There are several tutorials for both machine learning and deep learning. Each tutorial consists of a video walkthrough and a coding assignment or interactive demo.

This tutorial allows users who are completely unacquainted with programming to learn Python within a week

__Beginner__, Tutorial Link

This tutorial introduces the necessary algebraic background for machine learning. It covers vectors, spans, vector spaces, matrices, and tensors.

__Intermediate__, Tutorial Link

This short tutorial covers the calculus essentials for understanding machine learning. Topics include derivatives, partial derivatives, and gradients.

I__ntermediate__, Tutorial Link

This tutorial covers all of the probability fundamentals which are necessary to understand probabilistic machine learning

__Beginner__, Tutorial Link

This tutorial introduces basic data science and querying techniques that are needed to make sense of machine learning data.

__Intermediate__, Tutorial Link

This tutorial covers Python in-depth and explores techniques of storing data. It also navigates the process of using a notebook

__Advanced__, Tutorial Link

This tutorial uses Scikit-Learn and Python to predict housing prices based on pre-defined features

__Beginner__, Tutorial Link

This tutorial uses Scikit-Learn and Python to classify between benign and malignant tumors

__Intermediate__, Tutorial Link

This tutorial explains gradient descent in an iterative style while also covering the learning rate and hyperparameters

__Intermediate__, Tutorial Link

This tutorial explains the fundamentals of a Support Vector Machine and other kernel methods

__Advanced__, Tutorial Link

This tutorial implements a Naive Bayes classifier and explains probability in a visual manner

__Advanced__, Coming soon

This tutorial uses Scikit-Learn and Python to fit a KNN classifier to a select dataset in a notebook

__Intermediate__, Tutorial Link

This tutorial helps users to get acquainted with basic deep learning concepts and to understand the process of training and tuning a network

__Beginner__, Tutorial Link

This tutorial explains Keras and shows the process of designing a basic network

__Intermediate__, Tutorial Link

This tutorial explores the fundamentals of the Tensorflow library and its benefits

__Intermediate__, Tutorial Link

This tutorial explains Tensorflow groups and automatic differentiation with tensorboard

__Advanced__, Tutorial Link

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

__Advanced__, Tutorial Link

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

__Intermediate__, Tutorial Link

This tutorial explains perceptron, MLP, backpropagation, layers, activation functions, etc. so that you can understand how a neural network works

This tutorial outlines the methods of convolutional neural networks. Pooling, stride, filters, convolution are all explained.

This tutorial explains recommenders systems with a technical focus on Recurrent networks and LSTM.

This tutorial explains how generative adversarial networks function in respect to a discriminator and generator. It also discusses transfer learning, meta-learning, and Wasserstein GANs

This hands-on tutorial is designed so that the user can create a neural network from scratch. It is a way great to verify your understanding of deep learning.

This tutorial is a practical example of machine learning in action. Predict survival rate on the Titanic through simple algorithms and data preparation.

This tutorial outlines the methods of convolutional neural networks. Pooling, stride, filters, convolution are all explained.

This tutorial explores specific reinforcement learning algorithms like MDPs, SARSA, and implements them

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