Machine learning (Mode : Online Remote )

Eligible : Open To all Freshers & College Students (UG,PG)

What We Offer ?

(Mode : Online Remote )

✅ Internship + Training with Offer Letter & Course Completion Certificate

✅ ₹25,000 Per Month Internship For 6 Months After Course Completion

✅PPO up to 4.5–8 LPA( Based on internship performance )

✅ Duration: 1 to 6 Months .

✅ Weekend Live Session: Every Sunday at 6 PM

✅ Letter of Recommendation Included

✅ 100% Placement Support After Course + Projects

✅ 4 Minor & 2 Major Real-Time Projects

✅ Lab Sessions for Every Chapter

✅ Machine learning : Scratch to Advanced Level

✅ Recorded Theory & Lab Sessions + Weekly Assignments

📘 Machine learning – Course Index – Scratch to Advance

1. Introduction to Machine Learning
2. Math Prerequisites (Linear Algebra, Calculus, Probability & Statistics)
3. Python for ML (NumPy, pandas, Matplotlib)
4. Data Collection & Cleaning
5. Exploratory Data Analysis (EDA)
6. Feature Engineering & Feature Selection

7. Supervised Learning: Problem Framing

8. Linear Regression

9. Classification Algorithms (Logistic Regression, k-NN, SVM)

10. Model Evaluation & Validation (Cross-Validation, Metrics)

11. Unsupervised Learning: Clustering (k-means, Hierarchical)

12. Dimensionality Reduction (PCA, t-SNE, UMAP)

13. Regularization & Bias–Variance Tradeoff

14. Decision Trees & Random Forests

15. Gradient Boosting Machines (XGBoost, LightGBM, CatBoost)

16. Feature Pipelines & Data Leakage Prevention

17. Hyperparameter Tuning & Model Selection

18. Neural Network Basics (Perceptron → MLP)

19. Optimization Techniques (SGD, Adam, Learning Rate Schedules)

20. Convolutional Neural Networks (CNNs)

21. Recurrent Neural Networks, LSTM/GRU

22. Transformers & Attention Mechanisms

23. Transfer Learning & Pretrained Models

ADVANCED & SPECIALIZED TOPICS

24. Generative Models (VAEs, GANs, Diffusion Models)

25. Probabilistic Models & Bayesian ML

26. Graph Neural Networks (GNNs)

27. Causal Inference & Causal ML

28. Reinforcement Learning (Policy Gradients, Q-Learning)

29. Model Interpretability & Explainability (SHAP, LIME)

30. MLOps, Deployment & Monitoring (APIs, CI/CD, Drift)

31. Scalability, Distributed Training & Data Engineering

32. Ethics, Privacy, Fairness & Responsible AI

33. Research Methods, Paper Reading & Reproducibility

34. Capstone Projects & Real-world Case Studies