Machine Learning and Data Science with Python: A Complete Beginners Guide: Comprehensive beginners guide for machine learning and data science with Python for programming beginners

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Learn machine learning and data science using Python

A practical course designed for beginners who are interested in machine learning using Python

Work on predictions and case studies

Description

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

What you will learn

Install Python and required libraries

Choose the best machine learning model

Automate and combine workflows with pipeline

Look at performance improvement with ensembles

Study performance improvement with algorithm parameter tuning

Finalize a machine learning project

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Machine Learning and Data Science with Python: A Complete Beginners Guide

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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

Algorithm Evaluation Techniques

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)

Regression Algorithm Spot Check - Support Vector Machines

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

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 Iris Flowers Multi-Class Classification Dataset

Real-Time Predictions: Using the Boston Housing Regression Model

Machine Learning and Data Science with Python: A Complete Beginners Guide: Comprehensive beginners guide for machine learning and data science with Python for programming beginners

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