Introduction to Artificial Intelligence 7 | Supervised and Unsupervised Learning with Python

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Supervised and Unsupervised Learning with Python

By : Joshi

Supervised and Unsupervised Learning with Python

By: Joshi

Overview of this book

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.

Introduction to Artificial Intelligence 7

The Course Overview

Artificial Intelligence and Its Need

Applications and Branches of AI

Defining Intelligence Using Turing Test

Making Machines Think Like Humans

General Problem Solver

Building an Intelligent Agent

Installing Python 3 and Packages

Loading Data

Classification and Regression Using Supervised Learning

Supervised Versus Unsupervised Learning

What is Classification?

Preprocessing Data

Label Encoding

Logistic Regression and Naïve Bayes Classifier

Confusion Matrix

Support Vector Machines

Classifying Income Data

What is Regression?

Building a Single and Multivariable Regressor

Estimating Housing Prices

Predictive Analytics with Ensemble Learning

What is Ensemble Learning?

What Are Decision Trees

What are Random and Extremely Random Forests?

Dealing with Class Imbalance

Finding Optimal Training Parameters

Computing Relative Feature Importance

Predicting Traffic

Detecting Patterns with Unsupervised Learning

Clustering Data with K-Means Algorithm

Estimating the Number of Clusters

Estimating the Quality of Clustering

Building a Classifier

Segmenting the Market

Building Recommender Systems

Creating a Training Pipeline

Extracting the Nearest Neighbors

Building a K-Nearest Neighbors Classifier

Computing similarity scores

Finding Similar Users

Building a Movie Recommendation System

Chapter: 1

Introduction to Artificial Intelligence 7

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In order to build a learning model, we need data that's representative of the world. We will see how to use the packages to interact with data - Import the package containing all the datasets - Load the house prices dataset - Print the data

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