Tutorials for background (Python, Statistics, and Data Processing with Linear Algebra), Machine Learning, and Deep Learning (check out startonai.com for more)

Head and Editor: Siddharth Sharma Content Creators: Anaiy Somalwar, Shrey Gupta, Ayush Karupakula, Andy Phung, Aditya Chakka, Keshav Shah, Navein Suresh, and Aurko Roth

Ordering of Tutorials:

Background

Learn Python (#1) This tutorial allows users who are completely unacquainted with programming to learn Python within a week

Linear Algebra (#2) This tutorial introduces the necessary algebraic background for machine learning. It covers vectors, spans, vector spaces, matrices, and tensors.

Calculus (#3) This short tutorial covers the calculus essentials for understanding machine learning. Topics include derivatives, partial derivatives, and gradients.

Probability (#4) This tutorial covers all of the probability fundamentals which are necessary to understand probabilistic machine learning

Statistics (#5) This tutorial introduces basic data science and querying techniques that are needed to make sense of machine learning data.

Data Structures (#6) This tutorial covers Python in-depth and explores techniques of storing data. It also navigates the process of using a notebook

Machine Learning Tutorials

Build a Housing Prices Predictor (#1) This tutorial uses Scikit-Learn and Python to predict housing prices based on pre-defined features

Breast Cancer Classification (#2) This tutorial uses Scikit-Learn and Python to classify between benign and malignant tumors

Gradient Descent Exercise (#3) This tutorial explains gradient descent in an iterative style while also covering the learning rate and hyperparameters

Building a SVM in Python (#4) This tutorial explains the fundamentals of a Support Vector Machine and other kernel methods

Implementing Bayesian ML (#5) This tutorial implements a Naive Bayes classifier and explains probability in a visual manner

Constructing a K-nearest neighbors (#6) This tutorial uses Scikit-Learn and Python to fit a KNN classifier to a select dataset in a notebook

Deep Learning Tutorials

Tensorflow Playgrounds (#7) This tutorial helps users to get acquainted with basic deep learning concepts and to understand the process of training and tuning a network

Build a network in Keras (#8) This tutorial explains Keras and shows the process of designing a basic network

Tensorflow I - Overview (#9) This tutorial explores the fundamentals of the Tensorflow library and its benefits

Tensorflow II - Graphs (#10) This tutorial explains Tensorflow groups and automatic differentiation with tensorboard

Building a GAN in PyTorch (#11) This tutorial explains what Generative Adversarial Networks (GANs) are and implements a simple example with the PyTorch platform

Reinforcement Learning Tutorial (#12) This tutorial dives into the field of reinforcement learning and explores higher logic ML with the Cartpole problem. Other techniques covered include SARSA, Q-learning, and Monte Carlo Methods