Machine Learning

The Machine Learning (ML) course is designed to equip students and professionals with the knowledge and skills required to develop intelligent systems that learn from data. The course covers both theoretical concepts and practical applications of ML, using popular tools and frameworks such as Python, NumPy, Pandas, Scikit-learn, and TensorFlow. Learners will gain expertise in supervised, unsupervised, and reinforcement learning techniques, as well as model evaluation and optimization.

Trainer

P. Suresh, MCA, M.Phil

Course Fee

₹18000

Teaching Method

100% Practical

Duration

3 to 4 Months

Introduction to Machine Learning

Basics of ML, AI vs ML vs DL, real-world applications, and ML workflow.

Python for Machine Learning

Core Python programming, data structures, NumPy, Pandas, and data handling.

Data Preprocessing & Visualization

Data cleaning, feature selection, handling missing values, Matplotlib, Seaborn.

Supervised Learning

Linear regression, logistic regression, decision trees, random forests, support vector machines (SVM).

Unsupervised Learning

Clustering (K-Means, Hierarchical), dimensionality reduction (PCA).

Neural Networks & Deep Learning Basics

Introduction to artificial neural networks, TensorFlow/Keras basics.

Model Evaluation & Optimization

Cross-validation, hyperparameter tuning, accuracy, precision, recall, F1-score.

Demo Project

Hands-on real-world project such as predictive analytics, classification model, or recommendation system.