Machine Learning Course in Kolkata
Master predictive modeling and intelligent algorithms with the Machine Learning Course in Kolkata at Acesoftech Academy. Master Python, Scikit-Learn, PyTorch, gradient boosting, neural networks, and MLOps deployment with 100% placement support.
Accelerate Your Technical Career in Kolkata
Machine learning forms the technical foundation of modern artificial intelligence. Explore advanced deep learning in our Artificial Intelligence Course in Kolkata, or review enterprise data pipelines in our Data Science Course in Kolkata and Data Analytics Course in Kolkata.
Comprehensive 12-Module Curriculum
Mathematics for Machine Learning
Linear algebra, matrix factorizations, vector spaces, gradient calculus, and probability distributions.
- Industry-aligned hands-on practical exercises
- Enterprise architectural patterns and best practices
- Production-grade deployment and real-time debugging
Python ML Foundations: NumPy & Pandas
Vectorized numerical computing, DataFrame manipulations, missing value imputation, and encoding.
- Industry-aligned hands-on practical exercises
- Enterprise architectural patterns and best practices
- Production-grade deployment and real-time debugging
Feature Engineering & Data Preprocessing
Feature scaling (MinMax, Standard), one-hot encoding, feature selection, and polynomial features.
- Industry-aligned hands-on practical exercises
- Enterprise architectural patterns and best practices
- Production-grade deployment and real-time debugging
Supervised Learning: Regression Models
Linear regression, ordinary least squares, ridge, lasso, and elastic net regularization.
- Industry-aligned hands-on practical exercises
- Enterprise architectural patterns and best practices
- Production-grade deployment and real-time debugging
Supervised Learning: Classification Models
Logistic regression, decision trees, support vector machines (SVM), and k-nearest neighbors.
- Industry-aligned hands-on practical exercises
- Enterprise architectural patterns and best practices
- Production-grade deployment and real-time debugging
Model Evaluation & Cross-Validation
Confusion matrix, precision, recall, F1-score, ROC-AUC curves, and k-fold cross-validation.
- Industry-aligned hands-on practical exercises
- Enterprise architectural patterns and best practices
- Production-grade deployment and real-time debugging
Ensemble Learning: Random Forests & Boosting
Bagging vs boosting, Random Forest tuning, AdaBoost, Gradient Boosting, and XGBoost.
- Industry-aligned hands-on practical exercises
- Enterprise architectural patterns and best practices
- Production-grade deployment and real-time debugging
Unsupervised Learning & Clustering
K-Means, hierarchical agglomerative clustering, DBSCAN density clustering, and PCA reduction.
- Industry-aligned hands-on practical exercises
- Enterprise architectural patterns and best practices
- Production-grade deployment and real-time debugging
Introduction to Deep Learning with PyTorch
Perceptron, multi-layer perceptron (MLP), backpropagation, activation functions, and PyTorch tensors.
- Industry-aligned hands-on practical exercises
- Enterprise architectural patterns and best practices
- Production-grade deployment and real-time debugging
Natural Language Processing (NLP) with ML
Text vectorization, CountVectorizer, TF-IDF, sentiment analysis, and spam classification.
- Industry-aligned hands-on practical exercises
- Enterprise architectural patterns and best practices
- Production-grade deployment and real-time debugging
Model Deployment with FastAPI & Docker
Serializing models with joblib, creating RESTful inference APIs with FastAPI, and Docker containers.
- Industry-aligned hands-on practical exercises
- Enterprise architectural patterns and best practices
- Production-grade deployment and real-time debugging
Production ML Capstone & 100% Placement
End-to-end predictive pipeline with automated retraining, portfolio review, and placement support.
- Industry-aligned hands-on practical exercises
- Enterprise architectural patterns and best practices
- Production-grade deployment and real-time debugging
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Frequently Asked Questions
The course covers Python, mathematics for ML, Scikit-Learn, PyTorch, XGBoost, feature engineering, FastAPI, and Docker.
Basic high school algebra is sufficient; all relevant calculus and linear algebra are taught step-by-step.
Yes, 100% placement support is guaranteed with mock technical rounds and corporate recruitment drives.
Yes, you will build live classification and regression models and deploy them as REST APIs.
NumPy, Pandas, Scikit-Learn, PyTorch, Matplotlib, Seaborn, XGBoost, and FastAPI.
Weekday intensive classes and weekend batches are available for students and professionals.
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