Artificial Intelligence Training

90 Days

★ Featured

Overview

This comprehensive Artificial Intelligence (AI) training course provides an in-depth understanding of AI concepts, tools, and applications. Learners will explore Data Science, Machine Learning, Deep Learning, Neural Networks, and TensorFlow using Python. The program covers statistical foundations, big data handling, and practical implementation of AI models. By the end, students will gain hands-on experience in developing intelligent systems and predictive models.

Who Can Attend

Students and graduates interested in AI and Data Science.
Software developers and IT professionals seeking AI specialization.
Data analysts and machine learning enthusiasts.
Researchers and academics in computer science.
Business professionals aiming to implement AI-driven solutions.

Course Content

Introduction to Artificial Intelligence and Data Science

Overview of Artificial Intelligence and its applications
Need for Data Scientists
Business Intelligence and Data Mining
Machine Learning vs Deep Learning
Analytics Project Lifecycle

Data Fundamentals and Big Data Concepts

Types and Sources of Data
Data Architecture and Quality
OLTP vs OLAP Systems
Introduction to Big Data and Hadoop Ecosystem
MapReduce and Distributed Computing

Python for Artificial Intelligence

Python Overview and Setup
Data Structures and Flow Control
Functions and OOP Concepts
NumPy and Pandas for Data Analysis
Data Visualization and Manipulation

Statistics for Data Science

Descriptive and Inferential Statistics
Sampling and Hypothesis Testing
Correlation and Regression Analysis
Chi-Square and ANOVA Tests
Central Limit Theorem Applications

Machine Learning with Python

Supervised and Unsupervised Learning
Clustering and Association Rule Mining
Decision Trees, Random Forests, and Naive Bayes
Linear and Logistic Regression
Support Vector Machines

Time Series Analysis and Predictive Modeling

Components of Time Series Data
ARIMA and Exponential Smoothing Models
Forecasting Techniques
Model Implementation and Validation
Case Studies in Time Series Forecasting

Feature Engineering and Model Optimization

Feature Selection and Preprocessing
Scaling and Normalization Techniques
Model Evaluation and Cross Validation
Ensemble Techniques: Bagging and Boosting
Automating Machine Learning with GridSearchCV

Text Mining and Natural Language Processing (NLP)

Introduction to Text Mining
Sentiment Analysis
Vector Space Models and TF-IDF
Text Classification Techniques
NLP Case Studies

Deep Learning and Neural Networks

Artificial Neural Networks Fundamentals
Activation Functions and Backpropagation
Gradient Descent Optimization
Multi-Layer Perceptron Implementation
Case Study using TensorFlow

Advanced Deep Learning Techniques

Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs) and LSTMs
Restricted Boltzmann Machines (RBMs)
Autoencoders and Their Applications
Deep Learning Case Studies

TensorFlow and Keras Frameworks

TensorFlow Basics and Installation
Computation Graph and Tensors
Building Neural Networks in TensorFlow
Convolutional and Deep Neural Networks
Transfer Learning with Keras and TFLearn

AI Project and Case Studies

Problem Identification and Data Analysis
Model Design and Implementation
Performance Evaluation and Optimization
Use Cases across Industries
Final Capstone Project