Artificial Intelligence Course Syllabus
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Our artificial intelligence syllabus provides students a thorough introduction to the core ideas of artificial intelligence (AI), giving them the information and abilities they need to comprehend, create, and use AI solutions. Students will investigate the fundamental ideas of artificial intelligence (AI), such as search algorithms, knowledge representation, machine learning, natural language processing (NLP), computer vision, and robotics, through a combination of theoretical and hands-on learning with our AI course syllabus.
Course Syllabus
Download SyllabusModule 1: Introduction to Artificial Intelligence
- What is Artificial Intelligence?
- Key AI applications in various industries
- AI vs Machine Learning vs Deep Learning
- History and evolution of AI
- AI tools and frameworks overview
Module 2: Python for AI
- Introduction to Python programming
- Essential Python libraries: NumPy, Pandas, Matplotlib, Scikit-Learn
- Data preprocessing techniques for AI
- Working with datasets for machine learning
Module 3: Machine Learning Fundamentals
- Introduction to Machine Learning (ML)
- Supervised vs Unsupervised Learning
- Regression models (Linear, Logistic)
- Classification models (Decision Trees, Random Forest, SVM)
- Model training, testing, and evaluation
Module 4: Deep Learning & Neural Networks
- Understanding artificial neural networks (ANNs)
- Activation functions and backpropagation
- Convolutional Neural Networks (CNNs) for image recognition
- Recurrent Neural Networks (RNNs) for sequential data
- Implementing deep learning models using TensorFlow and Keras
Module 5: Natural Language Processing (NLP)
- Basics of NLP and text processing
- Tokenization, stemming, and lemmatization
- Sentiment analysis and chatbots
- Word embeddings and transformer models (BERT, GPT)
Module 6: Computer Vision
- Image processing techniques with OpenCV
- Object detection and facial recognition
- Image classification using CNNs
- Applications of computer vision in industries
Module 7: AI Model Deployment & Cloud Integration
- Optimizing AI models for deployment
- Deploying AI models using Flask and FastAPI
- Cloud-based AI deployment (AWS, Google Cloud, Azure)
- AI integration with mobile and web applications
Module 8: Ethics in AI & Future Trends
- Understanding AI biases and fairness
- Ethical concerns in AI decision-making
- Future advancements and career opportunities in AI
Explore our artificial intelligence course subjects as it will equip students with a solid foundation in artificial intelligence, preparing them for future advancements in this exciting and rapidly growing industry.
