Book Image

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

By : Abhilash Nelson
4.3 (3)
Book Image

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

4.3 (3)
By: Abhilash Nelson

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)
Free Chapter
1
Course Overview and Table of Contents
2
Introduction to Machine Learning
3
System and Environment Preparation
4
Learn Basics of Python
5
Learn Basics of NumPy
6
Learn Basics of Matplotlib
7
Learn Basics of Pandas
8
Understanding the CSV Data File
9
Load and Read CSV Data File
10
Dataset Summary
11
Dataset Visualization
12
Data Preparation
13
Feature Selection
14
Refresher Session - the Mechanism of Re-Sampling, Training, and Testing
15
Algorithm Evaluation Techniques
16
Algorithm Evaluation Metrics
17
Classification Algorithm Spot Check - Logistic Regression
18
Classification Algorithm Spot Check - Linear Discriminant Analysis
19
Classification Algorithm Spot Check - K-Nearest Neighbors
20
Classification Algorithm Spot Check - Naive Bayes
21
Classification Algorithm Spot Check – CART
22
Classification Algorithm Spot Check - Support Vector Machines
23
Regression Algorithm Spot Check - Linear Regression
24
Regression Algorithm Spot Check - Ridge Regression
25
Regression Algorithm Spot Check - LASSO Linear Regression
26
Regression Algorithm Spot Check - Elastic Net Regression
27
Regression Algorithm Spot Check - K-Nearest Neighbors
28
Regression Algorithm Spot Check – CART
29
Regression Algorithm Spot Check - Support Vector Machines (SVM)
30
Compare Algorithms - Part 1: Choosing the Best Machine Learning Model
31
Compare Algorithms - Part 2: Choosing the Best Machine Learning Model
32
Pipelines: Data Preparation and Data Modelling
33
Pipelines: Feature Selection and Data Modelling
34
Performance Improvement: Ensembles – Voting
35
Performance Improvement: Ensembles – Bagging
36
Performance Improvement: Ensembles – Boosting
37
Performance Improvement: Parameter Tuning Using Grid Search
38
Performance Improvement: Parameter Tuning Using Random Search
39
Export, Save and Load Machine Learning Models: Pickle
40
Export, Save and Load Machine Learning Models: Joblib
41
Finalizing a Model - Introduction and Steps
42
Finalizing a Classification Model - the Pima Indian Diabetes Dataset
43
Quick Session: Imbalanced Dataset - Issue Overview and Steps
44
Iris Dataset: Finalizing Multi-Class Dataset
45
Finalizing a Regression Model - the Boston Housing Price Dataset
46
Real-Time Predictions: Using the Pima Indian Diabetes Classification Model
47
Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset
48
Real-Time Predictions: Using the Boston Housing Regression Model
Chapter 48
Real-Time Predictions: Using the Boston Housing Regression Model
Content Locked
Section 1
Real-Time Predictions: Using the Boston Housing Regression Model
In this video, you’ll look at real-time predictions: using the Boston housing regression model.