Artificial Neural Networks
Overview ANN Architectures ANN for Classification ANN for Regression
Unsupervised Machine Learning
Clustering Techniques K-Means Clustering Hierarchical Clustering Principal Component Analysis Anomaly Detection
Boosting Techniques
Boosting Overview Gradient Boosting AdaBoost XGBoost
Supervised Machine Learning – Regression
Regression Analysis Simple Linear Regression Multiple Linear Regression Decision Tree Regression Random Forest Regression Support Vector Regression Lasso and Ridge Regression
Supervised Machine Learning – Classification
Classification Techniques Logistic Regression Decision Tree Random Forest K-Nearest Neighbours Naive Bayes Support Vector Machine
Getting Started
What is Machine Learning? Supervised and Unsupervised Learning Techniques of Supervised Learning Techniques of Unsupervised Learning
Getting Familiar
Introduction and Overviews AI and Machine Learning Need for Machine Learning Applications of Machine Learning Machine Learning Use Cases
Industry-Relevant Use Cases
Industry Use Cases of LLMs and Generative AI Industy Use Cases of Generative AI and LLMs (Video)
LLM-Powered Applications with LangChain
LLM-Powered Generative AI Applications with LangChain Hands-on: Building LLM-powered applications with LangChain LLM-Powered Applications with LangChain Intuition (Video) Building LLM-Powered GenAI Applications with LangChain Implementation (Video) Coding Practice – Classification Algorithms
How the completeness of each section is measured in the course?
There are MCQ-based short tests given after each section to assess the understanding of a learner in each section. Finally, the capstone project is evaluated manually by the instructors.