Learn AI

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.

Module 1: Introduction to Artificial Intelligence (Week 1-2)

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:

  • Install Python & AI libraries (NumPy, Pandas, Matplotlib)
  • Explore basic AI applications like chatbots & recommendation systems

Module 2: Mathematics for AI (Week 3-4)

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:

  • Implement Linear Algebra operations using Python (NumPy)
  • Visualize probability distributions using Matplotlib & Seaborn

Module 3: Machine Learning Fundamentals (Week 5-6)

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:

  • Train a Linear Regression Model using scikit-learn
  • Implement K-Means Clustering on real-world datasets

Module 4: Deep Learning & Neural Networks (Week 7-8)

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:

  • Build a Neural Network using TensorFlow/Keras
  • Implement an Image Classifier using CNN

Module 5: Natural Language Processing (NLP) (Week 9-10)

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:

  • Perform Text Sentiment Analysis using NLP libraries (spaCy, NLTK)
  • Build a Chatbot using OpenAI’s GPT API

Module 6: AI Applications & Advanced Topics (Week 11-12)

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:

  • Implement AI for stock price prediction
  • Train a GAN to generate synthetic images

Final Project & Certification (Week 13-16)

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

Tools & Libraries Used in This Course:

  • Programming Language: Python
  • Libraries: NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Keras
  • NLP Libraries: NLTK, spaCy, Hugging Face Transformers
  • Visualization Tools: Matplotlib, Seaborn

This AI course provides structured learning, hands-on projects, and real-world applications. Learn AI from scratch online in just 3 months (12 weeks).