Getting Started with AI Development
Why It’s Worth It
Dive into AI development at your own pace, combining flexibility with structured learning.
Receive instant support as you experiment with tools like TensorFlow, PyTorch, and scikit-learn.
Build a solid foundation in AI fundamentals, preparing you for advanced studies and career opportunities.
Your Learning Roadmap
Introduction to AI and Machine Learning
This module introduces key concepts of artificial intelligence and machine learning. Participants will learn what AI is, explore the history and evolution of machine learning, and gain an overview of popular frameworks such as TensorFlow, PyTorch, and scikit-learn. It sets the stage for deeper exploration of each framework in subsequent modules. Foundations of AI and ML Historical Evolution of AI Frameworks Overview of TensorFlow, PyTorch, and scikit-learn
Setting Up Your Development Environment
This module guides participants through the process of setting up a local development environment for AI. The focus is on installing necessary software such as Python, TensorFlow, PyTorch, and scikit-learn along with integrated development tools and notebooks for experimentation. Installing Python and Basic Libraries Setting Up TensorFlow, PyTorch, and scikit-learn Introduction to IDEs and Jupyter Notebooks
Introduction to TensorFlow
This module offers an in-depth look at TensorFlow, focusing on its architecture, data flow concepts, and the creation of simple models. Participants will learn about tensors, computational graphs, and basic operations to develop initial AI experiments using TensorFlow. TensorFlow Basics and Core Concepts Building a Simple TensorFlow Model Understanding Data Flow in TensorFlow
Introduction to PyTorch
This module introduces PyTorch, emphasizing its dynamic computation graph and ease of use. Participants will learn the fundamentals of tensors and neural network construction in PyTorch through hands-on coding examples, further solidifying their AI development skills. PyTorch Fundamentals and Tensors Constructing a Basic Neural Network Training and Evaluating PyTorch Models
Introduction to scikit-learn
This module focuses on scikit-learn, a robust library for machine learning in Python. Participants will explore data preprocessing, model creation, and evaluation techniques, building an understanding of how to apply classical machine learning methods effectively. Data Preprocessing with scikit-learn Building Simple Machine Learning Models Model Evaluation and Tuning
Experimenting with AI Models and Practical Projects
In this final module, participants apply their knowledge by experimenting with AI models built using TensorFlow, PyTorch, and scikit-learn. This project-based module encourages integration, comparison, and critical evaluation of models, preparing learners for advanced topics in AI development. Integrating Models from Different Frameworks Debugging and Model Optimization Hands-on Mini Project and Next Steps
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Enroll Now to Start Learning!
Interactive learning with real-time Q&A
Hands-on mini projects for practical application
Guided by an AI assistant for instant feedback
Modular lessons for flexible learning pace
Focus on both theory and practical skills
Exposure to multiple AI frameworks and models