CERTIFICATE IN MACHINE LEARNING WITH PYTHON ( S-ML WITH PYTHON )

Machine Learning with Python – 2 Months Certification Course

Course Duration: 2 Months
Mode: Offline / Online (Theory + Practical)
Prerequisites: Python Basics, Linear Algebra, Statistics

Unit 1: Introduction to Machine Learning

• What is Machine Learning?
• Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
• Understanding ML Problems
• Role of Data in ML
• Tools Used in ML (e.g., Scikit-learn, TensorFlow, Google Colab)
• Data Visualization Tools Overview

Unit 2: Neural and Probabilistic Learning

• Basics of Artificial Neural Networks (ANN)
• Bayesian Learning Fundamentals
• Deep Learning Overview
• Instance-Based Learning Techniques (e.g., k-NN)

Unit 3: Model Training & Overfitting

• Concept of Overfitting and Underfitting
• Model Complexity and Bias-Variance Tradeoff
• Training, Validation, and Test Data Splitting
• Classification Problem Handling

Unit 4: Probabilistic Classification

• Basics of Probability in ML
• Bayes Optimal Classifier
• Naive Bayes Algorithm
• Gaussian Distribution in Classification

Unit 5: Linear Classifiers

• Bayes Rule Recap
• Naive Bayes Model for Classification
• Logistic Regression
• Online Gradient Descent Algorithm

Unit 6: Advanced Models & Algorithms

• Radial Basis Function Networks (RBFN)
• Support Vector Machines (SVM) – Concepts & Kernels
• Introduction to Genetic Algorithms

Unit 7: Ensemble Learning

• Bagging: Bootstrap Aggregation
• Random Forest Algorithm
• Boosting Methods (AdaBoost, Gradient Boosting)

Unit 8: Unsupervised Learning

• Clustering Techniques (K-Means, DBSCAN)
• Hierarchical Clustering and Agglomeration
• Latent Space Representation & Dimensionality Reduction

Unit 9: Structural ML Concepts

• VC-Dimension and Hypothesis Space
• Structural Risk Minimization Principle
• Margin Maximization
• Advanced Support Vector Machine (SVM) Applications

Course Highlights

• Instructor-led Interactive Classes
• Hands-on Implementation using Python
• Case Studies and Real-life Projects
• Certificate on Completion

Eligibility :- 12 PASS