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KDnuggets Home » News » 2020 » May » Tutorials, Overviews » I Designed My Own Machine Learning and AI Degree ( 20:n20 )

I Designed My Own Machine Learning and AI Degree


With so many pioneering online resources for open education, check out this organized collection of courses you can follow to become a well-rounded machine learning and AI engineer.



By Angelica Dietzel, Self-taught journey in Data Science.

After noticing my programming courses in college were outdated, I decided to teach myself machine learning and artificial intelligence on the side using online resources. With no experience in tech and no previous degrees, I designed the following degree 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.

 

My Goals

 

  • 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 world’s 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 Logic Behind My Decisions

 

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.

 

Overview of My Choices

 

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 from consuming success stories of self-taught learners to really grasp what’s needed to succeed.

I start with mathematics. I believe math plays an important role as it builds the foundation for ML and AI (and I love math!), so I included a few courses that cover 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 courses that provide a foundation in data science, machine learning, artificial intelligence, and deep learning. I end my degree with advanced courses that dive deeper into ML and AI. I also include extra courses to fill in some gaps.

 

Let’s Begin!

 

I’ve included a link to each course, labeled each with the institution, provided the prices, and give an overview of what you’ll learn. 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.

 

Mathematics Foundation

 

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; the 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

Inferential Statistics by Duke University (Coursera) [$49/month]

  • Covers: hypothesis testing, confidence intervals, and statistical inference methods for numerical and categorical data.

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

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

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

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

 

Computer Science Foundation

 

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. Provides familiarity with 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; teaches how to write fun and useful programs using Python

 

Python Foundation

 

Introduction to Python Programming (Udacity) [Free]

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

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

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

Python 3 Programming Specialization by University of Michigan (Coursera) [$49/month]

  • Covers: variables, conditionals, and loops, keyword parameters, list comprehensions, lambda expressions, and class inheritance, writing programs that query Internet APIs for data and extract useful information from them.

 

Mastering R

 

Mastering Software Development in R 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, proper design of functions, building R packages, building data viz tools

 

Data Science Foundation

 

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

  • Covers: Python and Jupyter notebooks, pandas, NumPy, Matplotlib, Git; how to manipulate and analyze uncurated datasets; basic statistical analysis and machine learning methods; 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: introduces data science through Python; applied plotting, charting and data representation, text mining; pandas, Matplotlib.

 

Machine Learning Foundation

 

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

  • Covers: a broad introduction to machine learning, data mining, statistical pattern recognition, supervised and 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 Foundation

 

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 Courses

 

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

  • Covers: foundations of deep learning; understand how to build neural networks and 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; 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; how 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.

Getting Started with AWS Machine Learning by AWS (Coursera) [Free, $49 w/certificate]

  • Covers: how to build, train and deploy a model using Amazon SageMaker with built-in algorithms and Jupyter Notebook instance, how to build intelligent applications using Amazon AI services like Amazon Comprehend, Amazon Rekognition, Amazon Translate, and others.

 

Extras

 

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

Data Structures and Algorithms Specialization by UCSD (Coursera) [$49/month]

  • Covers: basic algorithmic techniques such as greedy algorithms, binary search, sorting, and dynamic programming, apply graph and string algorithms to solve a real-world challenge, maximum flow, linear programming, approximate algorithms, SAT-solvers, streaming.

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

  • Covers: a 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; structured, unstructured, and streaming data.

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

  • Covers: Apache Hadoop projects developing open source software for reliable, scalable, distributed computing; 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; 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 merge 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

Computational Neuroscience by University of Washington (Coursera) [$49]

  • Covers: basic computational methods for understanding what nervous systems do and how they function; artificial neural networks, reinforcement learning, and biological neuron model, using Matlab, Octave, and Python.

 

There you have it!

 

Thank you to CourseraedX, and Udacity for being total rock stars and pioneering the way for open education. And to Class Central and CourseTalk for providing a great way to find top online courses and for helping guide me to the curriculum choices above.

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

Original. Reposted with permission.

 

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