1 - Natural vs Artificial Intelligence (87.9 MB)
2 - Brief history of AI (187.08 MB)
3 - Demystifying AI Data science Machine learning and Deep learning (14.17 MB)
4 - Weak vs Strong AI (56.01 MB)
5 - Structured vs unstructured data (10.53 MB)
6 - How we collect data (151.08 MB)
7 - Labelled and unlabelled data (12.4 MB)
8 - Metadata Data that describes data (14.49 MB)
10 - Supervised Unsupervised and Reinforcement learning (47.97 MB)
11 - Deep learning (243.18 MB)
9 - Machine learning (169.78 MB)
12 - Robotics (96.89 MB)
13 - Computer vision (181.58 MB)
14 - Traditional ML (53.3 MB)
15 - Generative AI (84.64 MB)
16 - The rise of Gen AI Introducing ChatGPT (45.48 MB)
17 - Early approaches to Natural Language Processing NLP (15.37 MB)
18 - Recent NLP advancements (25.14 MB)
19 - From Language Models to Large Language Models LLMs (52.8 MB)
20 - The efficiency of LLM training Supervised vs Semisupervised learning (29.82 MB)
21 - From NGrams to RNNs to Transformers The Evolution of NLP (44.38 MB)
22 - Phases in building LLMs (39.47 MB)
23 - Prompt engineering vs Finetuning vs RAG Techniques for AI optimization (24.94 MB)
24 - The importance of foundation models (59.47 MB)
25 - Buy vs Make foundation models vs private models (15.13 MB)
26 - Inconsistency and hallucination (58.2 MB)
27 - Budgeting and API costs (17.24 MB)
28 - Latency (30.26 MB)
29 - Running out of data (14.05 MB)
30 - Python programming (17.88 MB)
31 - Working with APIs (9.16 MB)
32 - Vector databases (26.26 MB)
33 - The importance of open source (128.46 MB)
34 - Hugging Face (10.11 MB)
35 - Hugging Face (16.83 MB)
36 - AI evaluation tools (18.07 MB)
37 - AI strategist (43.58 MB)
38 - AI developer (25.88 MB)
39 - AI engineer (32.44 MB)
40 - AI ethics (120.06 MB)
41 - Future of AI (51.62 MB)