Machine Learning Training

Machine Learning Course Syllabus – Best AI & ML Training in Trichy
Looking to learn Machine Learning from scratch or upgrade your AI skills? Our Machine Learning training in Trichy is divided into well-structured, industry-relevant modules covering everything from Python basics to model deployment and MLOps.
Module 1: Introduction to Machine Learning & Python

• What is Machine Learning? Types: Supervised, Unsupervised, Reinforcement
• Overview of AI, ML, Deep Learning, and Data Science ecosystem
• Python for ML: NumPy, Pandas, Matplotlib, Seaborn
• Setting up the ML Environment (Jupyter Notebook, Anaconda, Google Colab)
• Mathematical Foundations: Linear Algebra, Statistics & Probability for ML                                                                                                                                                                                                                                                                                                                                         machine learning beginners course trichy, python for machine learning, AI course DOT Institute

Module 2: Data Preprocessing & Exploratory Data Analysis (EDA)

• Importing and Cleaning Datasets (handling missing values, duplicates)
• Feature Engineering and Feature Selection
• Data Normalization, Standardization, and Encoding
• Exploratory Data Analysis (EDA) with Pandas & Seaborn
• Train-Test Split and Cross-Validation Techniques

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Module 3: Supervised Learning Algorithms

• Linear Regression and Logistic Regression
• Decision Trees and Random Forest
• Support Vector Machine (SVM)
• K-Nearest Neighbours (KNN)
• Gradient Boosting: XGBoost, AdaBoost
• Model Evaluation: Accuracy, Precision, Recall, F1 Score, ROC-AUC

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Module 4: Unsupervised Learning & Clustering

• K-Means Clustering and Hierarchical Clustering
• DBSCAN – Density-Based Clustering
• Principal Component Analysis (PCA)
• Dimensionality Reduction Techniques
• Association Rule Mining (Apriori, FP-Growth)
• Anomaly Detection and Outlier Analysis

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Module 5: Deep Learning & Neural Networks

Introduction to Artificial Neural Networks (ANN)
Activation Functions, Forward & Backward Propagation
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN) and LSTM
TensorFlow and Keras Framework
Hands-On: Image Classification, Sentiment Analysis Projects

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Module 6: Natural Language Processing (NLP)

Text Preprocessing: Tokenization, Stemming, Lemmatization
Bag of Words, TF-IDF, Word Embeddings (Word2Vec, GloVe)
Sentiment Analysis and Text Classification
Named Entity Recognition (NER)
Chatbot Development Basics
Introduction to Large Language Models (LLMs) & Transformers

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Module 7: Computer Vision & Image Recognition

Introduction to OpenCV and Image Processing
Object Detection using YOLO
Face Recognition Systems
Image Classification with CNN
Transfer Learning (VGG, ResNet, EfficientNet)
Real-World Project: Smart Surveillance / Medical Image Analysis

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Module 8: Model Deployment & MLOps

Saving and Loading ML Models (Pickle, Joblib)
Building REST APIs with Flask & FastAPI for ML Models
Deployment on AWS, Azure, and Google Cloud (GCP)
Docker Basics for ML Applications
Introduction to MLflow and Model Monitoring
Building a Full-Stack ML Web Application (End-to-End Project)

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