Machine Learning A-Z

A thorough introduction to machine learning theory and practice. You will cover supervised, unsupervised, and reinforcement learning algorithms, implement them from scratch in Python, then apply them with Scikit-learn and TensorFlow on real datasets.

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Course Content

4 Modules • 23 Lessons

play_circle Types of Machine Learning Preview

play_circle The ML Workflow Preview

description Linear Algebra and Statistics Review

description Setting Up the Environment

play_circle Perceptrons and Multilayer Networks

play_circle Backpropagation

play_circle Building NNs with TensorFlow/Keras

play_circle CNNs for Image Classification

play_circle RNNs and LSTMs for Sequences

play_circle Transfer Learning

description Deep Learning Capstone

play_circle Linear Regression from Scratch

play_circle Gradient Descent

play_circle Logistic Regression

play_circle Support Vector Machines

play_circle k-NN Algorithm

play_circle Ensemble Methods: Bagging and Boosting

description Supervised Learning Practice

play_circle k-Means Clustering

play_circle Hierarchical Clustering

play_circle PCA — Dimensionality Reduction

play_circle Anomaly Detection

description Unsupervised Practice

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NRS 110

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layers 4 modules • 23 lessons
ondemand_video On-demand video lessons
all_inclusive Full lifetime access
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NRS 110

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