Ayushi Mishra

This all-in-one Machine Learning eBook is your complete guide to learning real-world ML algorithms and its core concepts not just by reading, but by implementing them with hands-on Python notebooks.

Whether you’re a:

• Beginner starting your ML journey
• Data Analyst looking to upgrade your skills
• Career-switcher preparing for your first ML job

This bundle gives you everything you need to succeed.

✅ What You’ll Get:

📗 Machine Learning eBook

• Covers all essential ML concepts clearly and practically
• No complex math, just what you need to build models
• Step-by-step breakdown of each algorithm
• Industry examples & real-world use cases
• Interview-ready knowledge and cheat sheets

💻 Python Practice Notebooks

• Clean, ready-to-run code for every algorithm
• Code templates you can re-use for your own projects

Table of contents

1. What is Machine Learning?

1.1 Machine Learning Lifecycle

1.2 When to use Machine Learning

1.3 Types of Machine Learning

1.4 Regression

1.5 Classification

1.6 Clustering

1.7 Machine Learning Pipeline

2. Feature Engineering

2.1 Why is it important?

2.2 Types of Feature Engineering

2.3 Feature Transformation

2.3.1 Feature Encoding

2.3.2 Feature Scaling

2.4 Feature Extraction

2.5 Feature Selection

3. Regularization

3.1 Types of Regularization

3.1.1 L1 Regularization

3.1.2 L2 Regularization

3.1.3 Elastic Net Regularization

3.2 Choosing Lambda

4. Supervised Machine Learning Models

4.1 Linear Regression

4.2 Polynomial Regression

4.3 Logistic Regression

4.4 Decision Trees

4.5 Support Vector Machines

4.6 K Nearest Neighbors

5. Errors in ML

5.1 Types of Error

5.2 Bias-Variance Tradeoff

5.3 What affects Bias and Variance

5.4 How to handle it?

6. Ensemble Methods

6.1 Why use Ensemble methods?

6.2 Types of Ensemble methods

6.3 Random Forest

6.4 Boosting Algorithms Comparison

7. Unsupervised Machine Learning Models 

7.1 K Means Clustering

7.2 DBSCAN Clustering

7.3 HDBSCAN Clustering

7.4 Agglomerative Clustering

8. Principal Component Analysis

8.1 Dimensionality Reduction

8.2 PCA

8.3 Comparing PCA, t-SNE, UMAP

9. Hyperparameter Tuning

9.1 Model Evaluation

9.2 Data Splitting Strategy

9.3 Cross Validation

9.4 Methods of Hyperparameter Tuning

10. Model Evaluation

10.1 Classification Metrics

10.2 Regression Metrics

10.3 Clustering Metrics

🔥 Limited-Time Offer

Get the eBook + Practice Code Notebooks Bundle today and take your

ML career to the next level.