AI MACHINE LEARNING
AI & Machine Learning Tutorial roadmap
Introduction to AI & Machine Learning What is Artificial Intelligence (AI)? Artificial Intelligence refers to the simulation of human intelligence in machines that are designed to think, learn, re
AI & Machine Learning
Definition of AI and Machine Learning (ML) Artificial Intelligence (AI): Machine Learning (ML): Types of AI Narrow AI (Weak AI): General AI (Strong AI): Superintelligent AI: Types of Machine Learning
Mathematical Foundations
Linear Algebra: Vectors, Matrices, and Tensors Vectors: Definition: A vector is an ordered list of numbers (scalars) that represent a point in space or a direction. Vectors can have different dim
Data Collection and Preprocessing
Data Types and Sources 1. Data Types: 2. Data Sources: Tensors: Data Cleaning: Handling Missing Values, Outliers 1. Handling Missing Values: 2. Handling Outliers: Feature Engineering: Scaling, Encodin
Supervised Learning
Overview of Supervised Learning Supervised learning is a type of machine learning where the model is trained on labeled data. The goal is to learn a mapping from input features (independent varia
Unsupervised Learning
Clustering algorithms: k-means, hierarchical clustering, DBSCAN 1. k-Means Clustering: 2. Hierarchical Clustering: 3. DBSCAN (Density-Based Spatial Clustering of Applications with Noise): Dimensionali
Basics of Natural Language Processing (NLP)
1. Tokenization: 2. Stemming: 3. Lemmatization: Text Representation Techniques 1. Bag of Words (BoW): 2. TF-IDF (Term Frequency-Inverse Document Frequency): 3. Word Embeddings: NLP Models 1. Recurrent
Fundamentals of Reinforcement Learning (RL)
What is Reinforcement Learning? Key Concepts in Reinforcement Learning 1. Agents: 2. Environments: 3. Rewards: 4. Policies: 5. Value Functions: Q-Learning and Deep Q-Networks (DQN) 1. Q-Learning: 2. D
Neural Networks and Deep Learning
Introduction Neural Networks and Deep Learning are core areas of Artificial Intelligence (AI) and Machine Learning (ML) that focus on building systems capable of learning patterns from data, similar t
AI and ML in Practice
Model Selection and Hyperparameter Tuning 1. Model Selection: 2. Hyperparameter Tuning: Cross-Validation and Model Evaluation Techniques Cross-Validation: 2. Model Evaluation Techniques: Deployment of
Ethics and Bias in AI and Machine Learning
As artificial intelligence (AI) and machine learning (ML) systems increasingly influence real-world decisions, ethical considerations and bias mitigation have become critical. This article explores di
Risk Management and Compliance in AI/ML
Introduction Risk Management and Compliance in Artificial Intelligence (AI) and Machine Learning (ML) focus on identifying, assessing, mitigating, and monitoring risks arising from the design, develop
Advanced Topics in AI/ML
Explainable AI and Interpretability Explainable AI (XAI): Interpretability: Techniques: Federated Learning and Privacy-Preserving ML Federated Learning: Privacy-Preserving ML: AI-Driven Automation and