Positive Quadrant Technologies LLP

Module 1: What is machine learning?

Module 2: Applications of Machine Learning

Module 3: Key Elements of Machine Learning

Module 4: Types of Learning

Module 5: Machine Learning in Practice

Module 6: AI ML Basics

Module 7: Python Basic

Module 8: NumPy & 2D Plotting Library

Module 9: Python Pandas & Data Analysis

Module 10: Accessing Data from & multiple sources

Module 11: Data Preparation & Cleaning

Module 12: Data Access & Databases

Module 13: Data Visualization

Module 14: Data Analysis

Module 15: Real-World Modelling & Problem Solving

Module 16: Python Data Science

Module 17: Python Forecasting Modelling in Data Science

AI & ML Technology Basics and Advanced

Rs.3000

Natural Language Processing (NLP)
Machine Learning: An integral part of AI.
AI in Finance: Applied for fraud detection.
Speech Recognition: Enables machines to understand and interpret human speech.
Expert Systems: Utilizes knowledge bases and rule-based systems to mimic human expertise in specific domains.
Supervised Learning: Trains models on labeled data to make predictions or classifications based on input features.
Reinforcement Learning: Utilizes a reward-based system for machines to learn and make decisions by trial and error.
Clustering: Groups similar data points together based on patterns and similarities.
Regression Analysis: Predicts numerical outcomes based on historical data and relationships between variables.
Transfer Learning: Applies knowledge gained from one task to improve learning and performance on a different but related task.
Robotics: Integrates AI to control and enhance the capabilities of robots.