Machine Learning Course in Kolkata
Industry Curriculum & Syllabus Modules
Python for Machine Learning
NumPy arrays, Pandas DataFrames, vectorization, and data manipulation workflows.
- Hands-on practical code implementations
- Live scenario architecture & debugging
- Enterprise code review & optimization
Data Preprocessing & Feature Engineering
Handling missing values, outlier detection, one-hot encoding, and feature scaling.
- Hands-on practical code implementations
- Live scenario architecture & debugging
- Enterprise code review & optimization
Linear & Polynomial Regression
Cost functions, gradient descent optimization, evaluation metrics (MSE, MAE, R-squared).
- Hands-on practical code implementations
- Live scenario architecture & debugging
- Enterprise code review & optimization
Logistic Regression & Classification
Sigmoid function, decision boundaries, ROC-AUC curves, and precision-recall trade-offs.
- Hands-on practical code implementations
- Live scenario architecture & debugging
- Enterprise code review & optimization
Decision Trees & Random Forests
Gini impurity, entropy, information gain, bagging, and random forest ensembles.
- Hands-on practical code implementations
- Live scenario architecture & debugging
- Enterprise code review & optimization
Support Vector Machines (SVM)
Hyperplanes, margins, kernel trick (RBF, polynomial), and slack variables.
- Hands-on practical code implementations
- Live scenario architecture & debugging
- Enterprise code review & optimization
Boosting Algorithms: XGBoost & LightGBM
Gradient boosting principles, hyperparameter tuning, learning rates, and early stopping.
- Hands-on practical code implementations
- Live scenario architecture & debugging
- Enterprise code review & optimization
Unsupervised Learning: K-Means & PCA
Centroid initialization, elbow method, silhouette analysis, and dimensionality reduction.
- Hands-on practical code implementations
- Live scenario architecture & debugging
- Enterprise code review & optimization
Model Validation & Hyperparameter Tuning
K-fold cross-validation, stratified splits, GridSearchCV, and RandomizedSearchCV.
- Hands-on practical code implementations
- Live scenario architecture & debugging
- Enterprise code review & optimization
Introduction to Artificial Neural Networks (ANN)
Perceptrons, forward propagation, backpropagation, and loss functions in Keras.
- Hands-on practical code implementations
- Live scenario architecture & debugging
- Enterprise code review & optimization
MLOps & Model Deployment Pipelines
Saving models with Pickle/Joblib, building REST APIs with FastAPI, and Dockerizing models.
- Hands-on practical code implementations
- Live scenario architecture & debugging
- Enterprise code review & optimization
Industry Capstone Project & Placement Support
Fraud detection or recommendation system project, technical mock interviews, and placement drives.
- Hands-on practical code implementations
- Live scenario architecture & debugging
- Enterprise code review & optimization
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