Advanced AI Industrial Training Program

Description

Advanced AI refers to the advanced techniques and applications of artificial intelligence (AI) that go beyond the fundamentals. It involves utilizing complex algorithms, deep learning architectures, and cutting-edge technologies to solve challenging problems and achieve high levels of performance in AI systems. Advanced AI techniques include deep learning, natural language processing, computer vision, reinforcement learning, and generative models. It finds applications in various fields, such as healthcare, finance, robotics, and autonomous systems. The Advanced AI Concepts and Techniques course provides students with a comprehensive understanding of advanced AI methodologies and their applications. Through hands-on projects and case studies, students will gain practical experience in applying advanced AI techniques to real-world problems.

What you'll learn

Over 72 Lectures And 144 Hours of Content!

Theoretical And Practical Training Included Real-Time Projects.

Best Suitable for Beginners to Advanced Level Users and Who Learn Faster When Demonstrated.

Course Content Is Designed by Considering Current Trending Technologies and The Job Market.

Practical Assignments at The End of Every Session

Practical Real-Time Experience with Live Projects for Every End of The Topic.

Unlimited Resources, With Overall Six Courses LMS

Requirements

No Prerequisites required

Course Content

Statistics Essentials for Data Science

Introduction to Calculus

Understanding the Data

Descriptive Statistics

Data Visualization

Probability

Probability Distributions

Sampling and Sampling Techniques

Inferential Statistics

Application of Statistics in Business

Programming Refresher

Fundamentals of Programming

Introduction to Python

Python Data Types and Operators

Conditional Statements and Loops in Python

Python Functions

Object-Oriented Programming Concepts with Python

Threading

Python for Data Science

Python Data Structures

Python Programming Fundamentals

Working with Data in Python

Working with NumPy Array

Applied Data Science with Python

Scientific Computing with Python (SciPy)

Data Manipulation with Pandas

Data Visualization in Python using Matplotlib

Data Analysis

Data Wrangling

End-to-End Statistics Applications in Python

Introduction to Artificial Intelligence & Machine Learning

Data Preprocessing

Supervised Learning

Feature Engineering

Supervised Learning-Classification

Unsupervised Learning

Time Series Modelling

Ensemble Learning

Recommender System

Deep Learning with TensorFlow

Introduction to AI and Deep Learning

Artificial Neural Network

Deep Neural Network and Tools

Optimization, Tuning, and Interpretability of Deep Neural Networks

Convolutional Neural Networks (CNN)

Recurrent Neural Networks

Autoencoder

Deep Learning Specialization

Deep Neural Networks

TensorFlow

Model Optimization and Performance Improvement

Transfer Learning

Object Detection

Transformer Models for Natural Language Processing (NLP)

Getting Started with Autoencoders

PyTorch

Essentials of Generative AI, Prompt Engineering & ChatGPT

Explainable AI

Prompt Engineering

ChatGPT

Fine-tuning

Ethical Considerations in Generative AI Models & ChatGPT

The Future of Generative AI

Deploying and Scaling ChatGPT

Security and Privacy Considerations

Monitoring and Debugging ChatGPT

Maintaining ChatGPT

Projects

Movie Recommendation Algorithm

Build a Movie Recommendation Algorithm to suggest films based on user preferences and viewing history, using collaborative filtering and content-based techniques to enhance personalized viewing experiences.

Breast Cancer Classification

Develop a Breast Cancer Classification model to accurately diagnose cancer types using patient data and machine learning algorithms, aiming to improve early detection and treatment outcomes.

Sentiment Analysis of IMDb Movie Reviews

Perform Sentiment Analysis on IMDb movie reviews to gauge public opinion, utilizing natural language processing techniques to classify reviews as positive, negative, or neutral.

Diabetes Classification

Create a Diabetes Classification model to predict the likelihood of diabetes based on patient data, using machine learning algorithms for accurate and actionable health insights.

Banknote Authentication

Develop a Banknote Authentication system to verify the authenticity of currency using image processing and machine learning techniques for accurate detection of counterfeit notes.

Face Recognition in Supervised Learning

Implement a Face Recognition system using supervised learning techniques to accurately identify and verify individuals based on facial features, leveraging labeled training data for model accuracy.

Credit Card Fraud Detection

Develop a Credit Card Fraud Detection system to identify and prevent fraudulent transactions using advanced algorithms and machine learning techniques for enhanced security.