Step-by-Step Machine Learning with Python [Video]

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Step-by-Step Machine Learning with Python [Video]

Yuxi (Hayden) Liu

Put your Python skills to the test and enter the big world of data science to learn the most effective machine learning tools and techniques with this interesting guide
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Video Details

ISBN 139781788622370
Course Length4 hours 56 minutes

Video Description

Data science and machine learning are some of the top buzzwords in the technical world today. The resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This video is your entry point to machine learning. It starts with an introduction to machine learning and the Python language and shows you how to complete the necessary setup. Moving ahead, you will learn all the important concepts such as exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression, and model performance evaluation. With the help of the various projects included, you will acquire the mechanics of several important machine learning algorithms, which will no longer seem obscure. Also, you will be guided step-by-step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and master best practices for applying machine learning techniques. Throughout this course, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple Python language. Interesting and easy-to-follow examples—including news topic classification, spam email detection, online ad click-through prediction, and stock prices forecasts—will keep you glued to the screen till you reach your goal.

Style and Approach

This course is an enticing journey that starts from the very basics and gradually picks up pace as the story unfolds. Each concept is first succinctly defined in the larger context of things, followed by a detailed explanation of their application. Every concept is explained with the help of a project that solves a real-world problem and involves hands-on work, giving you a deep insight into the world of machine learning. With this simple yet rich language—Python—you will understand and be able to implement the examples with ease.

Table of Contents

Getting Started with Python and Machine Learning
The Course Overview
Introduction to Machine Learning
Installing Software and Setting Up
Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms
Understanding NLP
Touring Powerful NLP Libraries in Python
Getting the Newsgroups Data
Thinking about Features
Visualization
Data Preprocessing
Clustering
Topic Modeling
Spam Email Detection with Naïve Bayes
Getting Started with Classification
Exploring Naïve Bayes
The Mechanics of Naïve Bayes
The Naïve Bayes Implementation
Classifier Performance Evaluation
Model Tuning and cross-validation
News Topic Classification with Support Vector Machine
Recap and Inverse Document Frequency
The Mechanics of SVM
The Implementations of SVM
The Kernels of SVM
Choosing Between the Linear and the RBF Kernel
News topic Classification with Support Vector Machine
Fetal State Classification with SVM
Click-Through Prediction with Tree-Based Algorithms
Brief Overview of Advertising Click-Through Prediction
Decision Tree Classifier
The Implementations of Decision Tree
Click-Through Prediction with Decision Tree
Random Forest - Feature Bagging of Decision Tree
Click-Through Prediction with Logistic Regression
One-Hot Encoding - Converting Categorical Features to Numerical
Logistic Regression Classifier
Click-Through Prediction with Logistic Regression by Gradient Descent
Feature Selection via Random Forest
Stock Price Prediction with Regression Algorithms
Brief Overview of the Stock Market And Stock Price
Predicting Stock Price with Regression Algorithms
Data Acquisition and Feature Generation
Linear Regression
Decision Tree Regression
Support Vector Regression
Regression Performance Evaluation
Stock Price Prediction with Regression Algorithms
Best Practices
Best Practices in Data Preparation Stage
Best Practices in the Training Sets Generation Stage
Best Practices in the Model Training, Evaluation, and Selection Stage
Best Practices in the Deployment and Monitoring Stage

What You Will Learn

  • Exploit the power of Python to handle data extraction, manipulation, and exploration techniques
  • Use Python to visualize data spread across multiple dimensions and extract useful features
  • Delve into the world of analytics to predict situations correctly
  • Implement machine learning classification and regression algorithms from scratch in Python
  • Be amazed to see algorithms in action
  • Evaluate the performance of a machine learning model and optimize it
  • Solve interesting, real-world problems using machine learning and Python as the journey unfolds

Authors

Table of Contents

Getting Started with Python and Machine Learning
The Course Overview
Introduction to Machine Learning
Installing Software and Setting Up
Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms
Understanding NLP
Touring Powerful NLP Libraries in Python
Getting the Newsgroups Data
Thinking about Features
Visualization
Data Preprocessing
Clustering
Topic Modeling
Spam Email Detection with Naïve Bayes
Getting Started with Classification
Exploring Naïve Bayes
The Mechanics of Naïve Bayes
The Naïve Bayes Implementation
Classifier Performance Evaluation
Model Tuning and cross-validation
News Topic Classification with Support Vector Machine
Recap and Inverse Document Frequency
The Mechanics of SVM
The Implementations of SVM
The Kernels of SVM
Choosing Between the Linear and the RBF Kernel
News topic Classification with Support Vector Machine
Fetal State Classification with SVM
Click-Through Prediction with Tree-Based Algorithms
Brief Overview of Advertising Click-Through Prediction
Decision Tree Classifier
The Implementations of Decision Tree
Click-Through Prediction with Decision Tree
Random Forest - Feature Bagging of Decision Tree
Click-Through Prediction with Logistic Regression
One-Hot Encoding - Converting Categorical Features to Numerical
Logistic Regression Classifier
Click-Through Prediction with Logistic Regression by Gradient Descent
Feature Selection via Random Forest
Stock Price Prediction with Regression Algorithms
Brief Overview of the Stock Market And Stock Price
Predicting Stock Price with Regression Algorithms
Data Acquisition and Feature Generation
Linear Regression
Decision Tree Regression
Support Vector Regression
Regression Performance Evaluation
Stock Price Prediction with Regression Algorithms
Best Practices
Best Practices in Data Preparation Stage
Best Practices in the Training Sets Generation Stage
Best Practices in the Model Training, Evaluation, and Selection Stage
Best Practices in the Deployment and Monitoring Stage

Video Details

ISBN 139781788622370
Course Length4 hours 56 minutes
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