01-20-2025, 10:31 AM
![[Image: 537368816_que-es-udemy-analisis-opiniones.jpg]](https://img100.pixhost.to/images/404/537368816_que-es-udemy-analisis-opiniones.jpg)
5.04 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 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)
10 Root Square Mean Error.mp4 (3.97 MB)
11 Variance vs Bias trade off.mp4 (6.09 MB)
12 Parameter tuning and Cross Validation.mp4 (7.81 MB)
13 SHAP Values.mp4 (6.18 MB)
14 Python - XGBoost Setup.mp4 (130.6 MB)
15 Python - Data Processing.mp4 (98.73 MB)
16 Python - First XGBoost Model.mp4 (137.2 MB)
17 Python - Evaluating XGBoost Model.mp4 (186.28 MB)
18 Python - Evaluating XGBoost Model part 2.mp4 (90.16 MB)
19 Python - Building Functions for Binary Model Assessment.mp4 (39.23 MB)
2 Problem Statement.mp4 (28.69 MB)
20 Python - Data Processing Part 2.mp4 (82.05 MB)
21 Python - Second XGBoost Model.mp4 (52.06 MB)
22 Random Parameter Tuning.mp4 (16.19 MB)
23 Python - Parameter Tuning.mp4 (158.6 MB)
24 Python - Final XGBoost Model.mp4 (136.35 MB)
25 Python - SHAP Importance and Summary Plot.mp4 (67.77 MB)
26 Python - SHAP Dependence and Force Plots.mp4 (90.46 MB)
27 Python - SHAP Waterfall and Cohort Deep Dives.mp4 (74.22 MB)
28 R - Loading and inspecting data.mp4 (31.26 MB)
29 R - Isolating numerical variables.mp4 (18.85 MB)
3 Introducing XGBoost.mp4 (10.47 MB)
30 R - Summary Statistics and Correlation Matrix.mp4 (17.95 MB)
31 R - Preparing first dataset.mp4 (8.33 MB)
32 R - Training and test set.mp4 (18.6 MB)
33 R - Isolating X and Y variables.mp4 (34.82 MB)
34 R - Setting XGBoost Parameters.mp4 (31.93 MB)
35 R - Parallel Processing.mp4 (7.42 MB)
36 R - Running XGBoost.mp4 (18.98 MB)
37 R - Predicting with XGBoost.mp4 (16.66 MB)
38 R - Confusion Matrix.mp4 (22.18 MB)
39 R - Transforming factors into numerical variables.mp4 (18.88 MB)
4 How XGBoost works.mp4 (10.65 MB)
40 R - Preparing final dataset.mp4 (14.61 MB)
41 R - Second XGBoost model.mp4 (12.02 MB)
42 R - Predictions and Confusion Matrix part 2.mp4 (7.29 MB)
43 R - Start Parallel Processing.mp4 (10 MB)
44 R - Cross Validation inputs.mp4 (11.12 MB)
45 R - Cross Validation Parameters.mp4 (10.98 MB)
46 R - Parameters to tune.mp4 (39.48 MB)
47 R - Parameter Tuning round 1.mp4 (61.18 MB)
48 R - Parameter Tuning round 2.mp4 (57 MB)
49 R - Final XGBoost model.mp4 (19.2 MB)
5 XGBoost quirks.mp4 (3.48 MB)
50 R - Business Perspective.mp4 (58.37 MB)
51 R - Importance Drivers and SHAP Values.mp4 (36.67 MB)
6 Dummy variable trap.mp4 (6.43 MB)
7 Training and test set.mp4 (2.79 MB)
8 Confusion Matrix.mp4 (8.79 MB)
9 Area Under the Curve (AUC ROC).mp4 (6.3 MB)]
Screenshot
![[Image: zhRsCpUz_o.jpg]](https://images2.imgbox.com/f9/57/zhRsCpUz_o.jpg)
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 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)
10 Root Square Mean Error.mp4 (3.97 MB)
11 Variance vs Bias trade off.mp4 (6.09 MB)
12 Parameter tuning and Cross Validation.mp4 (7.81 MB)
13 SHAP Values.mp4 (6.18 MB)
14 Python - XGBoost Setup.mp4 (130.6 MB)
15 Python - Data Processing.mp4 (98.73 MB)
16 Python - First XGBoost Model.mp4 (137.2 MB)
17 Python - Evaluating XGBoost Model.mp4 (186.28 MB)
18 Python - Evaluating XGBoost Model part 2.mp4 (90.16 MB)
19 Python - Building Functions for Binary Model Assessment.mp4 (39.23 MB)
2 Problem Statement.mp4 (28.69 MB)
20 Python - Data Processing Part 2.mp4 (82.05 MB)
21 Python - Second XGBoost Model.mp4 (52.06 MB)
22 Random Parameter Tuning.mp4 (16.19 MB)
23 Python - Parameter Tuning.mp4 (158.6 MB)
24 Python - Final XGBoost Model.mp4 (136.35 MB)
25 Python - SHAP Importance and Summary Plot.mp4 (67.77 MB)
26 Python - SHAP Dependence and Force Plots.mp4 (90.46 MB)
27 Python - SHAP Waterfall and Cohort Deep Dives.mp4 (74.22 MB)
28 R - Loading and inspecting data.mp4 (31.26 MB)
29 R - Isolating numerical variables.mp4 (18.85 MB)
3 Introducing XGBoost.mp4 (10.47 MB)
30 R - Summary Statistics and Correlation Matrix.mp4 (17.95 MB)
31 R - Preparing first dataset.mp4 (8.33 MB)
32 R - Training and test set.mp4 (18.6 MB)
33 R - Isolating X and Y variables.mp4 (34.82 MB)
34 R - Setting XGBoost Parameters.mp4 (31.93 MB)
35 R - Parallel Processing.mp4 (7.42 MB)
36 R - Running XGBoost.mp4 (18.98 MB)
37 R - Predicting with XGBoost.mp4 (16.66 MB)
38 R - Confusion Matrix.mp4 (22.18 MB)
39 R - Transforming factors into numerical variables.mp4 (18.88 MB)
4 How XGBoost works.mp4 (10.65 MB)
40 R - Preparing final dataset.mp4 (14.61 MB)
41 R - Second XGBoost model.mp4 (12.02 MB)
42 R - Predictions and Confusion Matrix part 2.mp4 (7.29 MB)
43 R - Start Parallel Processing.mp4 (10 MB)
44 R - Cross Validation inputs.mp4 (11.12 MB)
45 R - Cross Validation Parameters.mp4 (10.98 MB)
46 R - Parameters to tune.mp4 (39.48 MB)
47 R - Parameter Tuning round 1.mp4 (61.18 MB)
48 R - Parameter Tuning round 2.mp4 (57 MB)
49 R - Final XGBoost model.mp4 (19.2 MB)
5 XGBoost quirks.mp4 (3.48 MB)
50 R - Business Perspective.mp4 (58.37 MB)
51 R - Importance Drivers and SHAP Values.mp4 (36.67 MB)
6 Dummy variable trap.mp4 (6.43 MB)
7 Training and test set.mp4 (2.79 MB)
8 Confusion Matrix.mp4 (8.79 MB)
9 Area Under the Curve (AUC ROC).mp4 (6.3 MB)]
Screenshot
![[Image: signature.png]](https://softwarez.info/images/avsg/signature.png)
