If you are locked down because of the COVID-19 pandemic, you just might have some extra time on your hands. Binging Netflix is all well and good, but perhaps you are getting tired of that and you would like to learn something new.
One of the most lucrative fields to open up in the last couple of years is data science. The resources I list below will help those technical enough to understand math at the level of statistics and differential calculus to incorporate machine learning into their skill sets. They might even help you start a new career as a data scientist.
If you already can program in Python or R, that skill will give you a leg up on applied data science. On the other hand, the programming isn’t the hard part for most people — it’s the numerical methods.
Coursera offers many of the following courses. You can audit them for free, but if you want credit you need to pay for them.
I recommend starting with the book The Elements of Statistical Learning so that you can learn the math and the concepts before you start writing code.
I should also note that there are several good courses at Udemy, although they are not free. They usually cost about $200 each for lifetime access, but I’ve seen many of them discounted to less than $20 in recent days.
Jeff Prosise of Wintellectnow tells me that he’s planning to make a few more of his courses free, so stay tuned.
The Elements of Statistical Learning, Second Edition
By Trevor Hastie, Robert Tibshirani, and Jerome Friedman, Springer
This free 764-page ebook is one of the most widely recommended books for beginners in data science. It explains the fundamentals of machine learning and how everything works behind the scenes, but contains no code. Should you prefer a version of the book with applications in R, you can buy or rent it through Amazon.
Applied Data Science with Python Specialization
By Christopher Brooks, Kevyn Collins-Thompson, V. G. Vinod Vydiswaran, and Daniel Romero, University of Michigan/Coursera
The five courses (89 hours) in this University of Michigan specialization introduce you to data science through the Python programming language. This specialization is intended for learners who have a basic Python or programming background, and who want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular Python toolkits such as Pandas, Matplotlib, Scikit-learn, NLTK, and NetworkX to gain insight into their data.
Data Science: Foundations using R Specialization
By Jeff Leek, Brian Caffo, and Roger Peng, Johns Hopkins/Coursera
This 68-hour specialization (five courses) covers foundational data science tools and techniques, including getting, cleaning, and exploring data, programming in R, and conducting reproducible research.
By Andrew Ng, Kian Katanforoosh, and Younes Bensouda Mourri, Stanford/deeplearning.ai/Coursera
In 77 hours (five courses) this series teaches the foundations of deep learning, how to build neural networks, and how to lead successful machine learning projects. You will learn about Convolutional networks (CNNs), Recurrent neural networks (RNNs), Long Short Term Memory networks (LSTM), Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. In addition to the theory, you will learn how it is applied in industry using Python and TensorFlow, which they also teach.
Fundamentals of Machine Learning
By Jeff Prosise, Wintellectnow
In this free two-hour introductory video course, Prosise takes you through regression, classification, Support Vector Machines, Principal Component Analysis, and more, using Scikit-learn, the popular Python library for machine learning.
By Andrew Ng, Stanford/Coursera
This 56-hour video course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Topics include supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks), unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning), and best practices in machine learning and AI (bias/variance theory and innovation process). You’ll also learn how to apply learning algorithms to building smart robots, web search, anti-spam, computer vision, medical informatics, audio, database mining, and other areas.
By Carlos Guestrin and Emily Fox , University of Washington/Coursera
This 143-hour (four course) specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.
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