10 Days Prompt Engineering, Generative AI and Data Science

8.21 GB | 14min 56s | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English

Files Included :
1 Introduction to 5 Projects in Prompt Engineering, Generative AI and Data Science.mp4 (13.27 MB)
3 About me, Diogo.mp4 (21.47 MB)
4 Unlimited Updates 2025.mp4 (27.04 MB)
6 Setting Up Google Colab.mp4 (47.53 MB)
7 Setting Up Jupyter Notebook.mp4 (137.98 MB)
8 Installing R and RStudio.mp4 (10.53 MB)
1 Game Plan for Basics of Prompt Engineering.mp4 (29.69 MB)
10 Scientific Research on Chain of Thoughts.mp4 (95.33 MB)
11 Key Learnings and Outcomes Prompt Engineering Basics.mp4 (34.22 MB)
2 Understanding Transformers.mp4 (61.22 MB)
3 Attention Mechanisms in NLP.mp4 (17.51 MB)
4 Prompt Engineering Techniques.mp4 (51.71 MB)
5 Setting Up the LM Studio.mp4 (90.52 MB)
6 LM Studio - Explicit Instructions and One-Shot.mp4 (193.44 MB)
7 LM Studio - Few-Shot.mp4 (148.18 MB)
8 LLMs are Few-Shot Learners.mp4 (43.54 MB)
9 LM Studio - Chain of Thoughts.mp4 (158.17 MB)
1 Game Plan for Prompt Engineering with System Message and LLM Parameters.mp4 (39.05 MB)
10 LM Studio - Breaking the System Message Part 2.mp4 (41.18 MB)
12 Understanding Generation Model Parameters.mp4 (16.42 MB)
13 LM Studio - Parameters.mp4 (102.5 MB)
14 Key Learnings and Outcomes System Message and LLM Parameters.mp4 (40.6 MB)
2 Tokenization.mp4 (66.51 MB)
3 OpenAI Tokenizer.mp4 (69.85 MB)
4 Rock-Paper-Scissors, Dices and Strawberries.mp4 (138.3 MB)
5 System Message.mp4 (59.29 MB)
6 LM Studio - System Message.mp4 (147.29 MB)
8 LM Studio - Breaking the System Message Part 1.mp4 (18.22 MB)
1 Game Plan for Reasoning and Hallucinations.mp4 (28.07 MB)
2 Meta Prompting.mp4 (154.43 MB)
3 Analogical Reasoning Prompting.mp4 (72.16 MB)
4 Rephrase and Respond.mp4 (82.72 MB)
5 According-to Prompting.mp4 (108.22 MB)
6 Multi-Persona Collaboration.mp4 (112.42 MB)
7 Emotion Prompting.mp4 (78.44 MB)
8 Key Learnings and Outcomes Reasoning and Hallucinations for LLMs.mp4 (85.53 MB)
1 Game Plan LLM Reasoning Models.mp4 (44.93 MB)
2 How LLM Reasoning Models work.mp4 (42.45 MB)
3 Prompting Reasoning Models (OpenAI Focus).mp4 (65.2 MB)
4 Overthinking - LLM Reasoning Prompt Injection.mp4 (47.8 MB)
5 Overthinking in Action.mp4 (71.85 MB)
6 LLMs can't Reason - Apple Research Paper.mp4 (77.18 MB)
1 Game Plan for OpenAI API.mp4 (37.81 MB)
2 OpenAI API for Text.mp4 (23.84 MB)
3 Python - Setting Up OpenAI API Key.mp4 (57.17 MB)
4 Python - OpenAI API Setup.mp4 (91.51 MB)
5 Python - Generating Text with OpenAI API.mp4 (84.62 MB)
6 Python - OpenAI API Parameters.mp4 (94.35 MB)
7 Python - OpenAI API with Few-Shot.mp4 (115.06 MB)
8 Key Learning and Outcomes OpenAI API for Text.mp4 (35.27 MB)
1 Project Introduction Can GPT play rock paper scissors.mp4 (48.1 MB)
2 Python - OpenAI API Setup.mp4 (38.73 MB)
3 Python - Random Playing Strategy.mp4 (61.05 MB)
4 Python - Iteratively Improving the System Prompt.mp4 (73.78 MB)
5 Python - Testing Temperature Parameters.mp4 (57.42 MB)
6 Python - New Strategy Change if Defeat.mp4 (67.72 MB)
7 Python - Building Functions to Play Game.mp4 (159.26 MB)
8 Python - New Strategy The Analyst.mp4 (74.52 MB)
9 Python - Testing Strategies.mp4 (77.91 MB)
1 Game Plan for OpenAI API for Images.mp4 (67.75 MB)
2 Python - OpenAI API Setup.mp4 (87.51 MB)
3 Python - Analyzing images from Links.mp4 (128.01 MB)
4 Python - Encoding Images.mp4 (44.81 MB)
5 Python - Analyzing Images from base64.mp4 (56.74 MB)
6 Python - Adding Google Searches.mp4 (160.68 MB)
1 Game Plan for Random Forest.mp4 (27.12 MB)
10 Evaluation metrics for Classication Problems Part 2.mp4 (30.41 MB)
11 Python - Assessing Classification Model.mp4 (51.22 MB)
12 Python - Parameter Tuning.mp4 (64.63 MB)
13 Python - Parameter Tuning.mp4 (53.38 MB)
14 Python - Best Random Forest Model.mp4 (41.46 MB)
15 Python - Feature Importance.mp4 (55.93 MB)
16 Python - Charting a Great Plot Part 1.mp4 (91.5 MB)
17 Python - Charting a Great Plot Part 2.mp4 (42.32 MB)
18 Python - Charting a Great Plot Part 3.mp4 (96.14 MB)
19 Key Learnings and Outcomes Random Forest.mp4 (21.53 MB)
2 Random Forest and Ensemble Learning.mp4 (54.47 MB)
3 How Decision Trees Work.mp4 (15.82 MB)
4 Python - Setup.mp4 (57.87 MB)
5 Python - Data Processing.mp4 (69.53 MB)
6 Python - Correlation Heatmap.mp4 (116.42 MB)
7 Python - Training and Test Set.mp4 (69.16 MB)
8 Python - Random Forest.mp4 (21.2 MB)
9 Evaluation metrics for Classication Problems Part 1.mp4 (59.32 MB)
10 SHAP Values.mp4 (6.18 MB)
11 Python - XGBoost Setup.mp4 (130.6 MB)
12 Python - Data Processing.mp4 (98.73 MB)
13 Python - First XGBoost Model.mp4 (137.2 MB)
14 Python - Evaluating XGBoost Model.mp4 (186.28 MB)
15 Python - Evaluating XGBoost Model part 2.mp4 (90.16 MB)
16 Python - Building Functions for Binary Model Assessment.mp4 (39.23 MB)
17 Python - Data Processing Part 2.mp4 (82.05 MB)
18 Python - Second XGBoost Model.mp4 (52.06 MB)
19 Random Parameter Tuning.mp4 (16.19 MB)
2 Problem Statement.mp4 (28.69 MB)
20 Python - Parameter Tuning.mp4 (158.6 MB)
21 Python - Final XGBoost Model.mp4 (136.35 MB)
22 Python - SHAP Importance and Summary Plot.mp4 (67.77 MB)
23 Python - SHAP Dependence and Force Plots.mp4 (90.46 MB)
24 Python - SHAP Waterfall and Cohort Deep Dives.mp4 (74.22 MB)
25 R - Loading and inspecting data.mp4 (31.26 MB)
26 R - Isolating numerical variables.mp4 (18.85 MB)
27 R - Summary Statistics and Correlation Matrix.mp4 (17.95 MB)
28 R - Preparing first dataset.mp4 (8.33 MB)
29 R - Training and test set.mp4 (18.6 MB)
3 Introducing XGBoost.mp4 (10.47 MB)
30 R - Isolating X and Y variables.mp4 (34.82 MB)
31 R - Setting XGBoost Parameters.mp4 (31.93 MB)
32 R - Parallel Processing.mp4 (7.42 MB)
33 R - Running XGBoost.mp4 (18.98 MB)
34 R - Predicting with XGBoost.mp4 (16.66 MB)
35 R - Confusion Matrix.mp4 (22.18 MB)
36 R - Transforming factors into numerical variables.mp4 (18.88 MB)
37 R - Preparing final dataset.mp4 (14.61 MB)
38 R - Second XGBoost model.mp4 (12.02 MB)
39 R - Predictions and Confusion Matrix part 2.mp4 (7.29 MB)
4 How XGBoost works.mp4 (10.65 MB)
40 R - Start Parallel Processing.mp4 (10 MB)
41 R - Cross Validation inputs.mp4 (11.12 MB)
42 R - Cross Validation Parameters.mp4 (10.98 MB)
43 R - Parameters to tune.mp4 (39.48 MB)
44 R - Parameter Tuning round 1.mp4 (61.18 MB)
45 R - Parameter Tuning round 2.mp4 (57 MB)
46 R - Final XGBoost model.mp4 (19.2 MB)
47 R - Business Perspective.mp4 (58.37 MB)
48 R - Importance Drivers and SHAP Values.mp4 (36.67 MB)
5 XGBoost quirks.mp4 (3.48 MB)
6 Dummy variable trap.mp4 (6.43 MB)
7 Root Square Mean Error.mp4 (3.97 MB)
8 Variance vs Bias trade off.mp4 (6.09 MB)
9 Parameter tuning and Cross Validation.mp4 (7.81 MB)
1 Game Plan for CrewAI.mp4 (60.58 MB)
2 CrewAI.mp4 (35.11 MB)
3 Python - CrewAI Setup.mp4 (113.68 MB)
4 Python - First AI Agent.mp4 (84.24 MB)
5 Python - Task and AI Crew.mp4 (111.24 MB)
6 Python - Second AI Agent and Task.mp4 (72.32 MB)
7 Python - Third AI Agent.mp4 (129.86 MB)]
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