MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 4.62 GB | Duration: 3小时 51分钟

Algorithms and Practical Examples in Python

What you’ll learn
Understand the mathematics behine Machine Learning
Supervised Machine Learning Models such as Decision Tree, Support Vector Machine, k-Nearest Neighbor, Linear Regression etc.
Python Code for Supervised learning models
Creating a ML model and solving for a given set of data.

Requirements
Basic Mathematics, Programming foundations

Description
In this course, we present the concept of machine learning and the classification of different methods of learning such as Supervised and Unsupervised Learning. We also present reinforcement learning. We offer popular techniques and implement them in Python. We begin with the Decision Tree method. We present this simply with all the required mathematical tools such as entropy. We implement them in Python and explain how the accuracy can be improved. We offer the classification problem with a suitable real-life scenario. Linear Regression is taught using simple real-life examples. We present the L2 Error estimation and explain how we can minimize the error using gradient optimization. This is implemented using the Python library. We also offer the Logistic Regression method with an example and implement in Python. The Nearest Neighbourhood approach is explained with examples and implemented in Python. Support Vector Machines (SVM) are a popular supervised learning model that you can use for classification or regression. This approach works well with high-dimensional spaces (many features in the feature vector) and can be used with small data sets effectively. When trained on a data set, the algorithm can easily classify new observations efficiently. We also present a few more methods. The Bayesian model of classification is used for large finite datasets. It is a method of assigning class labels using a direct acyclic graph. The graph comprises one parent node and multiple children nodes. And each child node is assumed to be independent and separate from the parent. As the model for supervised learning in ML helps construct the classifiers in a simple and straightforward way, it works great with very small data sets. This model draws on common data assumptions, such as each attribute is independent. Yet having such simplification, this algorithm can easily be implemented on complex problems.

Overview
Section 1: Introduction

Lecture 1 Learning by Observation

Lecture 2 Learning Agents

Section 2: Forms of Learning

Lecture 3 Forms of Learning – Inductive Learning

Section 3: Inductive Learning Methods

Lecture 4 Supervised Learning

Lecture 5 Unsupervised Learning

Lecture 6 Reinforcement Learning

Section 4: Decision Tree Model

Lecture 7 Introduction to Decision Trees

Lecture 8 Decision Tree Construction Algorithm

Lecture 9 Mathematical Constructs for Decision Tree – Entropy, Remainder and Info gain

Lecture 10 Decision Tree Code using sklearn – Syntax explained

Lecture 11 Decision Tree – Python Lab

Lecture 12 Decision Tree Testing the Model Python Lab

Section 5: Linear Regression

Lecture 13 Linear Regression – Gradient Descent – Concept and Algorithm

Lecture 14 Linear Regression – Gradient Descent – Multivariate

Lecture 15 Writing Python code using Skilearn

Lecture 16 Linear Regression – Python with Skilearn Practical Demonstration

Bachelor and Master Degree students,Machine Learning Programmers