Machine Learning & Data Science Training – Top ML Course in Chennai

Course Overview

Machine Learning is transforming how businesses operate worldwide. This course covers the end-to-end data pipeline: from initial data wrangling, cleaning, and visualization using Pandas and Matplotlib, to deploying complex supervised learning algorithms like SVM and Random Forest using Scikit-Learn. It is fundamentally designed for aspiring Data Scientists.

What you'll learn

Python Data Science toolkit
Supervised Learning Algorithms
Clustering & Dimensionality
Neural Network Basics

Key Highlights

Exploratory Data Analysis (EDA)
Feature Engineering
Hyperparameter Tuning
Industry standard Scikit-Learn

Course Syllabus

Module 1: Python for Data Science

Introduction to Jupyter Notebooks and Environments
NumPy Basics: Working with Multi-dimensional Arrays
Pandas Fundamentals: Series, DataFrames, and Data manipulation
Data Cleaning (handling missing data, outliers)
Data Visualization using Matplotlib & Seaborn
Exploratory Data Analysis (EDA) concepts

Module 2: Supervised Learning Methods

Introduction to Machine Learning terminology
Linear Regression (Simple and Multiple)
Logistic Regression for Classification
Decision Trees and Information Gain
Support Vector Machines (SVM) & Kernel tricks
Model Evaluation (Accuracy, Precision, Recall, F1-Score)

Module 3: Ensemble & Unsupervised Learning

Ensemble Methods overview (Bagging vs Boosting)
Random Forests and Feature Importance
Gradient Boosting (XGBoost, LightGBM basics)
Unsupervised Learning: K-Means Clustering
Hierarchical Clustering basics
Principal Component Analysis (PCA) for dimensionality reduction

Module 4: Deep Learning Foundations

Introduction to Artificial Neural Networks (ANN)
Forward and Backpropagation intuition
Activation Functions (Sigmoid, ReLU, Softmax)
Introduction to TensorFlow / Keras Framework
Building a basic classification neural network
Brief introduction to Convolutional Neural Networks (CNNs)

Machine Learning: Predictive Modeling & AI