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