Vimal Venugopal

Vimal Venugopal

a curious mind with a passion for machine learning, photography and travel

(Note to Self - How I would learn Machine Learning) 01:00 1. Math: Khan Academy Recommended Courses: - Multi-Variable Calculus - Differential Equations - Linear Algebra - Statistics and Probability 02:00 2. Python Recommended Courses - FreeCodeCamp: Python in 4-Hours Full Course - FreeCodeCamp: Intermediate Python in 6-Hours 02:37 3. Machine Learning TECH STACK Most important Python libraries for Machine Learning, Data Science, and Data Visualization Optional: Can be picked up later when doing the ML course. Use for every project, which is why he recommends doing them now to build a base. Follow a free crash course for now, pick up more advanced concepts later if needed. - NumPy: Base for everything: Python Engineer - NumPy Crash Course Complete Tutorial - Pandas: Data handling: Keith Gali - Complete Python Pandas Data Science Tutorial - MatPlotLib: Visualization: FreeCodeCamp - MatPlotLib Crash Course ————————— The following MachineLearning courses aren’t yet needed - Tensor Flow - Scikit Learn - PyCharge ??? 03:35 4. Machine Learning Courses - Machine Learning Specialization by Andrew Ng (Coursera) - Implement algorithm from scratch using his ‘ML from SCRATCH’ playlist - ML from Scratch Playlist by Python Engineer (Assembly AI) 04:45 5. Hands - On & Data Preparation Kaggle Courses - Intro to Machine Learning - Intermediate Machine Learning 05:19 6. Practice & Build Portfolio Kaggle: Competitions - They provide lots of datasets, platform to evaluate, and a community. 06:15 7. Specialize & Create Blog - NLP - PyTorch / Tensor Flow - MLOps 06:52 Start a VLOG - Tutorial - Share what you’ve learned - Share the projects you’ve built - Problems faced and how you have solved them - Write about a topic 07:24 Books - Machine Learning with PyTorch and SckiKit-Learn by Raschka - https://github.com/rasbt/machine-learning-book - Hands-On Machine Learning with SciKit-Learn, Keras & TensorFlow by Geron

References

https://www.youtube.com/watch?v=wtolixa9XTg&ab_channel=AssemblyAI

Other Resources:

Source: Geron ML book

• Code examples: https://github.com/ageron/handson-ml2 • https://www.dataquest.io/ website • Joel Grus, Data Science from Scratch (O’Reilly). This book presents the funda‐ mentals of Machine Learning, and implements some of the main algorithms in pure Python (from scratch, as the name suggests). • Stephen Marsland, Machine Learning: An Algorithmic Perspective (Chapman and Hall). This book is a great introduction to Machine Learning, covering a wide range of topics in depth, with code examples in Python (also from scratch, but using NumPy). • Sebastian Raschka, Python Machine Learning (Packt Publishing). Also a great introduction to Machine Learning, this book leverages Python open source libra‐ ries (Pylearn 2 and Theano). • François Chollet, Deep Learning with Python (Manning). A very practical book that covers a large range of topics in a clear and concise way, as you might expect from the author of the excellent Keras library. It favors code examples over math‐ ematical theory. • Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin, Learning from Data (AMLBook). A rather theoretical approach to ML, this book provides deep insights, in particular on the bias/variance tradeoff (see Chapter 4). • Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 3rd Edition (Pearson). This is a great (and huge) book covering an incredible amount of topics, including Machine Learning. It helps put ML into perspective.

Deep Learning:

  • Book: Michael Nielsen - http://neuralnetworksanddeeplearning.com/
    • 1 year skilling up full time in DL specifically, first FastAI and then Full Stack Deep Learning and Deep Learning Systems (DLS: https://dlsyscourse.org/lectures/), and then also implementing papers I found interesting
  • first FastAI: https://course.fast.ai/Resources/book.html
  • Two: Full Stack Deep Learning (https://fullstackdeeplearning.com/course/)
  • Three: Deep Learning Systems (DLS: https://dlsyscourse.org/lectures/)

Reddit Recommendations:

  • “Understanding Deep Learning” by Simon J.D. Prince

Datasets

  • the Oxford-IIIT Pet Dataset that contains 7,349 images of cats and dogs from 37 different breeds https://www.robots.ox.ac.uk/~vgg/data/pets/
  • Famous Imagenet dataset https://image-net.org/

Learney Video List

Complete StatQuest Videos: https://maps.joindeltaacademy.com/maps/StatQuest