Curriculum
7 Sections
24 Lessons
10 Weeks
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Module 1: Introduction to Machine Learning
4
1.1
What is Machine Learning?
1.2
Types of ML: Supervised, Unsupervised, and Reinforcement Learning
1.3
Applications of ML in real-world industries
1.4
Setting up a Python ML environment (Jupyter Notebook, Anaconda, Google Colab)
Module 2: Python for Machine Learning
3
2.1
Python basics: Variables, loops, functions, and libraries
2.2
Introduction to NumPy and Pandas for data manipulation
2.3
Data visualization using Matplotlib and Seaborn
Module 3: Data Preprocessing & Feature Engineering
4
3.1
Handling missing data and duplicates
3.2
Feature scaling and normalization
3.3
One-hot encoding and label encoding for categorical variables
3.4
Splitting datasets into training and testing sets
Module 4: Supervised Learning – Regression Models
3
4.1
Understanding regression algorithms
4.2
Implementing Linear Regression, Polynomial Regression, Ridge, and Lasso Regression
4.3
Evaluating models using Mean Squared Error (MSE) and R² Score
Module 5: Supervised Learning – Classification Models
4
5.1
Logistic Regression for binary classification
5.2
Decision Trees and Random Forest Classifier
5.3
Support Vector Machines (SVM)
5.4
Model evaluation using Confusion Matrix, Precision, Recall, F1-score
Module 6: Unsupervised Learning – Clustering & Dimensionality Reduction
3
6.1
Understanding clustering algorithms (K-Means, DBSCAN, Hierarchical Clustering)
6.2
Dimensionality reduction techniques (PCA, LDA, t-SNE)
6.3
Applications of unsupervised learning
Module 7: Model Selection & Hyperparameter Tuning
3
7.1
Bias-variance tradeoff
7.2
Cross-validation techniques (K-Fold, Leave-One-Out)
7.3
Hyperparameter tuning with GridSearchCV & RandomizedSearchCV
Machine Learning – Beginner
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