Course Content
Foundation of AI & ML
- Introduction to Data Science, AI&ML
- R- Programming Essentials
- Statistical Analysis
Python for AI
- Python Essentials
- Python Environment Setup
- Python Data Types
- Python Looping and Control Statements
- Object-Oriented Programming Concepts
- Database Connection
Python Libraries for AI
- NumPy
- Scipy
- Pandas
- MatPlot
Data Management
- Data Acquisition
- Data Pre-processing and Preparation
- Data Transformation and Quality
- Handling Text Data
- Big Data Fundamentals
- Big Data Frameworks (Spark, Hadoop, NoSQL)
SAS-Data Analytics
- SAS Introduction
- SAS Functions
- SAS Operators
- SAS Procedures
- SAS Graphs
- SAS Macros
- SAS Format
Statistical Decision Making
- Data Visualisation
- Sampling and Estimation
- Inferential Statistics
Predictive Analytics
- Linear Regression
- Multiple Linear Regression
- Non-Linear Regression
- Forecasting Models
Machine Learning
- ML Foundations
- Clustering
- Classification (Naive Bayes Classifier, K-Nearest Neighbors)
- Association Rule Mining
Artificial Intelligence
- Foundations of AI
- Convolution Neural Networks
- Recurrent Neural Networks
Deep Learning with Keras and TensorFlow
- Deep Learning Libraries
- Keras API
- TensorFlow
- Deep Learning Algorithms
Advanced Deep Learning and Computer Vision
- Distributed and Parallel Computing
- Deploying Deep Learning Models
- Reinforcement Learning
- Generating Images with Neural Style
- Object Detection through Convolutional Neural Networks
Cloud Computing and AWS
- Introduction to Cloud Computing and AWS
- Storage Volumes and Elastic Compute
- Virtual Private Cloud
- Simple Storage Services
- AWS Lambda and Amazon Machine Learning
Tableau 10
- Introduction to Data Visualisation
- Tableau Architecture
- Working with Data Blending
- Creation of Sets
- Calculations, Expression, and Parameters.
- Dashboards, Stories, and Filters
- Tableau Prep