Description

TABLE OF CONTENT

Unit 1: Introduction to AI & Data Analytics
Overview of Artificial Intelligence (AI)
Role of Data Analytics in Decision Making
Importance of AI & Data Analytics in Various Industries

Unit 2: Foundations of Data
Understanding Data Types and Structures
Data Collection and Cleaning
Exploratory Data Analysis (EDA)

Unit 3: Basics of Statistics
Descriptive Statistics
Inferential Statistics
Probability Distributions

Unit 4: Machine Learning Overview
Introduction to Machine Learning
Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
Applications of Machine Learning in Real-world Scenarios

Unit 5: Data Preprocessing
Feature Scaling and Normalization
Handling Missing Data
Encoding Categorical Data

Unit 6: Model Building
Selecting and Evaluating Models
Model Training and Testing
Model Deployment

Unit 7: Introduction to Deep Learning
Neural Networks and Deep Learning
Deep Learning Applications
Overview of TensorFlow and PyTorch

Unit 8: Data Visualization
Importance of Data Visualization
Tools for Data Visualization (e.g., Matplotlib, Seaborn, Tableau)

Unit 9: Big Data Analytics
Introduction to Big Data
Hadoop and MapReduce
Apache Spark for Big Data Processing

Unit 10: AI Ethics and Bias
Ethical Considerations in AI & Data Analytics
Addressing Bias in Data and Algorithms