AI Training & Certification

Topics :

What is an Application?

Overview of applications and their significance.

Types of Applications

Classification and examples of various application types.

Fundamentals of Web Applications

Basic concepts and components of web applications.

Web Application Architecture

Structure and design patterns in web application architecture.

Web Technologies used in Projects

Key technologies and frameworks used in web application development.

Topics :

Introduction to Software Development Life Cycle

The phases, importance, and overview of SDLC.

Application Lifecycle Management - ALM

Tools, processes, and overview of ALM.

SDLC Methodologies

Examination of different methodologies used in software development.

DevOps Process

Understanding the principles, practices, and benefits of DevOps.

Topics

1. Introduction To Agile & Scrum

Fundamental overview of Agile methodologies and the Scrum framework.

2. The Principles of Agile Methodology

Core principles of Agile focusing on iterative development and customer collaboration.

3. Scrum Framework: Roles, Artifacts, and Events

Key components of Scrum, including its roles, artifacts, and structured events.

4. Implementing Agile with Scrum

Strategies for applying Agile and Scrum practices in software development projects.

5. Agile Project Management Best Practices

Essential practices for leading Agile projects, emphasizing communication and continuous improvement.

Topics Covered:

Python Syntax and Basic Constructs

Control Flow and Functions

Object-Oriented Programming in Python

NumPy for Numerical Data

Pandas for Data Cleaning and Preparation

Data Visualization with Matplotlib and Seaborn

Working with APIs and Web Data

Introduction to Web Scraping

File Handling and Data Persistence

Advanced Data Structures

Python Decorators, Generators, and Context Managers

Multithreading and Multiprocessing for Performance Optimization

TensorFlow Basics

Keras for Deep Learning Models

PyTorch Introduction

Vectors, Matrices, and Linear Transformations

Eigenvalues and Eigenvectors

Application in AI and ML

Differential Calculus and Gradients

Optimization Algorithms

Application in Neural Network Training

Basics of Probability

Probability Distributions

Bayesian Thinking in AI

Descriptive Statistics and Inferential Statistics

Hypothesis Testing and Confidence Intervals

Correlation vs. Causation

Discrete Mathematics Concepts

Graph Theory and Network Models

Continuous Optimization and Constraint Satisfaction

Types of Machine Learning: Supervised, Unsupervised, and Reinforcement

Bias-Variance Tradeoff

Evaluating Machine Learning Models

Classification Algorithms

Regression Analysis

Ensemble Learning Methods

K-means and Hierarchical Clustering

Dimensionality Reduction Techniques: PCA, t-SNE

Association Rule Learning

Time Series Data and Its Components

ARIMA and Seasonal ARIMA

Using Machine Learning for Time Series Prediction

Principles of Reinforcement Learning

Markov Decision Processes

Implementing Q-Learning and Deep Q-Networks (DQN)

Understanding Deep Learning and Neural Networks

Activation Functions, Loss Functions, and Optimization Techniques

Building Your First Neural Network

Introduction to CNNs and Their Architecture

Implementing CNNs for Image Recognition and Classification

Advanced CNN Techniques for Computer Vision

Understanding RNNs and Their Applications

Long Short-Term Memory Networks (LSTMs) for Sequential Data Processing

Use Cases: Text Generation, Time Series Forecasting

Topics

Fundamentals of GANs and Their Architecture

Building Simple Generative Models with GANs

Exploring Autoencoders for Data Compression and Generation

Basics of Attention Mechanisms and Transformer Architecture

Implementing Transformer Models for NLP Tasks

Understanding BERT, GPT, and Other Variants

Text Preprocessing and Feature Extraction Techniques

Sentiment Analysis, Named Entity Recognition (NER), and Text Summarization

Chatbots and Language Models

Object Detection and Image Segmentation Techniques

Facial Recognition Systems

Autonomous Vehicles and Drone Technology

Deep Reinforcement Learning for Game Playing

Applying RL in Robotics and Autonomous Systems

RL in Finance and Healthcare

Ensuring Fairness and Transparency in AI Models

Data Privacy and Security in AI Applications

AI Governance and Regulatory Compliance

Quantum Computing for AI

Edge AI and Its Applications

The Future of AI: Trends and Predictions

Overview of AWS, Google Cloud, and Azure for AI

Leveraging Cloud AI Services for Model Training and Deployment

Big Data Technologies and AI: Integrating Apache Spark and Hadoop

Introduction to MLOps and Its Importance

Continuous Integration and Continuous Deployment (CI/CD) for AI

Monitoring and Managing AI Models in Production

Techniques for Model Deployment: API, Docker Containers, and Microservices

Scalability and Performance Optimization

User Interfaces for AI Applications

Security Best Practices in AI Deployment

Techniques for Monitoring AI Systems for Anomalies and Performance Issues

Automated Remediation and Alerting Strategies

Emerging Tools and Platforms for AI Deployment

Ethics and Responsible AI in Deployment

Preparing for Future Technological Advances in AI