Supervised and Unsupervised Learning with Python

Supervised and Unsupervised Learning with Python

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 08m | 407 MB

An introduction to world of Artificial Intelligence. Hop on the wonderful journey of machine learning and data analysis

Build real-world Artificial Intelligence (AI) applications to intelligently interact with the world around you, explore real-world scenarios, and learn about the various algorithms that can be used to build AI applications. Packed with insightful examples and topics such as predictive analytics and deep learning, this course is a must-have for Python developers.

This course takes a concept-based, explanation-focused approach. Each concept is explained and then the exercise or example is implemented.

What You Will Learn

  • Get to know various classification and regression techniques
  • Understand the concept of clustering and how to use it to automatically segment data
  • See how to build an intelligent recommender system
Table of Contents

Introduction to Artificial Intelligence 7
01 The Course Overview
02 Artificial Intelligence and Its Need
03 Applications and Branches of AI
04 Defining Intelligence Using Turing Test
05 Making Machines Think Like Humans
06 General Problem Solver
07 Building an Intelligent Agent
08 Installing Python 3 and Packages
09 Loading Data

Classification and Regression Using Supervised Learning
10 Supervised Versus Unsupervised Learning
11 What is Classification
12 Preprocessing Data
13 Label Encoding
14 Logistic Regression and Naïve Bayes Classifier
15 Confusion Matrix
16 Support Vector Machines
17 Classifying Income Data
18 What is Regression
19 Building a Single and Multivariable Regressor
20 Estimating Housing Prices

Predictive Analytics with Ensemble Learning
21 What is Ensemble Learning
22 What Are Decision Trees
23 What are Random and Extremely Random Forests
24 Dealing with Class Imbalance
25 Finding Optimal Training Parameters
26 Computing Relative Feature Importance
27 Predicting Traffic

Detecting Patterns with Unsupervised Learning
28 Clustering Data with K-Means Algorithm
29 Estimating the Number of Clusters
30 Estimating the Quality of Clustering
31 Building a Classifier
32 Segmenting the Market

Building Recommender Systems
33 Creating a Training Pipeline
34 Extracting the Nearest Neighbors
35 Building a K-Nearest Neighbors Classifier
36 Computing similarity scores
37 Finding Similar Users
38 Building a Movie Recommendation System