Artificial Intelligence (AI) and Machine Learning (ML)Program

Master Artificial Intelligence and Machine Learning program, covering key AI concepts, machine learning algorithms, deep learning, NLP, and computer vision. Gain hands-on experience through practical exercises and case studies.

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Application Deadline: 6th October 2024

Want to upskill?

Enroll today in our Artificial Intelligence (AI) and Machine Learning (ML)Program to elevate and expand your expertise.

Program Fee

Duration

0 Week(s)

Study Mode

Virtual classes

Introduction

The comprehensive AI & Machine Learning program at Indepth Research Institute (IRES) has been designed to equip participants with the fundamental knowledge and practical skills necessary to thrive in the rapidly evolving field of artificial intelligence and machine learning. The program covers a wide range of topics, from the fundamentals of AI to advanced machine learning algorithms and neural networks. Participants will gain hands-on experience through practical exercises and case studies that demonstrate real-world applications of AI and ML in various industries.

Application Process

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Program Prerequisites

Basic knowledge of programming (Python preferred).
Familiarity with linear algebra, calculus, and statistics.
Experience with data analysis is recommended but not mandatory.

Program Modules

What is Artificial Intelligence?
History and Evolution of AI
Key AI Concepts and Terminologies
Case Study: Overview of AI applications in real-world industries.
Differences Between AI, Machine Learning, and Deep Learning
Common Use Cases of AI and ML
Current Trends and Future Directions in AI
Hands-on Exercise: Identifying AI use cases in a given industry.

Definition and Types of Machine Learning
Supervised vs. Unsupervised Learning
Key Components of a Machine Learning System
Case Study: Real-world application of supervised and unsupervised learning.
Data Collection and Preprocessing
Model Training and Evaluation
Deployment of Machine Learning Models
Hands-on Exercise: Build a simple ML model and evaluate its performance.

Data Cleaning and Transformation
Handling Missing Data and Outliers
Scaling and Normalization Techniques
Hands-on Exercise: Preprocess a dataset and prepare it for ML modeling.
Importance of Feature Engineering
Feature Selection and Dimensionality Reduction Techniques
Feature Engineering for Specific Algorithms
Hands-on Exercise: Engineer features for a given dataset.

Linear Regression
Polynomial Regression
Ridge and Lasso Regression
Hands-on Exercise: Build and evaluate a regression model.
Logistic Regression
Decision Trees and Random Forests
Support Vector Machines (SVM)
Hands-on Exercise: Implement a classification model for a given dataset.

K-Means Clustering
Hierarchical Clustering
DBSCAN (Density-Based Clustering)
Hands-on Exercise: Apply clustering techniques to a real dataset.
Principal Component Analysis (PCA)
t-SNE and UMAP
Feature Selection Techniques
Hands-on Exercise: Perform dimensionality reduction on a high-dimensional dataset.

Basic Architecture of Neural Networks
Activation Functions and Backpropagation
Training Deep Learning Models
Hands-on Exercise: Build a simple neural network using TensorFlow/Keras.
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Transfer Learning
Case Study: Application of CNNs and RNNs in image and text processing.

Text Preprocessing Techniques
Bag of Words and TF-IDF
Sentiment Analysis
Hands-on Exercise: Perform sentiment analysis on text data.
Word Embeddings (Word2Vec, GloVe)
Sequence Models (LSTMs and GRUs)
Transformer Models (BERT, GPT)
Case Study: Application of transformers in NLP tasks like text classification.

Image Preprocessing and Augmentation
Edge Detection and Filtering Techniques
Object Detection and Recognition
Hands-on Exercise: Build an image classifier using CNNs.
Transfer Learning for Computer Vision
Generative Adversarial Networks (GANs)
Image Segmentation Techniques
Case Study: Application of GANs in generating synthetic images.

Basic Concepts of Reinforcement Learning
Markov Decision Processes (MDPs)
Q-Learning and Deep Q-Networks (DQNs)
Hands-on Exercise: Implement a basic reinforcement learning algorithm.
Policy Gradients
Actor-Critic Methods
Applications of Reinforcement Learning in Robotics and Game AI
Case Study: Reinforcement learning in gaming and autonomous systems.

Fairness and Bias in AI Models
Transparency and Explainability in AI
Privacy Concerns in AI
Case Study: Ethical dilemmas in AI applications and how they were addressed.
Legal Frameworks for AI
Ensuring Accountability in AI Systems
Ethical AI Guidelines and Standards
Hands-on Exercise: Develop an ethical AI framework for a hypothetical project.

AI for Medical Diagnosis and Treatment
AI in Drug Discovery
AI for Personalized Medicine
Case Study: AI applications in improving healthcare outcomes.
AI for Fraud Detection
AI in Customer Behavior Analysis
AI in Supply Chain Optimization
Case Study: Analyze the impact of AI on a financial services company's operations.