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  • The Best Resources I Used to Teach Myself Machine Learning
    Python is also home to the largest data science ML community so there are tons of tools to help as you learn Learn Python: freeCodeCamp Python Crash Course With that out of the way, the first thing you should do is download “The Machine Learning Podcast” by OCDevel ( overcast fm , iTunes ) into your favorite podcast app Listen to the
  • Machine Learning Crash Course - Google Developers
    Learn how to analyze and transform numerical data to help train ML models more effectively Working with Categorical Data Learn the fundamentals of working with categorical data: how to distinguish categorical data from numerical data; how to represent categorical data numerically using one-hot encoding, feature hashing, and mean encoding; and
  • Machine Learning From Scratch in Python - Full Course [FREE]
    Decision Tree in Python Part 2 2 - ML From Scratch 09 ; Random Forest in Python - ML From Scratch 10 ; PCA (Principal Component Analysis) in Python - ML From Scratch 11 ; K-Means Clustering in Python - ML From Scratch 12 ; AdaBoost in Python - ML From Scratch 13 ; LDA (Linear Discriminant Analysis) In Python - ML From Scratch 14 ; PyTorch PyTorch
  • Start Here with Machine Learning
    Static Analyzers in Python; Step 3: Learn what you can do in Python ecosystem A Guide to Obtaining Time Series Datasets in Python; Data Visualization in Python with matplotlib, Seaborn, and Bokeh; Web Frameworks for Your Python Projects; You can see all Python posts here But don’t miss Python for Machine Learning (my book) Below is a
  • GitHub - steakmeatdev ML-and-DL-from-scratch: Simple Python . . .
    An Artificial Neural Network (ANN) implementation from scratch, inspired by the teachings of Guillaume from the MachineLearnia YouTube channel A comprehensive slide deck (around 250 slides) crafted to explain the basics of machine learning, deep learning, and the gradient descent algorithm, designed independently for a deep learning course
  • Machine learning education - TensorFlow
    Math and stats: ML is a math heavy discipline, so if you plan to modify ML models or build new ones from scratch, familiarity with the underlying math concepts is crucial to the process ML theory: Knowing the basics of ML theory will give you a foundation to build on, and help you troubleshoot when something goes wrong
  • Resources for getting started with Python and machine learning
    Python is one of the most highly recommended programming languages for beginners learning to code Python helped me understand programming concepts clearly and I like to use multiple resources to reinforce the fundamentals Also, Python is a great choice because it powers machine learning libraries such as TensorFlow and Keras





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