Description

This is an introductory course on machine learning. The course covers a wide range of topics, from handling a dataset to model delivery. Some prior training in Python programming and basic calculus knowledge will help you get the best out of this course.

Machine learning is a branch of AI and computer science that focuses on the use of data to imitate the way humans learn and improve its accuracy. The course is divided into two parts. The first part starts with a brief history of how machine learning started and introduces you to the basics of statistical learning. You will also understand linear regression and classification, which is the logistic regression model. Understand what cross-validation, sampling, and Bootstrap are. Explore how to go beyond linearity; we will specifically look at a couple of interesting examples to improve the linear regression model to see if we can create models that are non-linear. The second part of the course is completely hands-on labs, which start with an example of predicting fuel efficiency in linear regression. We will then look at a lab on logistic regression with a little bit of mathematics behind it. Understand another lab session on random forests and do a review of decision trees as well. Next, we will look at a lab session on Eigenfaces by using Principle Component Analysis (PCA) and wrap up a course with a lab on ROC-AUC (Receiver Operating Characteristic Curve-Area Under Curve). By the end of the course, you would have given yourself the skills and confidence to start programming machine learning algorithms. All resources and code files are placed here: https://github.com/PacktPublishing/Fundamentals-of-Machine-Learning

What You Will Learn

Learn the basics of statistical learning
Understand linear regression, classification, and supervised learning
Understand sampling and Bootstrap in machine learning
Explore model selection and regularization
Understand random forests and decision trees
Explore labs on Multilayer Perceptron (MLP)?and RNN

Audience

This course can be taken by beginners in Python programming, machine learning, and data science. Scientists, data scientists, and data analysts can also opt for this course. The course assumes no prior knowledge. However, some prior training in Python programming and some basic calculus knowledge is helpful for the course.

Approach

Each topic has its designated video. The video walks through the technical component of a model to prepare students with a mathematical background.

Each lab session covers one single topic, which will ensure that the topics covered in the course are well understood.

Key Features

Build customized deep learning models to start your own data science career * Build customized models to use for different data science projects * Learn about the fundamental principles of machine learning

Github Repo

https://github.com/PacktPublishing/Fundamentals-of-Machine-Learning

About the Author

Yiqiao Yin

Yiqiao Yin was a PhD student in statistics at Columbia University. He has a BA in mathematics and an MS in finance from the University of Rochester. He also has a wide range of research interests in representation learning: feature learning, deep learning, computer vision, and NLP. Yiqiao Yin is a senior data scientist at an S&P 500 company LabCorp, developing AI-driven solutions for drug diagnostics and development. He has held professional positions as an enterprise-level data scientist at EURO STOXX 50 company Bayer, a quantitative researcher at AQR working on alternative quantitative strategies to portfolio management and factor-based trading, and equity trader at T3 Trading on Wall Street.

Course Outline

1. Lectures

1. Welcome

This video explains the contents of the course.

2. Introduction

This video explains a brief history of where machine learning started.

3. Basics in Statistical Learning

This video explains some basic notations in statistical learning, such as Xij.

4. Linear Regression

This video explains the foundation of the linear regression model, which is the very first approach to supervised learning.

5. Classification

This video explains one of the most basic forms of classification, which is the logistic regression model.

6. Sampling and Bootstrap

This video explains the two most famous and common procedures-cross-validation and Bootstrap.

7. Model Selection

This video explains model selection and regularization.

8. Going Beyond Linearity

This video explains going beyond linearity; specifically, we will look at a couple of interesting examples to improve the linear regression model to see if we can create models that are non-linear.

9. Tree-Based Methods - Part 1

This part of the video explains decision tree.

10. Tree-Based Methods - Part 2

The second part of the tree-based methods explains random forests.

11. Support Vector Machine (SVM)

This video explains the support vector machine called SVM and its introduction.

12. Deep Learning

This video introduces you to deep learning, artificial neural networks, recurrent neural networks, and more.

13. Unsupervised Learning

This video explains unsupervised learning, Principal Components Analysis (PCA), and clustering.

14. Classification Metrics

This video explains classification metrics and will cover terminologies such as accuracy, specificity, sensitivity, and so on.

2. Labs

1. Linear Regression

This video explains linear regression using an example of predicting fuel efficiency.

2. Logistic Regression

This video explains logistic regression with a little bit of mathematics behind it.

3. Ridge

This video explains a lab session on Ridge regression, which holds a unique position in statistical machine learning.

4. Decision Tree

This video explains a lab session on a decision tree, getting dependencies, and how to create mock data.

5. Random Forests

This video explains a lab session on random forests and a review of decision trees.

6. Support Vector Machine (SVM)

This video explains a lab session on Support Vector Machines or SVM.

7. Multilayer Perceptron (MLP)

This video explains a lab session on neural networks and Multilayer Perceptron (MLP) models.

8. CNN

This video continues the lab from neural networks and will have a look at convolutional neural networks.

9. PCA

This video explains a lab session on Eigenfaces using PCA.

10. ROCAUC

In this final lab, you will learn about ROC-AUC (Receiver Operating Characteristic Curve-Area Under Curve).

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Fundamentals of Machine Learning

By Packt

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