Learn Artificial Intelligence (AI) from scratch. Our Free AI course covers topics including Machine Learning, Deep Learning, NLP, and AI applications. Master Python, Neural Networks, and AI model deployment with hands-on projects.
Topics Covered:
1- What is AI? History & Evolution
2- Types of AI: Narrow AI, General AI, Super AI
3- AI vs. Machine Learning vs. Deep Learning
4- Applications of AI in Real-World Industries
5- Ethical Considerations in AI
6- Hands-on Practice:
Topics Covered:
1- Linear Algebra: Vectors, Matrices, Eigenvalues
2- Probability & Statistics: Bayes Theorem, Probability Distributions
3- Calculus for AI: Derivatives, Gradients
4- Optimization Techniques: Gradient Descent, Loss Functions
5- Hands-on Practice:
Topics Covered:
1- Supervised vs. Unsupervised Learning
2- Regression & Classification Algorithms
3- Decision Trees, Random Forest, Naïve Bayes
4- Clustering Techniques: K-Means, DBSCAN
5- Feature Engineering & Data Preprocessing
6- Hands-on Practice:
Topics Covered:
1- Introduction to Neural Networks & Perceptrons
2- Activation Functions: ReLU, Sigmoid, Softmax
3- Backpropagation & Gradient Descent
4- Convolutional Neural Networks (CNNs)
5- Recurrent Neural Networks (RNNs) & Long Short-Term Memory (LSTMs)
6- Hands-on Practice:
Topics Covered:
1- Basics of NLP: Tokenization, Stemming, Lemmatization
2- Word Embeddings: Word2Vec, GloVe, Transformers
3- Named Entity Recognition (NER)
4- Sentiment Analysis & Chatbot Development
5- Hands-on Practice:
Topics Covered:
1- Reinforcement Learning: Q-Learning, Deep Q Networks
2- AI in Robotics & Autonomous Systems
3- Generative AI: GANs (Generative Adversarial Networks)
4- Ethical AI & Bias in Machine Learning
5- Hands-on Practice:
1- Develop a real-world AI project (e.g., AI assistant, self-learning chatbot, object detection system)
2- Deploy your AI model using Flask, FastAPI, or Streamlit
3- Present the project & get certification upon completion
This AI course provides structured learning, hands-on projects, and real-world applications. Learn AI from scratch online in just 3 months (12 weeks).
Reinforcement Learning: Q-Learning, Deep Q Networks Reinforcement Learning (RL) is a trial-and-error learning approach where an agent interacts with an environment to maximize cumulative rewards over time. 1. What is Q-Learning? Q-Learning is a model-free, off-policy RL algorithm used to learn the best actions to take in a given state. It uses a Q-table (a … Read more
Basics of NLP: Tokenization, Stemming, Lemmatization Natural Language Processing (NLP) helps computers understand and process human language. Tokenization, Stemming, and Lemmatization are essential text preprocessing techniques in NLP. 1. Tokenization: Splitting Text into Words or Sentences What is Tokenization? Tokenization is the process of splitting text into smaller units (tokens) like words or sentences. Example … Read more
Introduction to Neural Networks & Perceptrons Neural Networks are the foundation of modern AI and Deep Learning. They mimic the way the human brain processes information. Let’s break it down step by step. 1. What is Neural Networks A Neural Network is a computational model made of neurons (nodes) arranged in layers that learn patterns … Read more
Supervised vs Unsupervised Learning Machine Learning is broadly categorized into Supervised and Unsupervised Learning. Let’s break them down. Supervised Learning Definition: The model learns from labeled data (input-output pairs). Goal: Map inputs to outputs by minimizing error. Example: Given house features (size, location, etc.), predict price. Types of Supervised Learning 1- Regression: Predict continuous values. … Read more
1- Linear Algebra: Vectors, Matrices, Eigenvalues Linear algebra is essential for understanding the mathematics behind artificial intelligence (AI). Concepts like vectors, matrices, and eigenvalues play a fundamental role in machine learning, computer vision, natural language processing (NLP), and other AI domains. Let’s break down each concept about AI: Vectors A vector is essentially an ordered … Read more
What is AI? Artificial Intelligence (AI) is the branch of computer science that enables machines to mimic human intelligence. AI systems can learn, reason, problem-solve, perceive, and make decisions to perform complex tasks without direct human intervention. AI is widely used in chatbots, self-driving cars, recommendation systems, healthcare diagnostics, finance, and robotics. History & Evolution … Read more