Course Content

Covers ensemble methods, neural networks, and deep learning basics. Includes ML capstone project, mock interviews, and placement preparation. What is ML & its types (Supervised, Unsupervised, Reinforcement) ML workflow (data → model → evaluation) Use cases in industries Regression (Linear, Multiple), Classification (Logistic Regression, Decision Trees), Model evaluation metrics.

What You Will Learn?

• Clustering (K-means, Hierarchical), Dimensionality reduction (PCA)
• Applications of clustering, Ensemble methods (Random Forest, Gradient Boosting)
• Neural networks basics, Overfitting & regularization, Neural networks architecture
• TensorFlow & PyTorch basics, Hands-on project (image or text classification)
• Complete ML pipeline project: Data → Feature engineering → Model → Deployment
• Example: Fraud detection / Credit risk prediction / Healthcare predictions
• Mock interviews (ML concepts, algorithms, real-world problem solving)
• Resume workshops highlighting ML case studies
• Job referrals for ML Engineer & Data Scientist roles

Outcome

• Job-ready Machine Learning Engineer / Predictive Analytics Specialist with hands-on experience in supervised, unsupervised learning, and deep learning basics.