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Mastering Machine Learning Theory and Practice

Beginner

96 Lessons

36h

Certificate of Completion

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Explanations

AI-POWERED

Explanations

This course includes

109 Playgrounds

10 Quizzes

Course Overview

What You'll Learn

Course Content

Apply Your Skills

Course Overview

The machine learning field is rapidly advancing today due to the availability of large datasets and the ability to process big data efficiently. Moreover, several new techniques have produced groundbreaking results for standard machine learning problems. This course provides a detailed description of different machine learning algorithms and techniques, including regression, deep learning, reinforcement learning, Bayes nets, support vector machines (SVMs), and decision trees. The course also offers suffici...Show More

What You'll Learn

An understanding of the foundations of machine learning and scientific modeling

A working knowledge of deep learning models including MLPs, CNNs, and RNNs

Familiarity with traditional machine learning methods including regression, SVMs, and decision trees

An understanding of the proper application of these methods

Hands-on experience with implementing various machine learning models using sklearn and Keras

An understanding of the foundations of machine learning and scientific modeling

Course Content

Getting started

1 Lessons

Introduction to the Course

Introduction

7 Lessons

The Basic Idea of Machine Learning

History of Machine Learning

Mathematical Formulation of the Basic Learning Problem

Nonlinear Regression in High-Dimensions

Recent Advances

No Free Lunch, but Worth the Bite

Quiz

Scientific Programming with Python

6 Lessons

Programming Environment

Basic Language Elements

Code Efficiency and Vectorization

Data Handling

Image Processing and Convolutional Filters

Quiz

Machine Learning with Sklearn

12 Lessons

Overview

Classification Using Multiple Machine Learning Models

Performance Measures and Evaluations

Data Handling

Dimensionality Reduction, Feature Selection, and t-SNE

Decision Trees and Random Forests

Linear Support Vector Machines (SVMs)

Nonlinear Support Vector Machines (SVMs)

Validation of Support Vector Machines (SVMs)

Challenge: Which Classifier Works Well?

Solution: Which Classifier Works Well?

Quiz

Neural Networks and Keras

11 Lessons

Neurons and the Threshold Perceptron

Multilayer Perceptron (MLP) and Keras

Applying MLP to the MNIST dataset

Representational Learning

Introduction to Convolutional Neural Networks (CNNs)

Applications of Convolutional Neural Networks (CNNs)

Object Detection

Challenges in Machine Learning

Challenge: Choose the Best Model

Solution: Choose the Best Model

Quiz

Regression and Optimization

11 Lessons

Linear Regression and Gradient Descent

Error Surface and Challenges for Gradient Descent

Advanced Gradient Optimization (Learning)

Regularization: Ridge Regression and LASSO

Nonlinear Regression

Applying Gradient Descent to Neural Network Models

Stochastic Gradient Descent (SGD)

Artificial Neural Networks

Implementing a Multilayer Perceptron for the XOR Problem

Automatic Differentiation

Quiz

Basic Probability Theory

8 Lessons

Random Numbers and Their Probability (Density) Function

Moments: Mean and Variance

Examples of Probability (Density) Functions

Cumulative Probability Function and the Central Limit Theorem

Density Functions of Multiple Random Variables

How to Combine Prior Knowledge With New Evidence

Markov Chain Monte Carlo

Quiz

Probabilistic Regression and Bayes Nets

7 Lessons

Probabilistic Models

Learning in Probabilistic Models: Maximum Likelihood Estimate

Probabilistic Classification

Maximum A Posteriori (MAP) and Regularization with Priors

Bayes' Nets: Multivariate Causal Modeling

Probabilistic and Stochastic Neural Networks

Quiz

Generative Models

10 Lessons

Modeling Classes

Supervised Generative Models

Naive Bayes

Self-Supervised Generative Models

Generative Neural Networks

Challenge: Sentiment Analysis Using Naive Bayes

Solution: Sentiment Analysis Using Naive Bayes

Challenge: K-Means Clustering

Solution: K-Means Clustering

Quiz

10.

Cyclic Models and Recurrent Neural Networks

9 Lessons

Overview

Sequence Processing

Basic Sequence Processing with MLP and RNN

Gated RNN and NLP

Other Gated Architectures and Attention

Markov Random Field Model and the Hopfield Model

The Boltzmann Machine

The Restricted Boltzmann Machine

Quiz

11.

Reinforcement Learning

8 Lessons

Formalization of the T-Maze Problem

Representation of the T-Maze roblem

Model-Based Reinforcement Learning

Model-Free Reinforcement Learning

Deep Reinforcement Learning

Actors and Actor-Critics

Reinforcement Learning in the Brain

Quiz

12.

Artificial intelligence, the Brain, and Our Society

4 Lessons

Overview

Different Levels of Modeling and the Brain

Machine Learning and Artificial Intelligence

The Impact of Machine Learning Technology on Society

13.

Conclusion

2 Lessons

Wrap Up

Course Author

Oxford University Press

Project

Recognize Emotions from Speech using Librosa

advanced

1 hour

Project

Recognize Emotions from Speech using Librosa

advanced

1 hour

Project

Cats vs Dogs Classification with Convolutional Neural Networks

beginner

1 hour

Project

Cats vs Dogs Classification with Convolutional Neural Networks

Learn to classify images of cats and dogs using a CNN with TensorFlow in this hands-on project.

beginner

1 hour

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