Preprocess and transform data effectively to prepare it for machine learning applications.
- Data pre processing
- Data Transformation, Data Reduction
- Data Wrangling and Manipulation for Machine Learning
- Feature Scaling
- Categorical Conversions
Learn and apply various regression techniques to analyze data trends and build accurate predictive models
- Introduction to Machine Learning
- Simple Linear Regression
- Multiple Linear Regression
- Support Vector Regressor
- Decision Tree Regressor
- Ridge and Lasso Regression
Understand key model validation techniques such as hold-out, cross-validation, and bootstrapping to assess model performance effectively.
- Hold-out method
- Cross Validation Method
- Boot Straping method
Explore key classification techniques such as logistic regression, decision trees, and support vector machines (SVM) to build accurate predictive models.
- Introduction to Classification Techniques
- Logistic Regression
- Ensemble techniques
- Decision trees
- Random Forest
- Naïve Bayes
- K-Nearest Neighbours
- SMOTE
- Support Vector Machine
Use evaluation metrics such as confusion matrix, accuracy, precision, recall, and F1-score to assess and compare model performance effectively.
- Confusion Matrix
- Accuracy
- F1-Score, Precision, Recall
Learn and apply clustering techniques such as hierarchical clustering, K-means, and DBSCAN to identify patterns and group similar data effectively.
- Introduction to Unsupervised Learning
- Heirarchial Clustering
- K-Means Clustering
- DBSCAN (Density Based Spatial Clustering of Applications with Noise)
Apply dimensionality reduction techniques such as PCA, SVD, and t-SNE to simplify data while preserving key patterns and insights.
- Principal Component Analysis (PCA)
- Singular Value Decomposition (SVD)
- T-distributed Stochastic Neighbor Embedding (t-SNE)
Understand the principles of association rules and learn to build recommender systems that provide personalized suggestions based on user behavior.
- Association Rule, FP Growth
- Case Study
- Apriori Algorith in Python
- Recommender Systems
- Weighted Score Recommender System
- User Based Similarity
