8 Sections
46 Lessons
8 Hours
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Module 1: Introduction to Machine Learning (45 min)
6
1.1
What is Machine Learning?
1.2
Applications of ML in Real-World Scenarios
1.3
Types of ML: Supervised, Unsupervised, Reinforcement Learning
1.4
ML vs AI vs Deep Learning
1.5
Overview of ML Workflow (Data Collection → Preprocessing → Modeling → Evaluation → Deployment)
1.6
Popular ML Libraries: Scikit-Learn, TensorFlow, PyTorch
Module 2: Setting Up the Environment & Essential Libraries (30 min)
4
2.1
Installing Python, Jupyter Notebook, and Anaconda
2.2
Overview of Libraries: NumPy, Pandas, Matplotlib, Seaborn
2.3
Hands-on: Loading and Visualizing a Dataset
2.4
Introduction to Google Colab for Cloud-Based ML
Module 3: Data Preprocessing & Feature Engineering (1 Hour)
6
3.1
Understanding Data: Structured vs Unstructured
3.2
Handling Missing Values, Duplicates, and Outliers
3.3
Feature Scaling & Normalization (MinMaxScaler, StandardScaler)
3.4
Encoding Categorical Variables (Label Encoding, One-Hot Encoding, Ordinal Encoding)
3.5
Feature Selection: Removing Redundant & Irrelevant Features
3.6
Hands-on: Preprocessing a Real Dataset
Module 4: Supervised Learning - Regression (1 Hour 15 min)
6
4.1
What is Regression?
4.2
Linear Regression: Concept & Implementation
4.3
Polynomial Regression: When to Use It?
4.4
Decision Tree & Random Forest Regression
4.5
Performance Metrics: RMSE, MAE, R² Score
4.6
Hands-on: Predicting House Prices using Regression
Module 5: Supervised Learning - Classification (1 Hour 15 min)
6
5.1
What is Classification?
5.2
Logistic Regression: Understanding the Sigmoid Function
5.3
Decision Trees & Random Forest: Pros & Cons
5.4
Support Vector Machines (SVM): Concept & Implementation
5.5
Performance Metrics: Accuracy, Precision, Recall, F1 Score, Confusion Matrix, ROC Curve
5.6
Hands-on: Spam Email Detection using Classification
Module 6: Unsupervised Learning (1 Hour 15 min)
6
6.1
Introduction to Clustering
6.2
K-Means Clustering: Algorithm & Practical Implementation
6.3
Hierarchical Clustering: Concept & Implementation
6.4
Anomaly Detection: Identifying Outliers in Data
6.5
Dimensionality Reduction: PCA (Principal Component Analysis)
6.6
Hands-on: Customer Segmentation Using Clustering
Module 7: Model Evaluation & Hyperparameter Tuning (1 Hour)
6
7.1
Train-Test Split & Cross-Validation
7.2
Bias-Variance Tradeoff
7.3
Overfitting vs Underfitting
7.4
Hyperparameter Tuning: ○ Grid Search ○ Random Search ○ Automated Hyperparameter Tuning
7.5
Saving & Loading ML Models for Future Use
7.6
Hands-on: Tuning a Classification Model for Better Performance
Module 8: End-to-End ML Project (1 Hour)
6
8.1
Choosing a Dataset for the Final Project (E.g., House Price Prediction, Customer Churn Analysis)
8.2
Data Cleaning, Feature Engineering, Model Selection
8.3
Building & Evaluating the ML Model
8.4
Introduction to Model Deployment (Flask, Streamlit, FastAPI)
8.5
Best Practices in Machine Learning Projects
8.6
Final Thoughts & Next Steps in ML Learning
Machine Learning Mastery: A Fast-Track 8-Hour Practical Bootcamp
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