Python for AI Developers
A Beginner's Guide to Artificial Intelligence Programming
Failed to add items
Add to Cart failed.
Add to Wish List failed.
Remove from wishlist failed.
Adding to library failed
Follow podcast failed
Unfollow podcast failed
$0.99/mo for the first 3 months
Prime members: New to Audible? Get 2 free audiobooks during trial.
Python for AI Developers
Buy for $3.99
No default payment method selected.
We are sorry. We are not allowed to sell this product with the selected payment method
-
Narrated by:
-
Virtual Voice
This title uses virtual voice narration
About this listen
Chapter 1: Introduction to Python for AI
🔹 1.1 Why Python for AI Development?
🔹 1.2 Installing Python and Setting Up Your Development Environment
🔹 1.3 Introduction to Jupyter Notebooks and Google Colab
🔹 1.4 Python Basics Recap: Let’s Get Coding!
🎯 Hands-On Practice
🚀 What’s Next?
Chapter 2: Core Python Programming
🔹 2.1 Control Flow: If-Else, Loops
🔹 2.2 Functions and Modules
🔹 2.3 Object-Oriented Programming (OOP) in Python
🔹 2.4 Exception Handling
🧪 Practice Time
🚀 What’s Next?
Chapter 3: Essential Python Libraries for AI
🔹 3.1 NumPy: Handling Arrays and Matrices
🔹 3.2 Pandas: Data Analysis and DataFrames
🔹 3.3 Matplotlib & Seaborn: Data Visualization
🔹 3.4 Scikit-learn: Introduction to Machine Learning
🧪 Practice Time
🚀 What’s Next?
Chapter 4: Working with Data
🔹 4.1 Loading and Preprocessing Datasets
🔹 4.2 Handling Missing Data and Outliers
🔹 4.3 Feature Engineering and Scaling
🧪 Practice Time
📌 Quick Tips for Better Data Handling
🚀 What’s Next?
Chapter 5: Introduction to Machine Learning with Python
🔹 5.1 Supervised vs. Unsupervised Learning
🔹 5.2 Building a Simple Machine Learning Model with Scikit-learn
🔹 5.3 Evaluating Model Performance
🛠️ Other Useful Metrics
💡 Pro Tips
🧪 Practice Time
🚀 What’s Next?
Chapter 6: Deep Learning with Python
🔹 6.1 Introduction to Neural Networks
🔹 6.2 Using TensorFlow and PyTorch
🔸 6.3 Building a Simple Neural Network
🔹 6.4 Training and Evaluating Deep Learning Models
🔹 Bonus: PyTorch Version (Optional for Advanced Users)
🧪 Practice Time
📌 Quick Tips
🚀 What’s Next?
Chapter 7: Natural Language Processing (NLP) with Python
🔹 7.1 Tokenization and Text Processing
🔹 7.2 Word Embeddings and Transformers
🔹 7.3 Building an NLP Model with Hugging Face
🧪 Practice Time
📌 Quick Tips
🚀 What’s Next?
Chapter 8: Computer Vision with Python
🔹 8.1 Working with OpenCV
🔹 8.2 Image Classification with TensorFlow/Keras
🔹 8.3 Object Detection Basics
🧪 Practice Time
📌 Quick Tips
🚀 What’s Next?
Chapter 9: AI Model Deployment
🔹 9.1 Saving and Loading AI Models
🔹 9.2 Deploying Models with Flask
🔹 9.3 Deploying with FastAPI (Modern & Fast 🚀)
🔹 9.4 Running AI Models in the Cloud
🧪 Practice Time
📌 Quick Tips
🚀 What’s Next?
Chapter 10: Advanced AI Topics & Next Steps
🔹 10.1 Reinforcement Learning (RL) Overview
🔹 10.2 Generative AI & Large Language Models (LLMs)
🔹 10.3 Trends and Future of AI
🔹 10.4 Career Roadmap in AI
🧭 Your Learning Journey: What’s Next?
🧪 Final Challenge
🧠 Final Thoughts