Supervised and Unsupervised Learning with Python [Video]

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Supervised and Unsupervised Learning with Python [Video]

Prateek Joshi

Hop on the wonderful journey of machine learning and data analysis

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Video Details

ISBN 139781788990325
Course Length2 hours 08 minutes

Video Description

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.

Style and Approach

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

Table of Contents

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

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

Authors

Table of Contents

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

Video Details

ISBN 139781788990325
Course Length2 hours 08 minutes
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