/Machine Learning - Beginner

Course Curriculum

Section 1 · Module 1: Introduction to Machine Learning

○ What is Machine Learning? ○ Types of ML: Supervised, Unsupervised, and Reinforcement Learning ○ Applications of ML in real-world industries ○ Setting up a Python ML environment (Jupyter Notebook, Anaconda, Google Colab)

Section 2 · Module 2: Python for Machine Learning

○ Python basics: Variables, loops, functions, and libraries ○ Introduction to NumPy and Pandas for data manipulation ○ Data visualization using Matplotlib and Seaborn

Section 3 · Module 3: Data Preprocessing & Feature Engineering

▶ Handling missing data and duplicates ○ Feature scaling and normalization ○ One-hot encoding and label encoding for categorical variables ○ Splitting datasets into training and testing sets

Section 4 · Module 4: Supervised Learning – Regression Models

○ Understanding regression algorithms ○ Implementing Linear Regression, Polynomial Regression, Ridge, and Lasso Regression ○ Evaluating models using Mean Squared Error (MSE) and R² Score

Section 5 · Module 5: Supervised Learning – Classification Models

○ Logistic Regression for binary classification ○ Decision Trees and Random Forest Classifier ○ Support Vector Machines (SVM) ○ Model evaluation using Confusion Matrix, Precision, Recall, F1-score

Section 6 · Module 6: Unsupervised Learning – Clustering & Dimensionality Reduction

○ Understanding clustering algorithms (K-Means, DBSCAN, Hierarchical Clustering) ○ Dimensionality reduction techniques (PCA, LDA, t-SNE) ○ Applications of unsupervised learning

Section 7 · Module 7: Model Selection & Hyperparameter Tuning

○ Bias-variance tradeoff ○ Cross-validation techniques (K-Fold, Leave-One-Out) ○ Hyperparameter tuning with GridSearchCV & RandomizedSearchCV

Module 3: Data Preprocessing & Feature Engineering

Handling missing data and duplicates

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