Book Image

Supervised and Unsupervised Learning with Python [Video]

By : Prateek Joshi
Book Image

Supervised and Unsupervised Learning with Python [Video]

By: Prateek 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.

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Supervised and Unsupervised Learning with Python [Video]
Prateek Joshi
Nov, 2017 | 2 hours 8 minutes
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Table of Contents (5 chapters)
Free Chapter
1
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
2
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
3
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
4
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
5
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 5
Building Recommender Systems
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Section 4
Computing similarity scores
In order to build a recommendation system, it is important to understand how to compare various objects in our dataset. The similarity score gives us an idea of how similar two objects are. - Define a function to compute the Euclidean score between the input users - Extract the movies rated by both users - Repeat the same to compute Pearson score
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End of Section 4
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