How I Designed My Own Machine Learning and Artificial Intelligence Degree 

After noticing my programming courses in college were outdated, I began this year by dropping out of college to teach myself machine learning and artificial intelligence using online resources. With no experience in tech, no previous degrees, here is the degree I designed in Machine Learning and Artificial Intelligence from beginning to end to get me to my goal — to become a well-rounded machine learning and AI engineer. 

Bring value to the world. I’m not learning these technologies for the sake of learning or because it’s the hot new tech. I’m going to use what I learn to build something incredible. 

Use machine learning and AI to tackle big problems. I know I won’t ever be the worlds leading expert in Machine Learning or AI, but I hope I can make my mark. 

Inspire others to start their own learning journey. By writing about my journey and sharing all that I’ve learned, I hope to encourage others to create their own paths.

The main criteria I used when deciding what to choose were: price, flexibility, project based learning, reviews and ratings. I used thousands of course ratings and reviews from Class Central and CourseTalk along with the respective institution’s ratings and reviews to help me make my decisions. I selected the best computer science, math, data science, AI and machine learning courses I could find based on this. 

The reasoning behind each of the different subjects I chose to study came from analyzing Machine Learning and Data Science Degrees at top colleges all over the world and consuming success stories of self-taught learners to really grasp what is needed to succeed.

Starting with mathematics — I believe this plays an important role as it builds the foundation for ML and AI (and I love math!), so I included a few courses that covers Linear Algebra, Multivariate Calculus, Probability and Statistics. 

The language I chose to base my degree on is Python, but I also included a great course to master R. 

I move on to gaining a foundation in Data Science, Machine Learning, Artificial Intelligence and Deep Learning and end my degree with advanced courses that will dive deeper into ML and AI. I also include extra courses to fill in some gaps. 

I’ve included a link to each course, labeled each course with the institution, included the prices, and an overview of what you’ll learn in each. If you know of any great courses, feel free to recommend any certificates or programs related to ML or AI —  I’d love to check them out and/or include them. Enjoy!

Data Science Math Skills by Duke University (Coursera) [$49]

Covers: set theory, interval notation and algebra with inequalities, graphing functions and their inverses on the x-y plane, concept of instantaneous rate of change and tangent lines to a curve, exponents, logarithms, probability theory, including Bayes’ theorem.

Mathematics for Machine Learning by Imperial College (Coursera) [$49/month]

Covers: linear algebra, multivariate calculus, dimensionality reduction with principal component analysis, eigenvalues and eigenvectors.

Introduction to Mathematical Thinking by Stanford (Coursera) [$49]

Covers: learn how to think the way mathematicians do, number theory, real analysis, mathematical logic. 

Discrete Optimization by The University of Melbourne (Coursera) [$49]

Covers: how to solve complex search problems with discrete optimization concepts and algorithms, constraint programming, branch and bound, linear programming (LP), mixed integer programming.

Introduction to Computer Science by Harvard (edX) [Free, $99 w/ certificate)

Covers: abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development. Familiarity in C, Python, SQL, and JavaScript plus CSS and HTML

Learn to Program: The Fundamentals by University of Toronto (Coursera) [Free, $49 w/certificate]

Covers: fundamental building blocks of programming and teaches you how to write fun and useful programs using Python. 

Introduction to Python Programming (Udacity) [Free]

Covers: fundamentals Python, learn to represent and store data using Python data types and variables, use conditionals and loops, harness the power of complex data structures.

Python For Everybody by University of Michigan (Coursera) [$49/month]

Covers: basics of programming computers using Python, work with HTML, XML, and JSON data formats, introduce the core data structures, basics of SQL, basic database design for storing data.

Mastering Software Development in R Specialization by Johns Hopkins University (Coursera) [$39/month]

Covers: focus on using R in a data science setting, robust error handling, object oriented programming, profiling and benchmarking, debugging, and proper design of functions, building R packages, building data viz tools.

Python for Data Science by UC San Diego (edX) [Free, $350 w/certificate]

Covers: python, jupyter notebooks, pandas, numpy, matplotlib, git, how to manipulate and analyze uncurated datasets, basic statistical analysis and machine learning methods and how to effectively visualize results.

Data Analyst Nanodegree (Udacity) [$359/month for 4 months]

Covers: how to manipulate and prepare data for analysis, creating visualizations for data exploration, how to use your data skills to tell a story with data 

Applied Data Science with Python by University of Michigan (Coursera) [$49/month]

Covers: introduce data science through python, applied plotting, charting & data representation, text mining, Pandas, Matplotlib. 

Machine Learning by Stanford (Coursera) [Free, $79 w/certificate]

Covers: a broad introduction to machine learning, datamining, and statistical pattern recognition: supervised learning, unsupervised learning, best practices, how to apply learning algorithms to building smart robots, text understanding, computer vision, medical informatics, audio, database mining, and other areas. 

AI for Everyone (Non-Technical) by deeplearning.ai (Coursera) [$49]

Covers: the meaning behind common AI terminology: neural networks, ML, deep learning, and DS, what AI realistically can/can’t do, how to spot opportunities to apply AI, what it feels like to build ML and DS projects, how to work with an AI team and build an AI strategy.

TensorFlow in Practice Specialization by deeplearning.ai (Coursera)
[$49/month]

Covers: how to build and train neural networks, improve a network’s performance, teach machines to understand, analyze, and respond to human speech with natural language processing systems, computer vision.

Advanced Machine Learning Specialization by National Research University — Higher School of Economics [Free, $49/month w/certificate]

Covers: introduction to deep learning, reinforcement learning, natural language understanding, computer vision, Bayesian methods, and 
how to win a data science competition from Top Kagglers. 

Deep Learning Specialization by deeplearning.ai and Stanford (Coursera) [$49/month]

Covers: foundations of Deep Learning, understand how to build neural networks, how to lead successful machine learning projects, Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization.

Deep Learning NanoDegree (Udacity) [$324/month for 4 months]

Covers: become an expert in neural networks, learn to implement them using the deep learning framework PyTorch, build convolutional networks for image recognition, recurrent networks for sequence generation, generative adversarial networks for image generation, and learn how to deploy models accessible from a website.

MicroMasters in Artificial Intelligence by Columbia University (edX) [$894.40]

Covers: the guiding principles of AI, hot to apply concepts of machine learning to real life problems and applications, design and harness the power of Neural Networks and broad applications of AI in fields of robotics, vision and physical simulation.

Additional resources will be added to this section as I progress through this curriculum. Suggestions are welcome!

Data Engineering, Big Data, and Machine Learning on GCP Specialization by Google Cloud (Coursera) [$49/month]

Covers: hands-on introduction to designing and building data pipelines on Google Cloud Platform, design data processing systems, build end-to-end data pipelines, analyze data and derive insight, covers structured, unstructured, and streaming data.

Intro to Hadoop and MapReduce by Cloudera (Udacity) [Free]

Covers: Apache Hadoop projects develops open-source software for reliable, scalable, distributed computing. Learn the fundamental principles behind it, and how you can use its power to make sense of your Big Data.

Version Control with Git (Udacity) [Free]

Covers: essentials of using version control system Git, you’ll learn to create a new Git repo, commit changes, review the commit history of an existing repo, how to keep your commits organized using tags and branches and merging changes by crushing merge conflicts. 

Software Debugging with Python (Udacity) [Free]

Covers: how to debug programs systematically, how to automate the debugging process and build several automated debugging tools in Python.

Thank you to Coursera, edX and Udacity for being total rockstars and pioneering the way for an open eductaion. And to Class Central and CourseTalk for providing a great way to find top online courses and helping me guide the above curriculum choices. 

If you have any recommendations, comments or concerns regarding my degree, or would like to chat about your own educational goals, comment below!

I’ll keep this degree updated as I go along with the courses I’m learning and will continue to edit any that have changed and add in any I choose to tackle. 

I’m sharing my journey through Twitter and Medium, follow me if you’d like updates on my progress, thoughts on subjects I’m learning, or reviews of the courses above!