/Learning-Python

This is a readme that has information about resources for learning Python and Data Science

Learning Data Science in Python

This is a readme that has information about resources for learning Python and Data Science

Introduction to Python

I feel that having at least a basic understanding of the Python language is important before moving into the specific data focused packages like Numpy and Pandas. Below are two courses I found very valuable.

  • Intro to CS with Python
    • This course is provided by MIT and is fairly difficult, but it provides you with a great intro background to Python and basic concepts in Computer Science.
  • Complete Python Bootcamp. I think I can find a coupon code for this one for $11 instead of $19.99 if anyone wants to actually purchase it.
    • This link should include the coupon.

Data Science Related Podcasts

  • Machine Learning Guide
  • SuperDataScience Podcast
  • DataFramed
  • O'Reilly Data Show
  • This Week in Machine Learning & AI
  • Partially Derivative
  • Linear Degressions
  • Data Journeys
  • Data Stories
  • Not So Standard Deviations

Data Science and Machine Learning

I would recommend these next resources for data science and machine learning in the order provided below as the first courses hold your hand a bit more than some of the other resources. Some of my basic thoughts when it comes to learning.

  • I recommend using Jupyter Notebooks. It allows you to iterate through code quickly and fix broken code. When you are learning this is great.
  • One of the things I struggled with early on was getting frustrated when my code wouldn't run. Maybe this is just me, but when you are coding you will almost certainly screw up, lol. You can't always get it right the first time.
  • Google and Stackoverflow are your friend. The great thing about Python (and R) compared to SPSS and/or SAS is that there are tons of resources out there. If you have a question about something it's almost a gaurantee that someone else has had the same question and it has been answered on stackoverflow. Take advantage of that. As you get more familiar with Pandas, Numpy, etc. you will start to understand the lingo a bit better and your searches will yield you the results you are looking for much more quickly. At first for me it was searching something 4-5 different ways to find an answer, now I can usually find the information I am looking for on the first search.
  1. Python for Data Science and Machine Learning Bootcamp
  • This course is a really good introduction to Numpy, Pandas, Matplotlib, Seaborn, and the most popular algorithms used in scikit learn. What I really like about this course is that he builds in 'tests' with real Kaggle data, so it's engaging.
  1. Machine Learning A-Z
  • This course isn't as engaging as Jose's, but it does a really good job of showing you what a data pipeline process looks like. It really helps you understand how similar most of the commands are across algorithms. I.e. initialize the model as a python object, fit the model, then predict.
  1. Python Data Science Handbook
  • Jake made his entire book available for free on his github page and I think it's a great resource. I would highly encourage you to purchase the book as well to support more experts like this helping to teach. After you get the basics down Jake's book does a great job of helping walk you through more complex topics and code.
  1. Python Machine Learning
  • Sebastian's book is great and starts to get into some more advanced topic and code. This is one I personally own and reference very often. I would highly recommend.
  1. Machine Learning Specialization
  • This is a Machine Learning specialization through Coursera taught by professors from the University of Washington. IMO this course does a great job of going deeper into the math behind linear and logistic regression. My only issue with the course was that it used an ML API different than scikit learn that actually costs money (although they provide it to students for free for one year). Early on when I was taking it I decided to do the assignments twice. Once with their API and then again with scikit learn, but I started to speed it up towards the end and didn't do it for the last 2 courses.

Deep Learning

  1. Andrew Ng's Deep Learning Specialization
  • Andrew did a lot over the last decade to make machine learning and deep learning a household name. He is one of the founders of Coursera and his Machine Learning course on Coursera is still probably one of the most popular courses ever on the platform. This specialization does a great job of breaking down the math and walking you through the code step by step for neural networks, CNNs, and RNNs. It's a great overview of everything deep learning has to offer.
  1. Deep Learning A-Z
  2. Deep Learning and NLP A-Z
  3. Complete Guide to Tensorflow for Deep Learning
  • All of these courses will help further familiarize yourself with the concept of deep learning. I actually haven't completed any of these. I am about 70% of the way through Deep Learning A-Z and 50% of the way through the Complete guide to tensorflow, but they are from some great minds in the field and the information I have completed has been a very good overview of the topics.