Packt+ | Advance your knowledge in tech

You're reading from Mastering Machine Learning Algorithms using Python Build and Optimize ML Models Using Supervised and Unsupervised Algorithms

Product type Video

Published in Jul 2024

Publisher Packt

ISBN-13 9781836646716

Length 28hrs 35mins

Edition 1st Edition

Languages

Python

Concepts

Machine Learning

Author (1):

Manas Dasgupta

View More author details

Table of Contents (13) Chapters

1. Introduction to Machine Learning

Course Introduction
Introduction to Machine Learning
Machine Learning Terminology
History of Machine Learning
Machine Learning Use Cases and Types
Role of Data in Machine Learning
Challenges in Machine Learning
Machine Learning Life Cycle and Pipelines
Regression Problems
Regression Models and Performance Metrics
Classification Problems and Performance Metrics
Optimizing Classification Metrics
Bias and Variance

2. Statistical Techniques FREE CHAPTER

Statistics and Experiments
Types of Data and Descriptive Statistics
Random Variables and Normal Distribution
Histograms and Normal Approximation
Central Limit Theorem
Probability Theory
Binomial Theory - Expected Value and Standard Error
Hypothesis Testing

3. Learning Python

Introduction to Python
Starting with Python with Jupyter Notebook
Python Variables and Conditions
Python Iterations 1
Python Iterations 2
Python Lists
Python Tuples
Python Dictionaries 1
Python Dictionaries 2
Python Sets 1
Python Sets 2
Numpy Arrays 1
Numpy Arrays 2
Numpy Arrays 3
Pandas Series 1
Pandas Series 2
Pandas Series 3
Pandas Series 4
Pandas DataFrame 1
Pandas DataFrame 2
Pandas DataFrame 3
Pandas DataFrame 4
Pandas DataFrame 5
Pandas DataFrame 6
Python User Defined Functions
Python Lambda Functions
Python Lambda Functions and Date-Time Operations
Python String Operations

4. Exploratory Data Analysis

Exploratory Data Analysis
Tools and Processes of EDA
EDA Project 1
EDA Project 2
EDA Project 3
EDA Project 4
EDA Project 5
EDA Project 6
EDA Project 7

5. Linear Regression

Linear Regression Introduction
Training and Cost Function
Cost Functions and Gradient Descent
Linear Regression - Practical Approach
Feature Scaling and Cost Functions
OLS Assumptions and Testing
Car Price Prediction
Data Preparation and Analysis 1
Data Preparation and Analysis 2
Data Preparation and Analysis 3
Model Building
Model Evaluation and Optimization
Model Optimization

6. Logistic Regression

Logistic Regression Introduction
Logit Model
Telecom Churn Case Study
Data Analysis and Feature Engineering
Build the Logistic Model
Model Evaluation - AUC-ROC
Model Optimization 1
Model Optimization 2

7. Naive Bayes Classification Algorithm

Naive Bayes Probability Model
Naive Bayes Probability Computation
Employee Attrition Case Study
Model Building and Optimization

8. Decision Tree Classifier

Decision Tree - Model Concept
Decision Tree - Learning Steps
Gini Index and Entropy Measures
Pruning and Hyperparameter Tuning
Iris Dataset Case Study
Model Optimization using Grid Search Cross Validation

9. Random Forest Ensemble

Ensemble Techniques Bagging and Random Forest
Random Forest Steps Pruning and Optimization
Model Building and Hyperparameter Tuning using Grid Search CV
Optimization Continued

10. Support Vector Machine

Support Vector Machine Concepts
Support Vector Machine Metrics and Polynomial SVM
Support Vector Machine Project 1
Support Vector Machine Predictions
Support Vector Machine - Classifying Polynomial Data

11. Dimensionality Reduction - Principal Component Analysis (PCA)

Principal Component Analysis - Concepts
Principal Component Analysis - Computations 1
Principal Component Analysis - Computations 2
Principal Component Analysis Practicals

12. Unsupervised Learning using K-Means Clustering

Unsupervised Learning - K-Mean Clustering
K-Means Clustering Computation
K-Means Clustering Optimization
K-Means - Data Preparation and Modelling
K-Means - Model Optimization

13. Introduction to Deep Learning

Introduction to Deep Learning

You're reading from Mastering Machine Learning Algorithms using Python Build and Optimize ML Models Using Supervised and Unsupervised Algorithms

Table of Contents (13) Chapters

Authors (1)

Personalised recommendations for you

You're reading from Mastering Machine Learning Algorithms using Python Build and Optimize ML Models Using Supervised and Unsupervised Algorithms

Table of Contents (13) Chapters

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you