AI/ML Development Career Path: Foundations to Advanced Skills

Welcome to the comprehensive guide for a career in AI and machine learning development. This section compiles all chapters, meticulously structured to take you from foundational mathematics and programming to advanced topics like deep learning, LLM fine-tuning, and responsible AI. Dive into extensive hands-on projects, real-world datasets, and expert guidance to become a professional AI/ML engineer or researcher.

Chapters

1. 01 The AI/ML Landscape & Foundational Math 14m
2. 02 Python for AI/ML: A Deep Dive 14m
3. 03 Data Science Toolkit: NumPy, Pandas, Matplotlib 12m
4. 04 Introduction to Classical Machine Learning 18m
5. 05 Model Training, Evaluation & Hyperparameter Tuning 18m
6. 06 Deep Learning Fundamentals & Neural Networks 22m
7. 07 Convolutional Neural Networks (CNNs) for Computer Vision 14m
8. 08 Recurrent Neural Networks (RNNs) for Sequence Data 17m
9. 09 The Transformer Architecture & Attention Mechanisms 16m
10. 10 Fine-Tuning Large Language Models (LLMs) 22m
11. 11 Embeddings, Vector Databases & Semantic Search 15m
12. 12 Multimodal Models: Vision-Language Integration 14m
13. 13 Data Preparation & Feature Engineering for Production 19m
14. 14 Model Training Workflows & Optimization Techniques 18m
15. 15 Inference Optimization & Model Deployment 19m
16. 16 Hardware Considerations: CPU, GPU, & Accelerators 13m
17. 17 Distributed Training & Scaling Deep Learning 19m
18. 18 Experimentation, Tracking & Debugging Model Behavior 17m
19. 19 Research Literacy & Staying Current in AI 13m
20. 20 Responsible AI: Ethics, Bias & Fairness 18m
21. 21 Project: Building a Custom Image Classifier 18m
22. 22 Building a Semantic Search Engine Using Embeddings 15m
23. 23 Project: Fine-Tuning an LLM for a Specific Task 21m
24. 24 Professional Development & Career Guidance 13m