Faysal AzizProgramming in python for Artificial Intelligence
3-day highly intensive crash course
Why this course
This is a highly intensive 3-day program tailored for individuals who have a strong foundation in Python and have completed courses on "Python for Data Science" and "Machine Learning with Python." This course is designed to fast-track your understanding and skills in Artificial Intelligence (AI) with a focus on deep learning, neural networks, and the application of these concepts using Python. By integrating hands-on projects with theoretical insights, the course aims to equip participants with the ability to apply AI techniques to solve complex problems.
Learning outcomes
Upon completing this course, participants will:
- Understand the key concepts and applications of Artificial Intelligence.
- Be proficient in using TensorFlow and PyTorch for deep learning projects.
- Implement and train Convolutional Neural Networks (CNNs) for image recognition tasks.
- Understand and apply deep learning models to natural language processing (NLP) problems.
- Build and evaluate a recommendation engine using AI techniques.
- Gain insights into the latest AI trends and future directions.
Prerequisites
- Solid understanding of Python programming.
- Basic familiarity with machine learning concepts and algorithms.
- Good grasp of mathematics (linear algebra, statistics, calculus)
- An enthusiasm for learning about advanced AI techniques and their applications.
7 modules
01Quick Recap of Python for AI2 topics
- Python Libraries Overview: TensorFlow, PyTorch, Keras
- Python Advanced Features Relevant to AI (List comprehensions, Generators, Decorators)
02Foundations of Deep Learning4 topics
- Introduction to Deep Learning and Neural Networks
- Understanding Neural Network Architectures
- Activation Functions, Loss Functions, and Optimizers
- Backpropagation and Gradient Descent
03Deep Learning Frameworks2 topics
- TensorFlow and Keras
- Setting Up TensorFlow
- Building and Training Models with Keras
- TensorFlow 2.x Features and Ecosystem
- PyTorch
- Setting Up PyTorch
- Tensors in PyTorch
- Building and Training Models in PyTorch
- Autograd Mechanism
04Convolutional Neural Networks (CNN) for Image Processing4 topics
- Introduction to CNNs and Their Architecture
- Implementing CNNs with TensorFlow and Keras
- Implementing CNNs with PyTorch
- Applications of CNNs (Image Classification, Object Detection)
05Natural Language Processing (NLP) with Deep Learning4 topics
- NLP Basics and Text Preprocessing
- Word Embeddings and Word2Vec
- Recurrent Neural Networks (RNN) and LSTM for NLP
- Implementing NLP Projects with TensorFlow and PyTorch
06Building a Recommendation Engine4 topics
- Overview of Recommendation Systems
- Collaborative Filtering Techniques
- Content-Based Filtering Techniques
- Implementing a Recommendation System
07Current Trends and Future of AI3 topics
- Overview of Advanced Topics in AI (Generative Adversarial Networks, Reinforcement Learning)
- Ethical Considerations and Challenges in AI
- Future Directions in AI Research and Applications
This crash course is a condensed exploration into the realm of AI, blending practical skills in TensorFlow and PyTorch with a solid theoretical foundation. Designed for rapid learning, it moves from a quick Python recap to advanced AI applications, ensuring you leave with the ability to tackle real-world AI challenges.
A programme built around your team.
Share your training goals and requirements.