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Book Overview & Buying
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Table Of Contents
Machine Learning and Data Science with Python: A Complete Beginners Guide
By :
Machine Learning and Data Science with Python: A Complete Beginners Guide
By:
Overview of this book
Artificial intelligence, machine learning, and deep learning neural networks are the most used terms in the technology world today. They're also the most misunderstood and confused terms. Artificial intelligence is a broad spectrum of science that tries to make machines intelligent like humans, while machine learning and neural networks are two subsets that sit within this vast machine learning platform. But in this course, you will focus mainly on machine learning, which will include preparing your machine to make it ready for a prediction test.
You will be using Python as your programming language. Python is a great tool for the development of programs that perform data analysis and prediction. It has a variety of classes and features that perform complex mathematical analyses and provide solutions in just a few lines of code, making it easier for you to get up to speed with data science and machine learning.
Machine learning and data science jobs are among the most lucrative in the technology industry in recent times. Exploring this course will help you get well-versed with essential concepts and prepare you for a career in these fields.
All the code and supporting files for this course are available at https://github.com/PacktPublishing/Machine-Learning-and-Data-Science-with-Python-A-Complete-Beginners-Guide
Table of Contents (48 chapters)
Course Overview and Table of Contents
Introduction to Machine Learning
System and Environment Preparation
Learn Basics of Python
Learn Basics of NumPy
Learn Basics of Matplotlib
Learn Basics of Pandas
Understanding the CSV Data File
Load and Read CSV Data File
Dataset Summary
Dataset Visualization
Data Preparation
Feature Selection
Refresher Session - the Mechanism of Re-Sampling, Training, and Testing
Algorithm Evaluation Techniques
Algorithm Evaluation Metrics
Classification Algorithm Spot Check - Logistic Regression
Classification Algorithm Spot Check - Linear Discriminant Analysis
Classification Algorithm Spot Check - K-Nearest Neighbors
Classification Algorithm Spot Check - Naive Bayes
Classification Algorithm Spot Check – CART
Classification Algorithm Spot Check - Support Vector Machines
Regression Algorithm Spot Check - Linear Regression
Regression Algorithm Spot Check - Ridge Regression
Regression Algorithm Spot Check - LASSO Linear Regression
Regression Algorithm Spot Check - Elastic Net Regression
Regression Algorithm Spot Check - K-Nearest Neighbors
Regression Algorithm Spot Check – CART
Regression Algorithm Spot Check - Support Vector Machines (SVM)
Compare Algorithms - Part 1: Choosing the Best Machine Learning Model
Compare Algorithms - Part 2: Choosing the Best Machine Learning Model
Pipelines: Data Preparation and Data Modelling
Pipelines: Feature Selection and Data Modelling
Performance Improvement: Ensembles – Voting
Performance Improvement: Ensembles – Bagging
Performance Improvement: Ensembles – Boosting
Performance Improvement: Parameter Tuning Using Grid Search
Performance Improvement: Parameter Tuning Using Random Search
Export, Save and Load Machine Learning Models: Pickle
Export, Save and Load Machine Learning Models: Joblib
Finalizing a Model - Introduction and Steps
Finalizing a Classification Model - the Pima Indian Diabetes Dataset
Quick Session: Imbalanced Dataset - Issue Overview and Steps
Iris Dataset: Finalizing Multi-Class Dataset
Finalizing a Regression Model - the Boston Housing Price Dataset
Real-Time Predictions: Using the Pima Indian Diabetes Classification Model
Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset
Real-Time Predictions: Using the Boston Housing Regression Model
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