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This course provides a complete introduction to Machine Learning, covering basic concepts like linear regression to advanced topics like ensemble learning and hyperparameter tuning. It offers hands-on projects in Python, helping learners build practical skills in both supervised and unsupervised learning.
40+20+40+8
Regression Techniques (Linear, Logistic, Ridge, Lasso)Classification Models (Logistic Regression, KNN, Decision Trees)Clustering (K-Means, Hierarchical)Model Evaluation (Confusion Matrix, Accuracy, Precision, Recall)Feature Engineering (Missing Value Treatment, Encoding, Outlier Handling)Hyperparameter Tuning (Grid Search, Random Search)Ensemble Learning (Bagging, AdaBoost, XGBoost)Cross-Validation, Model Optimization
Explore the fundamentals of Machine Learning through real-world projects, focusing on algorithms such as linear regression, KNN, decision trees, and ensemble methods. Learn to preprocess data, evaluate models, and apply techniques like feature engineering and hyperparameter tuning to improve predictive accuracy.
