Course Description
‘AI with Python: Comprehensive Machine Learning and Deep Learning’ course is a comprehensive program designed to provide you with a deep understanding of artificial intelligence (AI) concepts, focusing on machine learning and deep learning, using the Python programming language. This course covers the fundamentals as well as advanced topics, allowing you to develop a strong foundation in AI and gain practical skills to apply in real-world scenarios.
Course Duration
30 Hours
Course Requirements
Basic knowledge of Python programming
Understanding of programming concepts (variables, loops, conditionals, functions)
Familiarity with mathematics (linear algebra, calculus) is beneficial but not mandatory
Course Benefits
Gain a comprehensive understanding of AI, machine learning, and deep learning concepts
Master the implementation of machine learning and deep learning algorithms using Python
Learn best practices for model evaluation, optimization, and deployment
Develop practical skills through hands-on projects and exercises
Stay updated with emerging trends and advancements in AI
Course Outline
Part 1: Introduction to Artificial Intelligence and Python Basics (2 hours)
Introduction to AI and its applications
Setting up the Python environment for AI development
Python programming essentials for AI
Part 2: Machine Learning Fundamentals (8 hours)
Introduction to machine learning
Supervised learning techniques (linear regression, logistic regression, support vector machines)
Unsupervised learning techniques (clustering, dimensionality reduction)
Evaluation and validation of machine learning models
Feature engineering and selection
Handling imbalanced datasets
Introduction to model optimization and hyperparameter tuning
Part 3: Deep Learning Fundamentals (8 hours)
Introduction to neural networks and deep learning
Deep learning frameworks (Keras, TensorFlow, PyTorch)
Multilayer perceptrons and activation functions
Convolutional neural networks (CNNs) for image recognition
Recurrent neural networks (RNNs) for sequence data
Generative models (GANs, VAEs)
Transfer learning and fine-tuning
Ethics and considerations in deep learning
Part 4: Advanced Topics in AI (8 hours)
Natural language processing (NLP) and text classification
Recommendation systems
Reinforcement learning
Time series analysis and forecasting
Deploying AI models to production
Explainable AI and model interpretability
Handling big data and distributed computing in AI
Emerging trends and future directions in AI
Part 5: Capstone Project (4 hours)
Apply the knowledge gained throughout the course to a hands-on project
Develop an end-to-end AI solution using machine learning and/or deep learning techniques
Showcase your skills and understanding of AI concepts