We like that the live classes give you direct access to instructors, some of which are senior data scientists and engineers from IBM.  This also gives you a unique opportunity to interact with other students.

The course is designed to help you master job-critical skills like supervised and unsupervised learning, hypothesis testing, data mining, clustering, linear and logistic regression, data wrangling, data visualization, and more. 

Some exciting projects you will build for your portfolio are a model to predict diabetic patients, a sales performance module, and a user-based recommendation model among others.

By the end of the course, you will be familiar with programming languages like Python, R, and Scala and also have a working knowledge of data science tools like Apache, Tableau, Spark, HBase, Sqoop, Hadoop, and Flume.

• Live access to instructors
• Participate in IBM hackathons
• Learn Python, R, and Scala
• The cohort-based schedule may not be flexible for some

14. [Simplilearn] Data Science Full Course 2024

In this course, you will learn what data science is, what data scientists do, and a step-by-step guide on how to become a data scientist. 

The instructors also provide in-depth explanations of various terms like artificial intelligence, machine learning, and deep learning and the differences between them. 

Our findings also show that this course covers essential topics like model building, distribution in statistics, Bayes theorem, machine learning algorithms, deep learning neural networks, and binomial distribution. 

You will also get an overview of useful libraries like TensorFlow, Numpy, Scipy, Pandas, and Matplotlib.

This course is a great option for beginners and those who want to polish their data science knowledge for an upcoming interview. There is even a section dedicated to helping you prepare for common data science interview questions along with a tutorial on creating a resume.

• Includes a section on data science interview prep
• An in-depth explanation of key terms
• Free and easily accessible on YouTube
• No certificate of completion

15. [Springboard] Data Science Prep Course 

Our findings revealed that this course is a preparatory data science course for aspiring data scientists. No prior knowledge of data science or programming is required, and taking this course will equip you with the knowledge and skills you need to tackle more advanced data science courses. 

It’s made up of eight parts that will walk you through fundamental data science concepts, including programming and its importance in data science, Bayes theorem, and conditional probability. 

By the end of the course, you will also have a working knowledge of data science tools like Numpy, Pandas, Anaconda, Jupyter Notebooks, Git, and GitHub.

As part of the course, students will also work on an app project to solve a real business problem using data from Google and Apple. 

Another advantage of taking this course is the one-on-one mentorship with mentors from renowned tech companies like Uber. You will also have access to the data science career coaching program and a vibrant peer community.

• Build an app to solve real business problems
• Access to a vibrant peer community
• One-on-one mentor support

16. [Edureka!] Data Science for Beginners 

This will be a great choice for beginners looking for a free course to get started with their data science journey. After explaining data science, the instructor provides a comprehensive roadmap for aspiring data scientists.

This training features over 11 hours of content, covering important topics such as the confusion matrix, Bayes theorem, the Bellman equation, and inferential statistics. 

You will also learn advanced ML and DL topics like regression, KNN algorithms, decision tree algorithms, reinforcement learning, and TensorFlow code basics. 

Our research also revealed that the curriculum includes many use-case sections, which give you the opportunity to put concepts into practice. You also get interview prep and help with creating a data science resume. 

• Suitable for complete beginners
• Includes a comprehensive data scientist roadmap

17. [Codecademy] Data Science Foundations

Codecademy’s data science foundations course begins by teaching you the principles of data literacy before moving on to topics like the fundamentals of statistics for data science and communicating data science findings. 

Based on our observations, this course also teaches you about exploratory data analysis (EDA) techniques and data wrangling, culminating with lessons on popular Python tools like Pandas and Matplotlib.

We like that the curriculum takes a project-based approach, as you’ll be building 34 mini-projects to help you practice the theory you’re learning. 

You’ll also work on two major projects to include in your portfolio, including a project to sort and analyze U.S. medical insurance costs and another to interpret data about endangered animals. These are great ways to get to grips with real-world data problems.

• Build projects for your data science portfolio
• Learn best practices for communicating data science findings
• Includes quizzes at the end of each unit
• No access to instructors to ask questions
• How to Choose the Best Data Science Course in 2024

When choosing the best courses for data science, you’ll want to find one that matches your personal learning goals while blending data science theory with practical skills. 

When reviewing the best course to learn data science, we considered the following criteria and recommend you do the same:

• Accreditation and Reputation: We emphasized online courses for data science from reputable institutions and online learning platforms .
• Curriculum and Topics Covered: We evaluated course curriculums to ensure they covered essential Deep Learning concepts.
• Practical Exercises and Projects: We looked for courses that included hands-on experience, whether via practical exercises or projects.
• Instructor Expertise: We looked for course instructors with relevant practical knowledge and industry experience.
• Student Reviews and Testimonials: We analyzed reviews and testimonials from previous students to gauge the overall learning experience.
• Do I Need To Know AI To Become a Data Scientist?

One thing is for sure: we're all slowly becoming acquainted with the various forms of AI in our professional and personal lives.

But do you need to be an AI wizard to excel in data science in 2024? Interesting question: let's dive deeper.

In the constantly evolving landscape of data science, the integration of artificial intelligence has become increasingly significant, and there is no doubt about that.

And while data science and AI are distinct fields, their intersection is becoming hard to ignore.

As you probably already know, data scientists are tasked with the responsibility of extracting actionable insights from data.

This involves not just the manipulation of structured and unstructured data but also the application of advanced analytical techniques. AI, particularly in the form of machine learning, plays a pivotal role in this process.

This enables us as data scientists to create predictive models that can learn from data, thereby enhancing the accuracy and effectiveness of their analyses.

Understanding AI principles, especially machine learning algorithms, can also immensely benefit us as data scientists.

This knowledge allows us to automate complex data processes, optimize predictive models, and implement solutions that are both innovative and efficient.

And while not every data science role will demand deep expertise in AI, a foundational understanding is becoming increasingly important in the field.

If you're curious to learn more about the transformative power of generative AI across various industries, I'd highly recommend attending DataCamp's free digital conference , RADAR: AI Edition in June 2024.

This event is a fantastic opportunity to discover how businesses and individuals can unlock their full potential with AI. You'll hear from industry leaders like Megan Finck, the Global Head of Talent Acquisition (Data & AI) at Boeing, and Sadie St Lawrence, the CEO of Women in Data. Nnamdi Iregbulem, Partner at Lightspeed Venture Partners, and Eric Seigel, Founder of Machine Learning Week and bestselling author, will also share their insights.

Additionally, Carolann Diskin, Senior Technical Program Manager at Dropbox, and Julien Simon, Chief Evangelist at Hugging Face, will discuss the future of AI and its applications. Don't miss this chance to learn from experts about the latest advancements and strategies in AI.

• Wrapping Up

And there you are, the 17 best data science courses in 2024, including a range of data science courses for beginners and experienced pros alike. Whether you’re just starting out in your data science career or want to level up your existing skills, we’ve included a range of data science courses to help you achieve your goals.

Happy learning!

Want to enhance your data science skills with Deep Learning? Check out:

Coursera’s Deep Learning Specialization with DeepLearning.AI

• Frequently Asked Questions

1. What Course is Best for Data Science?

The best data science courses online depend on various factors like your goals, skill level, and preferred style of learning. We’d recommend reviewing each of the data science courses on our list, but if you’re a beginner, perhaps start with Udemy’s data science training course,  and if you’re more experienced,  Udacity's data science nanodegree is a solid option.

2. What Course is Best for Data Science Beginners? 

It’s not possible to select a single course from the best data science courses for beginners, as this depends on your previous experience, preferred learning style, and career goals. That said, we’ve included a range of excellent beginner courses, including excellent options like Coursera’s data science fundamentals .

3. Does Data Science Require Coding?

Yes. Data science requires you to have a grasp of programming languages like Python and R to manipulate and analyze datasets, build models, and create machine learning algorithms.

People are also reading:

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Analysing Learning Styles for Engineering Capstone Projects: 4MAT Analysis on Student Outcomes and Perceptions

Capstone projects are integrated into engineering curricula to combine various subjects and impart essential professional skills that may be difficult to teach solely through traditional lecture-based courses. These projects play a crucial role in preparing students for their future roles as professional engineers, thereby significantly impacting a university’s industry reputation and ranking. The challenge in engineering education lies in aligning the teaching approach of educators with the diverse learning styles of their students. This study aims to examine the impact of the learning style of the students measured by their watching-doing scores using the 4MAT tool, on the attainment of the benefits of the graduation project (GP). The Bayesian Belief Networks (BBN) approach was adopted in this study to analyse the data collected from 271 students enrolled in both GP1 and GP2 semesters in the engineering department of United Arab Emirates University. Results show that regardless of learning style, both watching and doing category students share similar perspectives on various aspects of the GP course, such as the optimal team performance ratio. However, when assessing the overall effectiveness of the GP programme, doing students exhibit a higher level of agreement than watching students. The study provides valuable insights to faculty members, helping them navigate the optimal balance between providing mentorship and fostering students’ independence during the different stages of their final-year design capstone projects. These findings underscore the importance of tailored educational strategies to accommodate diverse learning styles, contributing to more effective engineering education and better-prepared graduates. 

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Alaskar, H. (2023). The role of online learning in enhancing the performance of introverted female Saudi students in translation. Saudi Journal of Language Studies. https://doi.org/10.1108/sjls-12-2022-0092

Dick, W., Carey, L., & Carey, J. O. (2005). The systematic design of instruction. Harper Collins.

Elwell, G. R., Dickinson, T. E., & Dillon, M. D. (2021). A postgraduate capstone project: Impact on student learning and organizational change. Industry and Higher Education, 36(3), 334–343. https://doi.org/10.1177/09504222211036584

Felder, R. M., & Spurlin, J. (2005). Applications, reliability and validity of the index of learning styles. International Journal of Engineering Education, 21(1), 103–112.

Halim, M. H. A., Buniyamin, N., Imazawa, A., Naoe, N., & Ito, M. (2014). The role of final year project and capstone project in undergraduate engineering education in Malaysia and Japan. 2014 IEEE 6th Conference on Engineering Education (ICEED), 1–6. https://doi.org/10.1109/ICEED.2014.7194678

He, C., Liu, W., & Ren, J. (2022). Bayesian network structure learning: A review. 2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT), 1–7. https://doi.org/10.1109/ACAIT56212.2022.10137915

Howe, S., & Goldberg, J. (2019). Engineering capstone design education: Current Practices, emerging trends, and successful strategies. In D. Schaefer, G. Coates, & C. Eckert (Eds.), Design education today: Technical contexts, programs and best practices (pp. 115–148). Springer International Publishing. https://doi.org/10.1007/978-3-030-17134-6_6

Irfan, O. M., Almufadi, F. A., & Brisha, A. M. (2016). Effect of using 4MAT method on academic achievement and attitudes toward engineering economy for undergraduate students. International Journal of Vocational and Technical Education, 8(1), 1–11.

Kapadia, R. J. (2008). Teaching and learning styles in engineering education. 2008 38th Annual Frontiers in Education Conference, T4B-1-T4B-4. https://doi.org/10.1109/FIE.2008.4720326

Keshavarz, M. H., & Hulus, A. (2019). The effect of students’ personality and learning styles on their motivation for using blended learning. Advances in Language and Literary Studies, 10(6), 78–88. https://doi.org/10.7575/aiac.alls.v.10n.6p.78

Laurila-Pant, M., Mäntyniemi, S., Venesjärvi, R., & Lehikoinen, A. (2019). Incorporating stakeholders’ values into environmental decision support: A Bayesian Belief Network approach. Science of the Total Environment, 697, 134026. https://doi.org/10.1016/j.scitotenv.2019.134026

Naveen, H. M. (2021). Enhancement of accountability in learning among engineering graduates through 4MAT model of instruction. International Journal of Scientific Research in Science and Technology, 8(5), 116–124.

Nicoll-Senft, J. M., & Seider, S. N. (2009). Assessing the Impact of the 4MAT Teaching Model Across Multiple Disciplines in Higher Education. College Teaching, 58(1), 19–27. https://doi.org/10.1080/87567550903245623

Obaya-Valdivia, A. E., Parrales-Vargas, D., & Osorio, C. M. (2023). Different learning styles and the 4 Mat in Science. International Journal of Education (IJE), 11(3). https://doi.org/10.5121/ije.2023.11304

Panezai, R. A., & Mahmood, A. (2022). Effect of 4MAT Cycle on the academic achievements of the students in the subject of biology at secondary level in Baluchistan. International Research Journal of Management and Social Sciences, 3(1), 142–148. https://doi.org/10.53575/irjmss.v3.1(22)15.142-148

Parul, Sarin, J., Sheoran, P., & Phanden, R. K. (2021). Alliance in teaching-learning strategies and learning styles. In N. Kumar, S. Tibor, R. Sindhwani, J. Lee, & P. Srivastava (Eds.), Advances in interdisciplinary engineering (pp. 67–76). Springer Singapore. https://doi.org/10.1007/978-981-15-9956-9_7

Pascasio, P. P., Miraran, L. A. M., Blin, A. S., Cabachete Jr, H. P., Ferreras, J. D. D., & Jimenez, P. L. D. (2020). The unsung prodigy: Navigating through the introspective world of introverts. International Journal of Research Publications, 44(1), 26.

Qattawi, A., Alafaghani, A., Ablat, M. A., & Jaman, M. S. (2019). A multidisciplinary engineering capstone design course: A case study for design-based approach. International Journal of Mechanical Engineering Education, 49(3), 223–241. https://doi.org/10.1177/0306419019882622

Rofi’i, A. (2017). A comparative analysis on extrovert and introvert students towards their speaking skill. ETERNAL (English Teaching Journal), 8(2). https://doi.org/10.26877/eternal.v8i2.3046

Scott, E., Rodríguez, G., Soria, Á., & Campo, M. (2014). Are learning styles useful indicators to discover how students use Scrum for the first time? Computers in Human Behavior, 36, 56–64. https://doi.org/10.1016/j.chb.2014.03.027

Sullivan, D., Colburn, M., & Fox, D. E. (2013). The influence of learning styles on student perception and satisfaction in a highly collaborative team taught course. American Journal of Business Education (AJBE), 6(4), 429–438. https://doi.org/10.19030/ajbe.v6i4.7942

Tezcan, G., & Güvenç, H. (2017). The effects of 4MAT teaching model and whole brain model on academic achievement in science. Education & Science/Egitim ve Bilim, 42(192). https://doi.org/10.15390/eb.2017.7085

Tuysuzoglu, G., Moarref, N., Cataltepe, Z., Misirli, A. T., & Yaslan, Y. (2015). Analysing graduation project rubrics using machine learning techniques. 2015 10th International Conference on Computer Science & Education (ICCSE), 19–24. https://doi.org/10.1109/iccse.2015.7250211

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Purdue’s online data science master’s addresses burgeoning demand for trained data scientists

The interdisciplinary degree is accessible for working professionals from both technical and nontechnical backgrounds

WEST LAFAYETTE, Ind. — Data scientists who can make sense of today’s epic floods of data to generate actionable insights and communicate them to a variety of audiences are in demand in almost any field, from retail business and industry to health care, government, education, and more.

The U.S. Bureau of Labor Statistics estimates that jobs for data scientists will grow 36% by 2031. Nationally, there were nearly 125,000 data scientist jobs added from 2013-2023. Yet many of those jobs — with many more openings coming — went unfilled for a lack of trained data scientists. The bottom line: Nearly every industry today requires data scientists, and the number of these positions is expected to grow.

Purdue University’s new 100% online Master of Science in data science degree addresses the need and the high demand for a trained data science workforce that can harness the power of data to drive innovation, efficiency and competitiveness. The interdisciplinary master’s program is designed for working professionals with a technical background but includes a pathway to entry for professionals from nontechnical fields.

“This data science master’s program is specifically designed for online delivery and optimal online learning, making it accessible to professionals around the world,” said Dimitrios Peroulis, Purdue senior vice president for partnerships and online. “The interdisciplinary curriculum is diverse, customizable to a student’s needs and tailored for practical application immediately.”

Purdue’s online master’s in data science features core courses covering foundations of data science, machine learning and data mining, big data technologies and tools, data analysis, and data visualization and communication.

Students do a capstone project pairing them with an industry mentor and a collaborative team to manage a data science project from inception to completion. That includes developing project timelines, allocating resources and adapting strategies based on the project’s evolution. The capstone, modeled after curriculum from The Data Mine , Purdue’s award-winning data science learning community, is an opportunity to apply knowledge acquired throughout the master’s program to solve complex, real-world problems.

The online master’s program also features the opportunity to earn industry-aligned certificates along the way to earning a master’s degree. Options include education, leadership, and policy; smart mobility and smart transportation; data science in finance; spatial data science; geospatial information science; managing information technology projects; IT business analysis; and applied statistics.

The program was developed by an interdisciplinary cohort of expert faculty from Purdue’s flagship campus, including the colleges of Agriculture, Education, Engineering, Health and Human Sciences, Liberal Arts, Pharmacy, Science, and Veterinary Medicine, along with the Mitch Daniels School of Business, the Purdue Polytechnic Institute, the Purdue Libraries, and the Office of the Vice Provost for Graduate Students and Postdoctoral Scholars.

“Purdue’s new online MS in data science program leverages the real-world experience of faculty working across several distinct disciplines,” said Timothy Keaton, assistant professor of practice in Purdue’s Department of Statistics, who was involved in developing the new degree. “This cooperation between experts in the application of data science in diverse fields provides a great opportunity to create engaging and meaningful coursework that incorporates many different potential areas of interest for our students.”

Students will develop expertise in programming languages, gaining the ability to design and implement data-driven solutions; learn to apply advanced technologies, including cloud computing and big data frameworks, to effectively handle and process large-scale datasets; gain a deep understanding of machine learning algorithms and models, applying them to real-world scenarios; and become proficient in collecting, cleaning, and analyzing diverse datasets.

The curriculum also is designed to teach learners data visualization and communication methods for creating compelling visual representations of complex data to effectively convey insights, along with the application of storytelling techniques to communicate findings clearly to both technical and nontechnical audiences. The program covers adherence to ethical standards in data science, privacy, transparency and fairness as well.

The program draws on Purdue’s expertise in myriad aspects of data science. Known for its emphasis on practical programs with proven value, Purdue has been rated among the Top 10 Most Innovative Schools for six years running by U.S. News & World Report and is the No. 8 public university in the U.S. according to the latest QS World University Rankings.

“The breadth and depth of topics that data science encompasses necessitate graduate programs that incorporate expertise from a variety of disciplines and then integrate this into a curriculum to meet the needs of its students,” said John Springer, a Purdue computer and information technology professor who was involved in developing the new degree. “Purdue’s unique approach to the development and delivery of its new online master’s program wholly fulfills these requirements by utilizing a highly interdisciplinary team of Purdue faculty backed by Purdue’s outstanding team of instructional designers.”

For more information about Purdue’s 100% online Master of Science in data science degree, visit the program website .

About Purdue University

Purdue University is a public research institution demonstrating excellence at scale. Ranked among top 10 public universities and with two colleges in the top four in the United States, Purdue discovers and disseminates knowledge with a quality and at a scale second to none. More than 105,000 students study at Purdue across modalities and locations, including nearly 50,000 in person on the West Lafayette campus. Committed to affordability and accessibility, Purdue’s main campus has frozen tuition 13 years in a row. See how Purdue never stops in the persistent pursuit of the next giant leap — including its first comprehensive urban campus in Indianapolis, the Mitch Daniels School of Business, Purdue Computes and the One Health initiative — at https://www.purdue.edu/president/strategic-initiatives .

Media contact: Brian Huchel, [email protected]

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Udacity Machine Learning Engineer Capstone Project. The project is my participation to the Kaggle competition - Jigsaw unintended bias in toxicity classification. Field: Natural Language Processing

gromag/MachineLearning-Engineer-Specialisation-Capstone-Project-Udacity

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Unintended bias in toxicity classification, udacity machinelearning engineer capstone project.

Project from the Kaggle competition: Jigsaw unintended bias in toxicity classification

Project Overview

Natural Language Processing is a complex field which is hypothesised to be part of AI-complete set of problems, implying that the difficulty of these computational problems is equivalent to that of solving the central artificial intelligence problem making computers as intelligent as people. With over 90% of data ever generated being produced in the last 2 years and with a great proportion being human generated unstructured text there is an ever increasing need to advance the field of Natural Language Processing.

Recent UK Government proposal to have measures to regulate social media companies over harmful content, including "substantial" fines and the ability to block services that do not stick to the rules is an example of the regulamentary need to better manage the content that is being generated by users.

Other initiatives like ​Riot Games​' work aimed to predict and reform toxic player behaviour during games is another example of this effort to understand the content being generated by users and moderate toxic content.

However, as highlighted by the Kaggle competition ​Jigsaw unintended bias in toxicity classification​, existing models suffer from unintended bias where models might predict high likelihood of toxicity for content containing certain words (e.g. "gay") even when those comments were not actually toxic (such as "I am a gay woman"), leaving machine only classification models still sub-standard.

Having tools that are able to flag up toxic content without suffering from unintended bias is of paramount importance to preserve Internet's fairness and freedom of speech.

Project Report

Download the Project-Report.pdf

Acquiring the data

Download the data from https://www.kaggle.com/c/12500/download-all , unzip and place it in /input folder.

Python package requirements

Python entry file.

• Jupyter Notebook 86.0%
• Python 13.8%

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Generative AI Career Roadmap After 12th | Eligibility, Course, Fees and Syllabus for 2024

Explore the career roadmap for generative ai after 12th grade, including eligibility, course options, fees, and syllabus details for 2024. learn about the pathways to becoming a generative ai professional, including bachelor’s degrees, diplomas, and certification courses, along with essential skills and topics covered in ai education..

What is Generative AI?

How generative ai works, applications of generative ai.

• Content Creation: Generating realistic images, videos, music, and art.
• Natural Language Processing (NLP): Creating human-like text for chatbots, virtual assistants, and content generation.
• Healthcare: Generating synthetic medical data for research, creating personalized treatment plans, or even generating drug molecules.
• Gaming and Virtual Reality: Creating realistic characters, environments, and storylines in video games and VR simulations.
• Fashion and Design: Generating new designs for clothing, accessories, and home decor.

Why Generative AI is Important

Eligibility, course options.

• B.Tech/B.E. in Computer Science with AI specialization: Offers a comprehensive education in AI and computer science fundamentals, including generative models.
• B.Sc. in Data Science and AI: Focuses on data science, machine learning, and AI concepts, with opportunities to specialize in Generative AI.
• Diploma in Artificial Intelligence: Short-term courses that provide foundational knowledge in AI, machine learning, and neural networks.
• Online Certifications: Platforms like Coursera, Udacity, and edX offer specific courses in Generative AI, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).
• Integrated M.Tech or M.Sc. in AI: Combines undergraduate and postgraduate studies, allowing students to delve deeper into AI and machine learning.

Course Fees

• Bachelor’s Degree: The average fee ranges from INR 1,00,000 to INR 5,00,000 per year, depending on the institution.
• Diploma and Certifications: Fees for diploma courses can range from INR 50,000 to INR 1,50,000, while online certifications can cost between INR 10,000 to INR 50,000.
• Integrated Programs: These courses may cost between INR 2,00,000 to INR 6,00,000 per year.

Syllabus for Generative AI Courses

• Introduction to AI and ML
• Basic Algorithms and Data Structures
• Linear Algebra, Calculus, and Probability
• Python Programming
• Libraries: TensorFlow, PyTorch, Keras
• Data Handling and Preprocessing
• Neural Networks Basics
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Introduction to Generative Models
• Generative Adversarial Networks (GANs)
• Variational Autoencoders (VAEs)
• Transformer Models for Text and Image Generation
• Image Synthesis and Editing
• Text Generation and Language Models
• AI Art and Creativity Tools
• Hands-on Projects in Generative AI
• Internships with AI Labs or Companies

Expected Salary for various roles in Generative AI 

• LPA stands for "Lakhs Per Annum," which is a unit of salary in India.
• Salaries vary based on factors such as location, company size, and industry demand.
• The salary ranges are approximate and can differ significantly with additional skills, certifications, and industry-specific expertise.

Career Growth in Generative AI

Key Points in Career Growth

• Generative AI career after 12th
• AI eligibility after 12th
• Generative AI courses
• AI course fees
• AI syllabus 2024
• career in Generative AI
• AI roadmap after 12th
• Generative AI education
• AI diploma after 12th
• AI certification courses
• Generative Adversarial Networks
• AI career guide 2024
• AI integrated programs
• Generative AI job opportunities

Ashwini Ghugarkar

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AWS Machine Learning Engineer Nanodegree

Nanodegree Program

The goal of the AWS Machine Learning Engineer (MLE) Nanodegree program is to equip software developers/data scientists with the data science and machine learning skills required to build and deploy machine learning models in production using Amazon SageMaker. This program will focus on the latest best practices and capabilities that are enabled by Amazon SageMaker, including new model design/deployment features and case studies in which they can be applied to.

Built in collaboration with

Intermediate

Real-world Projects

Completion Certificate

Last Updated July 29, 2024

Skills you'll learn:

Prerequisites:

Courses In This Program

Course 1 • 1 day

Welcome to AWS Machine Learning Engineer Nanodegree

An introduction to your nanodegree program.

Welcome! We're so glad you're here. Join us in learning a bit more about what to expect and ways to succeed.

Getting Help

You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

Course 2 • 4 weeks

Introduction to Machine Learning

In this course, you'll start learning what machine learning is by being introduced to the high level concepts through AWS SageMaker. You'll begin by using SageMaker Studio to perform exploratory data analysis. Know how and when to apply the basic concepts of machine learning to real world scenarios. Create machine learning workflows, starting with data cleaning and feature engineering, to evaluation and hyperparameter tuning. Finally, you'll build new ML workflows with highly sophisticated models such as XGBoost and AutoGluon.

Overview of key background around Machine Learning and preparing you to be successful in the rest of this course.

Exploratory Data Analysis

Use AWS SageMaker Studio to access S3 datasets and perform data analysis, feature engineering with Data Wrangler and Pandas. And finally label new data using SageMaker Ground Truth.

Machine Learning Concepts

In this lesson you'll learn about ML Lifecycles, how to differentiate between supervised vs. unsupervised ML, regression methods, and classification methods.

Model Deployment Workflow

In this lesson you'll load a dataset, clean/create features, train a regression/classification model with scikit learn, evaluate a model and tune a model's hyperparameter.

Algorithms and Tools

In this lesson you'll train, test, and optimize on liner, tree-based, XGBoost, and AutoGluon Tabular models. And you will also create a model using SageMaker Jumpstart

Lesson 6 • Project

Predict Bike Sharing Demand with AutoGluon

Train a model using AutoGluon to predict bike sharing demand, and see how highly you can place in the competition!

Course 3 • 3 weeks

Developing your First ML Workflow

This course discusses how to use AWS services to train a model, deploy a model, and how to use AWS Lambda Functions, Step Functions to compose your model and services into an event-driven application.

Introduction to Developing ML Workflows

This lesson gives an introduction to the course, including prerequisites, final project, stakeholders, and tools & environment.

SageMaker Essentials

This lesson will go over SageMaker essential services such as training jobs, endpoints, batch transforms, and processing jobs.

Designing Your First Workflow

This lesson will discuss machine learning workflows and AWS tools such as Lambda, Step Function for building a workflow.

Monitoring a ML Workflow

This lesson will go over monitoring a machine learning workflow and some useful services within AWS to help you monitoring the healthy of data and machine learning models.

Lesson 5 • Project

Project: Build a ML Workflow For Scones Unlimited On Amazon SageMaker

In the project, you will build and ship an image classification model with AWS SageMaker for Scones Unlimited, a scone-delivery-focused logistic company.

Course 4 • 3 weeks

Deep Learning Topics with Computer Vision and NLP

In this course you will learn how to train, finetune and deploy deep learning models using Amazon SageMaker. You’ll begin by learning what deep learning is, where it is used, and the tools used by deep learning engineers. Next we will learn about artificial neurons and neural networks and how to train them. After that we will learn about advanced neural network architectures like Convolutional Neural Networks and BERT as well as how to finetune them for specific tasks. Finally, you will learn about Amazon SageMaker and you will take everything you learned and do them in SageMaker Studio.

Introduction to Deep Learning Topics within Computer Vision and NLP

In this lesson, we will give a background around Deep Learning for Computer Vision and NLP and preparing you to be successful in the rest of this course.

Introduction to Deep Learning

In this lesson, you will learn about neural networks, cost functions, optimization, and how to train a neural network.

Common Model Architecture Types and Fine-Tuning

In this lesson you will learn about Model Architectures, Convolutions, and Fine-tuning.

Deploy Deep Learning Models on SageMaker

In this lesson, you will learn how to apply all you have learned about deep learning in AWS SageMaker.

Image Classification using AWS SageMaker

In this project, you will use AWS SageMaker to finetune a pretrained model and perform a image classification using profiling, debugging, and hyperparameter tuning.

Taught By The Best

Matt Maybeno

Principal Software Engineer

Matt is a Principal Software Engineer at SOCi. With a masters in Bioinformatics from SDSU, he utilizes his cross domain expertise to build solutions in NLP and predictive analytics.

Bradford Tuckfield

Data Scientist and Writer

Bradford Tuckfield is a data scientist and writer. He has worked on applications of data science in a variety of industries. He's the author of Dive Into Algorithms, forthcoming with No Starch Press.

Soham Chatterjee

GRADUATE STUDENT AT THE NANYANG TECHNOLOGICAL UNIVERSITY

Soham is an Intel® Software Innovator and a former Deep Learning Researcher at Saama Technologies. He is currently a Masters by Research student at NTU, Singapore. His research is on Edge Computing, IoT and Neuromorphic Hardware.

Charles Landau

Technical Lead, AI/ML - Guidehouse

Charles holds a MPA from George Washington University, where he focused on econometrics and regulatory policy, and holds a BA from Boston University. At Guidehouse, he supports data scientists and developers working on internal and client-facing ML platforms.

Joseph Nicolls

Senior Machine Learning Engineer - Blue Hexagon

Joseph Nicolls is a senior machine learning scientist at Blue Hexagon. With a major in Biomedical Computation from Stanford University, he currently utilizes machine learning to build malware-detecting solutions at Blue Hexagon.

Ratings & Reviews

Average Rating: 4.7 Stars

February 3, 2023

This program is up to date and provide very good projects to practice the learned skills

October 30, 2022

Subhasish S.

October 26, 2022

So far so good, although I was already familiar with the concepts covered so far. The coming concepts are what I'm most excited to learn about.

Guangchu Y.

September 7, 2022

August 27, 2022

very up to date and excellent quality

The Udacity Difference

Combine technology training for employees with industry experts, mentors, and projects, for critical thinking that pushes innovation. Our proven upskilling system goes after success—relentlessly.

Demonstrate proficiency with practical projects

Projects are based on real-world scenarios and challenges, allowing you to apply the skills you learn to practical situations, while giving you real hands-on experience.

Gain proven experience

Retain knowledge longer

Apply new skills immediately

Top-tier services to ensure learner success

Reviewers provide timely and constructive feedback on your project submissions, highlighting areas of improvement and offering practical tips to enhance your work.

Get help from subject matter experts

Learn industry best practices

Gain valuable insights and improve your skills

Unlock access to .css-15mt56z{font-weight:500;color:var(--chakra-colors-blue-500);} AWS Machine Learning Engineer Nanodegree and the rest of our best-in-class catalog

Unlimited access to our top-rated courses

Real-world projects

Personalized project reviews

Program certificates

Proven career outcomes

Full Catalog Access

One subscription opens up this course and our entire catalog of projects and skills.

Month-To-Month

Average time to complete a Nanodegree program

, Intermediate

Your subscription also includes:

About aws machine learning engineer nanodegree.

Our AWS Machine Learning Engineer Nanodegree program, built in collaboration with AWS, is an intermediate-level machine learning engineering course. It's designed to equip you with the skills needed to build and deploy machine learning models using Amazon SageMaker. The program covers neural network basics, deep learning fluency, and essential machine learning framework fundamentals. You'll learn through practical courses, including developing your first ML workflow and exploring deep learning topics with computer vision and NLP. At Udacity, we provide an unparalleled learning experience, combining expert instruction with real-world projects that ensure you can apply your skills immediately. Under the guidance of industry professionals like Matt Maybeno, you'll gain hands-on experience in AWS machine learning, preparing you to excel as an AWS machine learning engineer.