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Mastering Machine Learning Algorithms using Python [Video]

Mastering Machine Learning Algorithms using Python: Build and Optimize ML Models Using Supervised and Unsupervised Algorithms

Manas Dasgupta

By Manas Dasgupta

$89.99

Video Jul 2024 28 hours 35 minutes 1st Edition

Renews at $19.99p/m

Manas Dasgupta

By Manas Dasgupta

$89.99

Video Jul 2024 28 hours 35 minutes 1st Edition

Renews at $19.99p/m

Renews at $19.99p/m

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Download this video in MP4 format

Access this title in our online reader with advanced features

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View table of contents Download Code

Key benefits

From basic ML concepts to advanced algorithms
Detailed Python modules to enhance your coding skills
Techniques for tuning and optimizing machine learning models

Description

Embark on a comprehensive journey into the world of machine learning with this expertly designed course. Begin with foundational principles, terminology of machine learning. Explore various types of machine learning problems, including regression and classification, and understand the crucial role of data. Progressing further, the course covers essential statistical techniques and Python programming. Learn to work with different data types, perform descriptive statistics, and understand probability theory and hypothesis testing. The Python modules span from basic syntax to advanced data manipulation with libraries like NumPy and pandas, providing a solid foundation for data analysis and model building. The course culminates with hands-on projects and case studies, blending theoretical knowledge with practical experience. You'll implement algorithms such as linear and logistic regression, Naive Bayes, decision trees, random forests, and support vector machines. The deep learning section offers a glimpse into neural networks. Through these projects, you’ll gain the skills to optimize and evaluate models, ensuring their effectiveness in real-world scenarios.

What you will learn

Implement regression and classification algorithms
Perform exploratory data analysis and data preprocessing
Utilize Python libraries for data manipulation and visualization
Build and optimize machine learning models
Understand the principles of deep learning
Develop skills to solve real-world machine learning problems

What do you get with a video?

Download this video in MP4 format

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

Product Details

Publication date : Jul 15, 2024

Length 28 hours 35 minutes

Edition : 1st Edition

Language : English

ISBN-13 : 9781836646716

Category :

Languages :

Python (Intermediate)

Concepts :

Machine Learning

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

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

4. Exploratory Data Analysis

Exploratory Data Analysis

Tools and Processes of EDA

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 Evaluation and Optimization

Model Optimization

6. Logistic Regression

Logistic Regression Introduction

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

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About the author

Manas Dasgupta

Manas Dasgupta

Manas Dasgupta, from Bangalore, the Silicon Valley of India, holds an MSc in AI from Liverpool John Moores University (LJMU), UK. His expertise lies in Generative AI, Machine Learning, and Data Science, focusing on RAG Application Development with LangChain and LlamaIndex, and various supervised and unsupervised techniques. His research includes NLP using Deep Learning, Siamese Networks, and BERT. With over 20 years of IT experience, mainly in Financial Services, he founded Teksands, where his team develops Gen AI applications in the talent space.

See other products by Manas Dasgupta

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