Courses

• Python Basics & Libraries Overview (NumPy, Pandas, Matplotlib)
• Working with DataFrames
• Data Cleaning and Manipulation
• Visualizing Data with Seaborn and Matplotlib

• Handling Missing Values
• Encoding Categorical Data
• Feature Scaling
• Train/Test Splitting
• Data Pipeline Creation

• Linear Regression
• Logistic Regression
• Decision Trees and Random Forests
• Support Vector Machines (SVM)
• K-Nearest Neighbors (KNN)

• K-Means Clustering
• Hierarchical Clustering
• Principal Component Analysis (PCA)
• Anomaly Detection Techniques

• Accuracy, Precision, Recall, F1-Score
• Confusion Matrix
• Cross Validation Techniques
• Hyperparameter Tuning (GridSearchCV)
• Overfitting and Underfitting

• Predictive Analytics (e.g., House Price Prediction)
• Classification Project (e.g., Spam Detection)
• Clustering Project (e.g., Customer Segmentation)
• End-to-End ML Workflow Deployment