Machine Learning A–Z™: Hands-On Python & Real Projects

Xcel Learning

Review Questions and Assessments

Included in course price

• Chapter 1: Introduction to Machine Learning & Python Setup 07:00

  • 1: Chapter 1: Introduction to Machine Learning & Python Setup Preview 07:00

• Chapter 2: Data Preprocessing & Feature Engineering 08:00

  • 2: Chapter 2: Data Preprocessing & Feature Engineering 08:00

• Chapter 3: Regression Models – Part I 07:00

  • 3: Chapter 3: Regression Models – Part I 07:00

• Chapter 4: Regression Models – Part II 07:00

  • 4: Chapter 4: Regression Models – Part II 07:00

• Chapter 5: Classification Models – Part I 07:00

  • 5: Chapter 5: Classification Models – Part I 07:00

• Chapter 6: Classification Models – Part II 06:00

  • 6: Chapter 6: Classification Models – Part II 06:00

• Chapter 7: Clustering Techniques 06:00

  • 7: Chapter 7: Clustering Techniques 06:00

• Chapter 8: Association Rule Learning 06:00

  • 8: Chapter 8: Association Rule Learning 06:00

• Chapter 9: Reinforcement Learning 06:00

  • 9: Chapter 9: Reinforcement Learning 06:00

• Chapter 10: Natural Language Processing (NLP) 06:00

  • 10: Chapter 10: Natural Language Processing (NLP) 06:00

• Chapter 11: Deep Learning – Artificial Neural Networks 06:00

  • 11: Chapter 11: Deep Learning – Artificial Neural Networks 06:00

• Chapter 12: Model Deployment & Real-World ML 06:00

  • 12: Chapter 12: Model Deployment & Real-World ML 06:00

• Review Questions and Assessments 00:00

  • 13: Review Questions and Assessments -

Exciting Adventures Await: Discover the Fascinating Topics This Course Will Explore!

Chapter 1: Introduction to Machine Learning & Python Setup

1. What is Machine Learning? Types and Real-World Applications
2. Setting Up Python Environment (Anaconda, Jupyter, VS Code)
3. Essential Python Libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn
4. Data Preprocessing Fundamentals
5. End-to-End ML Project Overview

Chapter 2: Data Preprocessing & Feature Engineering

1. Handling Missing Data
2. Encoding Categorical Variables
3. Feature Scaling (Standardization & Normalization)
4. Splitting Data into Train/Test Sets
5. Feature Engineering Techniques

Chapter 3: Regression Models – Part I

1. Simple Linear Regression
2. Multiple Linear Regression
3. Polynomial Regression
4. Model Evaluation Metrics (R², MAE, MSE, RMSE)
5. Regression Project: Predicting House Prices

Chapter 4: Regression Models – Part II

1. Support Vector Regression (SVR)
2. Decision Tree Regression
3. Random Forest Regression
4. Regularization (Ridge, Lasso, ElasticNet)
5. Regression Model Comparison Project

Chapter 5: Classification Models – Part I

1. Logistic Regression
2. K-Nearest Neighbors (KNN)
3. Support Vector Machine (SVM)
4. Naive Bayes Classifier
5. Classification Project: Customer Purchase Prediction

Chapter 6: Classification Models – Part II

1. Decision Tree Classification
2. Random Forest Classification
3. Gradient Boosting (XGBoost Basics)
4. Confusion Matrix & Classification Metrics
5. Classification Project: Fraud Detection

Chapter 7: Clustering Techniques

1. K-Means Clustering
2. Choosing Optimal Clusters (Elbow Method & Silhouette Score)
3. Hierarchical Clustering
4. DBSCAN Clustering
5. Clustering Project: Customer Segmentation

Chapter 8: Association Rule Learning

1. Apriori Algorithm
2. Eclat Algorithm
3. Support, Confidence & Lift
4. Market Basket Analysis
5. Association Rule Project: Retail Insights

Chapter 9: Reinforcement Learning

1. Introduction to Reinforcement Learning Concepts
2. Upper Confidence Bound (UCB)
3. Thompson Sampling
4. Multi-Armed Bandit Problem
5. Reinforcement Learning Project: Ad Optimization

Chapter 10: Natural Language Processing (NLP)

1. Text Cleaning & Preprocessing
2. Bag of Words Model
3. TF-IDF & Word Embeddings Basics
4. Sentiment Analysis with Naive Bayes
5. NLP Project: Review Classification

Chapter 11: Deep Learning – Artificial Neural Networks

1. Neural Network Fundamentals
2. Activation Functions & Backpropagation
3. Building ANN with Keras/TensorFlow
4. Overfitting, Dropout & Regularization
5. Deep Learning Project: Churn Prediction

Chapter 12: Model Deployment & Real-World ML

1. Model Evaluation & Cross-Validation
2. Hyperparameter Tuning (Grid Search & Random Search)
3. Saving & Loading Models (Pickle & Joblib)
4. Building ML APIs with Flask/FastAPI
5. Deploying ML Models to Cloud (Heroku/AWS)

Unleash Your Potential: Join Us Today and Elevate Your Skills with a Prestigious Digital Certificate upon Course Completion!

Machine Learning A–Z™: Hands-On Python & Real Projects is designed for aspiring data scientists, students, developers, and professionals seeking practical skills in machine learning. It suits beginners wanting structured guidance and intermediates aiming to strengthen real-world project experience, covering Python implementation, model building, and deployment concepts with clear, step-by-step learning.

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