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Complete Machine Learning & Data Science Bootcamp 2021
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[TutsNode.com] - Complete Machine Learning & Data Science Bootcamp 2021 (Size: 19.54 GB) (Files: 1163)
[TutsNode.com] - Complete Machine Learning & Data Science Bootcamp 2021
5. Data Science Environment Setup
8. Windows Environment Setup 2.mp4
10. Sharing your Conda Environment.html
11.1 6-step-ml-framework.png
3.2 conda-cheatsheet.pdf
8. Windows Environment Setup 2.srt
5. Mac Environment Setup.srt
12. Jupyter Notebook Walkthrough 2.srt
6. Mac Environment Setup 2.srt
1. Section Overview.srt
3.1 Getting started with Conda (documentation).html
3.3 Getting your computer ready for machine learning How, what and why you should use Anaconda, Miniconda and Conda (blog post).html
3.4 Conda documentation.html
5.1 Miniconda download documentation.html
7.1 Miniconda download documentation.html
9. Linux Environment Setup.html
10.1 Conda documentation on sharing an environment.html
11.3 Dataquest Jupyter Notebook for Beginners Tutorial.html
11.4 Jupyter Notebook documentation.html
11. Jupyter Notebook Walkthrough.srt
13. Jupyter Notebook Walkthrough 3.srt
11.2 heart-disease.csv
7. Windows Environment Setup.srt
4. Conda Environments.srt
2. Introducing Our Tools.srt
3. What is Conda.srt
5. Mac Environment Setup.mp4
6. Mac Environment Setup 2.mp4
12. Jupyter Notebook Walkthrough 2.mp4
13. Jupyter Notebook Walkthrough 3.mp4
11. Jupyter Notebook Walkthrough.mp4
7. Windows Environment Setup.mp4
4. Conda Environments.mp4
2. Introducing Our Tools.mp4
3. What is Conda.mp4
1. Section Overview.mp4
8. Matplotlib Plotting and Data Visualization
4.2 matplotlib-anatomy-of-a-plot.png
4.1 matplotlib-anatomy-of-a-plot-with-code.png
13. Plotting from Pandas DataFrames 4.srt
3. Importing And Using Matplotlib.srt
16. Plotting from Pandas DataFrames 7.srt
5. Scatter Plot And Bar Plot.srt
4. Anatomy Of A Matplotlib Figure.srt
17. Customizing Your Plots.srt
11. Plotting From Pandas DataFrames 2.srt
18. Customizing Your Plots 2.srt
6. Histograms And Subplots.srt
14. Plotting from Pandas DataFrames 5.srt
1. Section Overview.srt
15. Plotting from Pandas DataFrames 6.srt
2.2 Matplotlib Documentation.html
12. Plotting from Pandas DataFrames 3.srt
8. Quick Tip Data Visualizations.srt
13.1 heart-disease.csv
10. Quick Note Regular Expressions.html
19.1 Introduction to Matplotlib Notebook (from the videos).html
20. Assignment Matplotlib Practice.html
9. Plotting From Pandas DataFrames.srt
2. Matplotlib Introduction.srt
7. Subplots Option 2.srt
19. Saving And Sharing Your Plots.srt
2.1 Introduction to Matplotlib Jupyter Notebook (from the upcoming videos).html
18. Customizing Your Plots 2.mp4
16. Plotting from Pandas DataFrames 7.mp4
11. Plotting From Pandas DataFrames 2.mp4
17. Customizing Your Plots.mp4
3. Importing And Using Matplotlib.mp4
4. Anatomy Of A Matplotlib Figure.mp4
15. Plotting from Pandas DataFrames 6.mp4
12. Plotting from Pandas DataFrames 3.mp4
6. Histograms And Subplots.mp4
5. Scatter Plot And Bar Plot.mp4
9. Plotting From Pandas DataFrames.mp4
14. Plotting from Pandas DataFrames 5.mp4
19. Saving And Sharing Your Plots.mp4
13. Plotting from Pandas DataFrames 4.mp4
7. Subplots Option 2.mp4
2. Matplotlib Introduction.mp4
8. Quick Tip Data Visualizations.mp4
1. Section Overview.mp4
9. Scikit-learn Creating Machine Learning Models
7. Typical scikit-learn Workflow.srt
7. Typical scikit-learn Workflow.mp4
8. Optional Debugging Warnings In Jupyter.mp4
41. Tuning Hyperparameters.srt
48. Putting It All Together.srt
8. Optional Debugging Warnings In Jupyter.srt
15. Getting Your Data Ready Handling Missing Values With Scikit-learn.srt
11. Getting Your Data Ready Convert Data To Numbers.srt
16. Choosing The Right Model For Your Data.srt
9.1 scikit-learn-data.zip
43. Tuning Hyperparameters 3.srt
38. Evaluating A Model With Cross Validation and Scoring Parameter.srt
26. Evaluating A Machine Learning Model 2 (Cross Validation).srt
20. Choosing The Right Model For Your Data 3 (Classification).srt
42. Tuning Hyperparameters 2.srt
12. Getting Your Data Ready Handling Missing Values With Pandas.srt
39. Evaluating A Model With Scikit-learn Functions.srt
49. Putting It All Together 2.srt
31. Evaluating A Classification Model 4 (Confusion Matrix).srt
40. Improving A Machine Learning Model.srt
33. Evaluating A Classification Model 6 (Classification Report).srt
25. Evaluating A Machine Learning Model (Score).srt
28. Evaluating A Classification Model 2 (ROC Curve).srt
22. Making Predictions With Our Model.srt
9. Getting Your Data Ready Splitting Your Data.srt
34. Evaluating A Regression Model 1 (R2 Score).srt
17. Choosing The Right Model For Your Data 2 (Regression).srt
23. predict() vs predict_proba().srt
32. Evaluating A Classification Model 5 (Confusion Matrix).srt
2. Scikit-learn Introduction.srt
6. Scikit-learn Cheatsheet.srt
29. Evaluating A Classification Model 3 (ROC Curve).srt
46. Saving And Loading A Model.srt
2.1 Introduction to Scikit-Learn Jupyter Notebook (from the upcoming videos).html
2.2 Scikit-Learn Documentation.html
2.3 Introduction to Scikit-Learn Jupyter Notebook (with annotations).html
3. Quick Note Upcoming Video.html
5. Quick Note Upcoming Videos.html
21. Fitting A Model To The Data.srt
6.1 Scikit-Learn Reference Notebook.html
36. Evaluating A Regression Model 3 (MSE).srt
7.1 Example Scikit-Learn Workflow Notebook.html
24. Making Predictions With Our Model (Regression).srt
47. Saving And Loading A Model 2.srt
13. Extension Feature Scaling.html
14. Note Correction in the upcoming video (splitting data).html
16.1 Scikit-Learn machine learning map (how to choose the right machine learning model).html
18. Quick Note Decision Trees.html
19. Quick Tip How ML Algorithms Work.srt
30. Reading Extension ROC Curve + AUC.html
37. Machine Learning Model Evaluation.html
31.1 Notebook from video with updated confusion matrix labels.html
10. Quick Tip Clean, Transform, Reduce.srt
4. Refresher What Is Machine Learning.srt
44. Note Metric Comparison Improvement.html
27. Evaluating A Classification Model 1 (Accuracy).srt
48.1 Reading extension Scikit-Learn's Pipeline class explained.html
49.1 Introduction to Scikit-Learn Jupyter Notebook (from the videos).html
49.2 Introduction to Scikit-Learn Jupyter Notebook (with annotations).html
50. Scikit-Learn Practice.html
35. Evaluating A Regression Model 2 (MAE).srt
1. Section Overview.srt
45. Quick Tip Correlation Analysis.srt
41. Tuning Hyperparameters.mp4
48. Putting It All Together.mp4
16. Choosing The Right Model For Your Data.mp4
15. Getting Your Data Ready Handling Missing Values With Scikit-learn.mp4
11. Getting Your Data Ready Convert Data To Numbers.mp4
43. Tuning Hyperparameters 3.mp4
20. Choosing The Right Model For Your Data 3 (Classification).mp4
49. Putting It All Together 2.mp4
42. Tuning Hyperparameters 2.mp4
12. Getting Your Data Ready Handling Missing Values With Pandas.mp4
26. Evaluating A Machine Learning Model 2 (Cross Validation).mp4
39. Evaluating A Model With Scikit-learn Functions.mp4
38. Evaluating A Model With Cross Validation and Scoring Parameter.mp4
40. Improving A Machine Learning Model.mp4
4. Refresher What Is Machine Learning.mp4
33. Evaluating A Classification Model 6 (Classification Report).mp4
25. Evaluating A Machine Learning Model (Score).mp4
17. Choosing The Right Model For Your Data 2 (Regression).mp4
31. Evaluating A Classification Model 4 (Confusion Matrix).mp4
6. Scikit-learn Cheatsheet.mp4
34. Evaluating A Regression Model 1 (R2 Score).mp4
22. Making Predictions With Our Model.mp4
28. Evaluating A Classification Model 2 (ROC Curve).mp4
32. Evaluating A Classification Model 5 (Confusion Matrix).mp4
9. Getting Your Data Ready Splitting Your Data.mp4
47. Saving And Loading A Model 2.mp4
21. Fitting A Model To The Data.mp4
36. Evaluating A Regression Model 3 (MSE).mp4
23. predict() vs predict_proba().mp4
46. Saving And Loading A Model.mp4
29. Evaluating A Classification Model 3 (ROC Curve).mp4
24. Making Predictions With Our Model (Regression).mp4
2. Scikit-learn Introduction.mp4
27. Evaluating A Classification Model 1 (Accuracy).mp4
35. Evaluating A Regression Model 2 (MAE).mp4
45. Quick Tip Correlation Analysis.mp4
10. Quick Tip Clean, Transform, Reduce.mp4
1. Section Overview.mp4
19. Quick Tip How ML Algorithms Work.mp4
1. Introduction
2. Join Our Online Classroom!.html
3. Exercise Meet The Community.html
1. Course Outline.srt
4. Your First Day.srt
1. Course Outline.mp4
4. Your First Day.mp4
7. NumPy
2.2 NumPy Documentation.html
4. NumPy DataTypes and Attributes.srt
13. Exercise Nut Butter Store Sales.srt
8. Manipulating Arrays.srt
12. Dot Product vs Element Wise.srt
2.1 Introduction to NumPy Jupyter Notebook (with annotations).html
2.3 Introduction to NumPy Jupyter Notebook (from the upcoming videos).html
3. Quick Note Correction In Next Video.html
7. Viewing Arrays and Matrices.srt
8.1 Standard deviation and variance explained.html
9.1 Standard deviation and variance explained.html
10.1 Standard deviation and variance explained.html
5. Creating NumPy Arrays.srt
12.1 Matrix Multiplication Explained.html
16.1 Introduction to NumPy Jupyter Notebook (from the videos).html
16.2 Introduction to NumPy Jupyter Notebook (with annotations).html
17. Assignment NumPy Practice.html
18. Optional Extra NumPy resources.html
9. Manipulating Arrays 2.srt
16. Turn Images Into NumPy Arrays.srt
6. NumPy Random Seed.srt
11. Reshape and Transpose.srt
10. Standard Deviation and Variance.srt
15. Sorting Arrays.srt
2. NumPy Introduction.srt
14. Comparison Operators.srt
1. Section Overview.srt
13. Exercise Nut Butter Store Sales.mp4
16. Turn Images Into NumPy Arrays.mp4
12. Dot Product vs Element Wise.mp4
8. Manipulating Arrays.mp4
4. NumPy DataTypes and Attributes.mp4
7. Viewing Arrays and Matrices.mp4
9. Manipulating Arrays 2.mp4
5. Creating NumPy Arrays.mp4
11. Reshape and Transpose.mp4
6. NumPy Random Seed.mp4
10. Standard Deviation and Variance.mp4
15. Sorting Arrays.mp4
2. NumPy Introduction.mp4
14. Comparison Operators.mp4
1. Section Overview.mp4
16.3 numpy-images.zip
6. Pandas Data Analysis
4.1 pandas-anatomy-of-a-dataframe.png
10.1 pandas-anatomy-of-a-dataframe.png
9. Manipulating Data.srt
8. Selecting and Viewing Data with Pandas Part 2.srt
4. Series, Data Frames and CSVs.srt
2. Downloading Workbooks and Assignments.html
3.1 Introduction to Pandas Jupyter Notebook (from the upcoming videos).html
3.2 10-minutes to pandas (from the pandas documentation).html
3.3 Pandas Documentation.html
3.4 Introduction to Pandas Jupyter Notebook (with annotations).html
5. Data from URLs.html
7.1 car-sales.csv
9.1 Jake VanderPlas's Data Manipulation with Pandas.html
9.2 car-sales-missing-data.csv
7. Selecting and Viewing Data with Pandas.srt
11.1 Introduction to Pandas Jupyter Notebook (with annotations).html
11.2 Introduction to Pandas Jupyter Notebook (from the videos).html
12. Assignment Pandas Practice.html
10. Manipulating Data 2.srt
13.1 Google Colab.html
13.2 Course notebooks - Github.html
11. Manipulating Data 3.srt
6. Describing Data with Pandas.srt
13. How To Download The Course Assignments.srt
3. Pandas Introduction.srt
1. Section Overview.srt
8. Selecting and Viewing Data with Pandas Part 2.mp4
9. Manipulating Data.mp4
4. Series, Data Frames and CSVs.mp4
11. Manipulating Data 3.mp4
10. Manipulating Data 2.mp4
6. Describing Data with Pandas.mp4
7. Selecting and Viewing Data with Pandas.mp4
13. How To Download The Course Assignments.mp4
3. Pandas Introduction.mp4
1. Section Overview.mp4
2. Machine Learning 101
3.1 Teachable Machine.html
5.1 Machine Learning Playground.html
7. Are You Getting It Yet.html
9. Section Review.srt
1. What Is Machine Learning.srt
3. Exercise Machine Learning Playground.srt
4. How Did We Get Here.srt
2. AIMachine LearningData Science.srt
8. What Is Machine Learning Round 2.srt
5. Exercise YouTube Recommendation Engine.srt
6. Types of Machine Learning.srt
3. Exercise Machine Learning Playground.mp4
4. How Did We Get Here.mp4
1. What Is Machine Learning.mp4
8. What Is Machine Learning Round 2.mp4
6. Types of Machine Learning.mp4
2. AIMachine LearningData Science.mp4
5. Exercise YouTube Recommendation Engine.mp4
9. Section Review.mp4
14. Neural Networks Deep Learning, Transfer Learning and TensorFlow 2
32. Training Your Deep Neural Network.srt
41. Making Predictions On Test Images.srt
2. Deep Learning and Unstructured Data.srt
21. Turning Data Into Batches 2.srt
34. Make And Transform Predictions.srt
40. Training Model On Full Dataset.srt
43. Making Predictions On Our Images.srt
37. Visualizing And Evaluate Model Predictions 2.srt
35. Transform Predictions To Text.srt
36. Visualizing Model Predictions.srt
39. Saving And Loading A Trained Model.srt
9. Importing TensorFlow 2.srt
42. Submitting Model to Kaggle.srt
14. Loading Our Data Labels.srt
25. Building A Deep Learning Model.srt
22. Visualizing Our Data.srt
15. Preparing The Images.srt
38. Visualizing And Evaluate Model Predictions 3.srt
16. Turning Data Labels Into Numbers.srt
18. Preprocess Images.srt
19. Preprocess Images 2.srt
26. Building A Deep Learning Model 2.srt
11. Using A GPU.srt
28. Building A Deep Learning Model 4.srt
20. Turning Data Into Batches.srt
17. Creating Our Own Validation Set.srt
27. Building A Deep Learning Model 3.srt
30. Evaluating Our Model.srt
4. Setting Up Google Colab.srt
33. Evaluating Performance With TensorBoard.srt
6. Uploading Project Data.srt
23. Preparing Our Inputs and Outputs.srt
13. Optional Reloading Colab Notebook.srt
7. Setting Up Our Data.srt
5. Google Colab Workspace.srt
12. Optional GPU and Google Colab.srt
29. Summarizing Our Model.srt
31. Preventing Overfitting.srt
10. Optional TensorFlow 2.0 Default Issue.srt
44. Finishing Dog Vision Where to next.html
1. Section Overview.srt
24. Optional How machines learn and what's going on behind the scenes.html
8. Setting Up Our Data 2.srt
3. Setting Up With Google.html
43.2 End-to-end Dog Vision Notebook (from the videos).html
43.1 End-to-end Dog Vision Notebook (with annotations).html
4.5 End-to-end Dog Vision Notebook (the project we'll be working through).html
42.1 Dog Vision Predictions with MobileNetV2 Ready for Kaggle Submission.html
4.1 Kaggle Dog Breed Identification Competition (the basis of our upcoming project).html
4.2 Google Colab (our workspace for the upcoming project).html
4.3 Google Colab IO example (how to get data in and out of your Colab notebook).html
4.4 Introduction to Google Colab example notebook.html
27.2 Step by step breakdown of a convolutional neural network (what MobileNetV2 is made of).html
41.1 Dog Vision Prediction Probabilities Array.html
5.1 Google Colab FAQ (things you should know about Google Colab).html
5.2 Google Colab (our workspace for the upcoming project).html
28.1 [Article] How to choose loss & activation functions when building a deep learning model.html
6.1 Kaggle Dog Breed Identification Competition Data.html
6.2 Google Colab IO example (how to get data in and out of your Colab notebook).html
27.3 MobileNetV2 (the model we're using) architecture explanation by Sik-Ho Tsang.html
31.1 Early Stopping Callback (a way to stop your model from training when it stops improving) Documentation.html
30.1 TensorBoard Callback Documentation.html
25.3 MobileNetV2 (the model we're using) on TensorFlow Hub.html
10.1 Loading TensorFlow 2.0 into a Colab Notebook (if it isn't the default).html
14.1 Documentation on how many images Google recommends for image problems.html
35.1 TensorFlow documentation for the unbatch() function.html
11.1 Google Colab example GPU usage.html
21.1 Yann LeCun's (OG of deep learning) Tweet on Batch Sizes.html
12.1 Introduction to Google Colab example notebook.html
12.2 Google Colab Example of GPU speed up versus CPU.html
18.1 Documentation for loading images in TensorFlow.html
17.1 Blog post by Rachel Thomas (of fast.ai) on how and why you should create a validation set.html
26.1 Keras in TensorFlow Overview Documentation.html
27.1 The Softmax Function (activation function we use in our model).html
18.2 TensorFlow guidelines for loading all kinds of data (turning your data into Tensors).html
23.1 TensorFlow Hub (resource for pre-trained deep learning models and more).html
25.1 Andrei Karpathy's talk on AI at Tesla.html
25.2 Papers with Code (a great resource for some of the best machine learning papers with code examples).html
25.4 PyTorch Hub (PyTorch version of TensorFlow Hub).html
25.5 TensorFlow Hub (resource for pre-trained deep learning models and more).html
32. Training Your Deep Neural Network.mp4
34. Make And Transform Predictions.mp4
21. Turning Data Into Batches 2.mp4
37. Visualizing And Evaluate Model Predictions 2.mp4
41. Making Predictions On Test Images.mp4
40. Training Model On Full Dataset.mp4
15. Preparing The Images.mp4
35. Transform Predictions To Text.mp4
39. Saving And Loading A Trained Model.mp4
22. Visualizing Our Data.mp4
25. Building A Deep Learning Model.mp4
42. Submitting Model to Kaggle.mp4
36. Visualizing Model Predictions.mp4
43. Making Predictions On Our Images.mp4
9. Importing TensorFlow 2.mp4
14. Loading Our Data Labels.mp4
38. Visualizing And Evaluate Model Predictions 3.mp4
16. Turning Data Labels Into Numbers.mp4
27. Building A Deep Learning Model 3.mp4
26. Building A Deep Learning Model 2.mp4
19. Preprocess Images 2.mp4
2. Deep Learning and Unstructured Data.mp4
18. Preprocess Images.mp4
13. Optional Reloading Colab Notebook.mp4
20. Turning Data Into Batches.mp4
28. Building A Deep Learning Model 4.mp4
11. Using A GPU.mp4
30. Evaluating Our Model.mp4
4. Setting Up Google Colab.mp4
33. Evaluating Performance With TensorBoard.mp4
17. Creating Our Own Validation Set.mp4
6. Uploading Project Data.mp4
23. Preparing Our Inputs and Outputs.mp4
12. Optional GPU and Google Colab.mp4
29. Summarizing Our Model.mp4
7. Setting Up Our Data.mp4
5. Google Colab Workspace.mp4
31. Preventing Overfitting.mp4
10. Optional TensorFlow 2.0 Default Issue.mp4
8. Setting Up Our Data 2.mp4
1. Section Overview.mp4
11. Milestone Project 1 Supervised Learning (Classification)
22. Finding The Most Important Features.srt
9. Finding Patterns 2.srt
10. Finding Patterns 3.srt
14. TuningImproving Our Model.srt
5. Step 1~4 Framework Setup.srt
15. Tuning Hyperparameters.srt
19. Evaluating Our Model.srt
16. Tuning Hyperparameters 2.srt
3. Project Environment Setup.srt
23. Reviewing The Project.srt
8. Finding Patterns.srt
12. Choosing The Right Models.srt
6. Getting Our Tools Ready.srt
11. Preparing Our Data For Machine Learning.srt
21. Evaluating Our Model 3.srt
7. Exploring Our Data.srt
7.1 heart-disease.csv
2. Project Overview.srt
17. Tuning Hyperparameters 3.srt
13. Experimenting With Machine Learning Models.srt
20. Evaluating Our Model 2.srt
2.1 End-to-end Heart Disease Classification Notebook (with annotations).html
2.2 Structured Data Projects on GitHub.html
2.3 End-to-end Heart Disease Classification Notebook (same as in videos).html
4. Optional Windows Project Environment Setup.srt
18. Quick Note Confusion Matrix Labels.html
23.1 End-to-end Heart Disease Classification Notebook (with annotations).html
23.2 End-to-end Heart Disease Classification Notebook (same as in videos).html
1. Section Overview.srt
10. Finding Patterns 3.mp4
22. Finding The Most Important Features.mp4
15. Tuning Hyperparameters.mp4
5. Step 1~4 Framework Setup.mp4
16. Tuning Hyperparameters 2.mp4
14. TuningImproving Our Model.mp4
3. Project Environment Setup.mp4
9. Finding Patterns 2.mp4
12. Choosing The Right Models.mp4
23. Reviewing The Project.mp4
6. Getting Our Tools Ready.mp4
11. Preparing Our Data For Machine Learning.mp4
19. Evaluating Our Model.mp4
7. Exploring Our Data.mp4
21. Evaluating Our Model 3.mp4
8. Finding Patterns.mp4
17. Tuning Hyperparameters 3.mp4
13. Experimenting With Machine Learning Models.mp4
20. Evaluating Our Model 2.mp4
4. Optional Windows Project Environment Setup.mp4
2. Project Overview.mp4
1. Section Overview.mp4
12. Milestone Project 2 Supervised Learning (Time Series Data)
9. Turning Data Into Numbers.srt
8. Feature Engineering.srt
6. Exploring Our Data.srt
19. Preproccessing Our Data.srt
21. Feature Importance.srt
10. Filling Missing Numerical Values.srt
15. Custom Evaluation Function.srt
3. Project Environment Setup.srt
16. Reducing Data.srt
13. Splitting Data.srt
17. RandomizedSearchCV.srt
4. Step 1~4 Framework Setup.srt
20. Making Predictions.srt
11. Filling Missing Categorical Values.srt
18. Improving Hyperparameters.srt
12. Fitting A Machine Learning Model.srt
7. Exploring Our Data 2.srt
2. Project Overview.srt
1. Section Overview.srt
2.1 Structured Data Projects on GitHub.html
2.2 End-to-end Bluebook Bulldozer Regression Notebook (same as in videos).html
2.3 End-to-end Bluebook Bulldozer Regression Notebook (with annotations).html
2.4 Kaggle Bluebook for Bulldozers Competition.html
5. Downloading the data for the next two projects.html
10.1 Pandas Categorical Datatype Documentation.html
14. Challenge What's wrong with splitting data after filling it.html
21.1 End-to-end Bluebook Bulldozer Regression Notebook (with annotations).html
21.2 End-to-end Bluebook Bulldozer Regression Notebook (same as in videos).html
8. Feature Engineering.mp4
9. Turning Data Into Numbers.mp4
21. Feature Importance.mp4
19. Preproccessing Our Data.mp4
6. Exploring Our Data.mp4
10. Filling Missing Numerical Values.mp4
15. Custom Evaluation Function.mp4
3. Project Environment Setup.mp4
16. Reducing Data.mp4
17. RandomizedSearchCV.mp4
4. Step 1~4 Framework Setup.mp4
13. Splitting Data.mp4
18. Improving Hyperparameters.mp4
20. Making Predictions.mp4
11. Filling Missing Categorical Values.mp4
12. Fitting A Machine Learning Model.mp4
7. Exploring Our Data 2.mp4
2. Project Overview.mp4
1. Section Overview.mp4
3. Machine Learning and Data Science Framework
3.1 A 6 Step Field Guide for Machine Learning Modelling (blog post).html
12. Overfitting and Underfitting Definitions.html
15. Optional Elements of AI.html
4. Types of Machine Learning Problems.srt
11. Modelling - Comparison.srt
8. Modelling - Splitting Data.srt
7. Features In Data.srt
3. 6 Step Machine Learning Framework.srt
5. Types of Data.srt
9. Modelling - Picking the Model.srt
14. Tools We Will Use.srt
13. Experimentation.srt
10. Modelling - Tuning.srt
1. Section Overview.srt
6. Types of Evaluation.srt
2. Introducing Our Framework.srt
4. Types of Machine Learning Problems.mp4
11. Modelling - Comparison.mp4
7. Features In Data.mp4
5. Types of Data.mp4
8. Modelling - Splitting Data.mp4
14. Tools We Will Use.mp4
3. 6 Step Machine Learning Framework.mp4
9. Modelling - Picking the Model.mp4
13. Experimentation.mp4
6. Types of Evaluation.mp4
10. Modelling - Tuning.mp4
1. Section Overview.mp4
2. Introducing Our Framework.mp4
16. Career Advice + Extra Bits
9. CWD Git + Github.srt
3. What If I Don't Have Enough Experience.srt
10. CWD Git + Github 2.srt
11. Contributing To Open Source.srt
9. CWD Git + Github.mp4
12. Contributing To Open Source 2.srt
7. JTS Start With Why.srt
6. JTS Learn to Learn.srt
1. Endorsements On LinkedIn.html
14. Exercise Contribute To Open Source.html
13. Coding Challenges.html
2. Quick Note Upcoming Video.html
5. Quick Note Upcoming Videos.html
8. Quick Note Upcoming Videos.html
4. Learning Guideline.html
3. What If I Don't Have Enough Experience.mp4
11. Contributing To Open Source.mp4
10. CWD Git + Github 2.mp4
12. Contributing To Open Source 2.mp4
7. JTS Start With Why.mp4
6. JTS Learn to Learn.mp4
4. The 2 Paths
2. Python + Machine Learning Monthly.html
3. Endorsements On LinkedIN.html
1. The 2 Paths.srt
1. The 2 Paths.mp4
17. Learn Python
17. Variables.srt
11. Numbers.srt
35. List Methods.srt
27. Built-In Functions + Methods.srt
49. Sets 2.srt
25. String Indexes.srt
4. Our First Python Program.srt
24. Formatted Strings.srt
29. Exercise Type Conversion.srt
33. List Slicing.srt
2. Python Interpreter.srt
6. Python 2 vs Python 3.srt
48. Sets.srt
31. Exercise Password Checker.srt
45. Dictionary Methods 2.srt
41. Dictionaries.srt
1. What Is A Programming Language.srt
3. How To Run Python Code.srt
20. Strings.srt
38. Common List Patterns.srt
46. Tuples.srt
32. Lists.srt
12. Math Functions.srt
30. DEVELOPER FUNDAMENTALS II.srt
44. Dictionary Methods.srt
13. DEVELOPER FUNDAMENTALS I.srt
9. Python Data Types.srt
37. List Methods 3.srt
23. Escape Sequences.srt
16. Optional bin() and complex.srt
36. List Methods 2.srt
43. Dictionary Keys.srt
34. Matrix.srt
28. Booleans.srt
42. DEVELOPER FUNDAMENTALS III.srt
14. Operator Precedence.srt
26. Immutability.srt
22. Type Conversion.srt
47. Tuples 2.srt
19. Augmented Assignment Operator.srt
39. List Unpacking.srt
7. Exercise How Does Python Work.srt
5. Latest Version Of Python.srt
8. Learning Python.srt
40. None.srt
18. Expressions vs Statements.srt
21. String Concatenation.srt
15. Exercise Operator Precedence.html
10. How To Succeed.html
6.3 Python 2 vs Python 3 - another one.html
6.1 Python 2 vs Python 3.html
44.1 Dictionary Methods.html
17.1 Python Keywords.html
36.1 Python Keywords.html
19.1 Exercise Repl.html
27.1 String Methods.html
47.1 Tuple Methods.html
35.1 List Methods.html
49.2 Sets Methods.html
16.1 Base Numbers.html
27.2 Built in Functions.html
14.1 Exercise Repl.html
15.1 Exercise Repl.html
30.1 Python Comments Best Practices.html
6.2 The Story of Python.html
11.1 Floating point numbers.html
24.1 Exercise Repl.html
25.1 Exercise Repl.html
45.1 Exercise Repl.html
36.2 Exercise Repl.html
38.1 Exercise Repl.html
34.1 Exercise Repl.html
33.1 Exercise Repl.html
49.1 Exercise Repl.html
2.1 python.org.html
3.1 Glot.io.html
3.2 Repl.it.html
1. What Is A Programming Language.mp4
17. Variables.mp4
2. Python Interpreter.mp4
11. Numbers.mp4
6. Python 2 vs Python 3.mp4
27. Built-In Functions + Methods.mp4
49. Sets 2.mp4
35. List Methods.mp4
13. DEVELOPER FUNDAMENTALS I.mp4
3. How To Run Python Code.mp4
31. Exercise Password Checker.mp4
29. Exercise Type Conversion.mp4
33. List Slicing.mp4
24. Formatted Strings.mp4
25. String Indexes.mp4
4. Our First Python Program.mp4
45. Dictionary Methods 2.mp4
12. Math Functions.mp4
38. Common List Patterns.mp4
8. Learning Python.mp4
48. Sets.mp4
41. Dictionaries.mp4
20. Strings.mp4
30. DEVELOPER FUNDAMENTALS II.mp4
9. Python Data Types.mp4
37. List Methods 3.mp4
36. List Methods 2.mp4
44. Dictionary Methods.mp4
42. DEVELOPER FUNDAMENTALS III.mp4
7. Exercise How Does Python Work.mp4
46. Tuples.mp4
23. Escape Sequences.mp4
32. Lists.mp4
16. Optional bin() and complex.mp4
26. Immutability.mp4
43. Dictionary Keys.mp4
34. Matrix.mp4
22. Type Conversion.mp4
47. Tuples 2.mp4
28. Booleans.mp4
19. Augmented Assignment Operator.mp4
14. Operator Precedence.mp4
39. List Unpacking.mp4
18. Expressions vs Statements.mp4
5. Latest Version Of Python.mp4
40. None.mp4
21. String Concatenation.mp4
18. Learn Python Part 2
2. Conditional Logic.srt
24. return.srt
45. Modules in Python.srt
48. Packages in Python.srt
47. Optional PyCharm.srt
18. Our First GUI.srt
36. Pure Functions.srt
41. List Comprehensions.srt
21. Functions.srt
32. Scope Rules.srt
8. Exercise Logical Operators.srt
40. reduce().srt
9. is vs ==.srt
7. Logical Operators.srt
29. args and kwargs.srt
19. DEVELOPER FUNDAMENTALS IV.srt
10. For Loops.srt
49. Different Ways To Import.srt
15. While Loops.srt
11. Iterables.srt
33. global Keyword.srt
42. Set Comprehensions.srt
16. While Loops 2.srt
37. map().srt
4. Truthy vs Falsey.srt
23. Default Parameters and Keyword Arguments.srt
13. range().srt
28. Clean Code.srt
3. Indentation In Python.srt
17. break, continue, pass.srt
26. Methods vs Functions.srt
38. filter().srt
43. Exercise Comprehensions.srt
22. Parameters and Arguments.srt
5. Ternary Operator.srt
35. Why Do We Need Scope.srt
30. Exercise Functions.srt
14. enumerate().srt
6. Short Circuiting.srt
20. Exercise Find Duplicates.srt
27. Docstrings.srt
34. nonlocal Keyword.srt
31. Scope.srt
12. Exercise Tricky Counter.srt
39. zip().srt
1. Breaking The Flow.srt
44. Python Exam Testing Your Understanding.html
50. Next Steps.html
51. Bonus Resource Python Cheatsheet.html
46. Quick Note Upcoming Videos.html
25. Exercise Tesla.html
4.1 Truthy vs Falsey Stackoverflow.html
30.1 Solution Repl.html
20.1 Solution Repl.html
43.2 Solution Repl.html
43.1 Exercise Repl.html
18.1 Exercise Repl.html
18.2 Solution Repl.html
34.1 Solution Repl.html
12.1 Solution Repl.html
45. Modules in Python.mp4
2. Conditional Logic.mp4
48. Packages in Python.mp4
36. Pure Functions.mp4
24. return.mp4
41. List Comprehensions.mp4
47. Optional PyCharm.mp4
40. reduce().mp4
19. DEVELOPER FUNDAMENTALS IV.mp4
18. Our First GUI.mp4
21. Functions.mp4
49. Different Ways To Import.mp4
8. Exercise Logical Operators.mp4
11. Iterables.mp4
29. args and kwargs.mp4
4. Truthy vs Falsey.mp4
37. map().mp4
23. Default Parameters and Keyword Arguments.mp4
32. Scope Rules.mp4
33. global Keyword.mp4
42. Set Comprehensions.mp4
10. For Loops.mp4
9. is vs ==.mp4
26. Methods vs Functions.mp4
13. range().mp4
7. Logical Operators.mp4
15. While Loops.mp4
3. Indentation In Python.mp4
16. While Loops 2.mp4
14. enumerate().mp4
38. filter().mp4
22. Parameters and Arguments.mp4
17. break, continue, pass.mp4
43. Exercise Comprehensions.mp4
30. Exercise Functions.mp4
39. zip().mp4
1. Breaking The Flow.mp4
20. Exercise Find Duplicates.mp4
31. Scope.mp4
5. Ternary Operator.mp4
28. Clean Code.mp4
6. Short Circuiting.mp4
35. Why Do We Need Scope.mp4
34. nonlocal Keyword.mp4
27. Docstrings.mp4
12. Exercise Tricky Counter.mp4
13. Data Engineering
9. Optional OLTP Databases.srt
7. Types Of Databases.srt
2. What Is Data.srt
4. What Is A Data Engineer 2.srt
5. What Is A Data Engineer 3.srt
13. Kafka and Stream Processing.srt
3. What Is A Data Engineer.srt
11. Hadoop, HDFS and MapReduce.srt
1. Data Engineering Introduction.srt
6. What Is A Data Engineer 4.srt
12. Apache Spark and Apache Flink.srt
2.1 Kaggle.html
7.1 OLTP vs OLAP.html
7.2 A Primer on ACID Transactions.html
8. Quick Note Upcoming Video.html
10. Optional Learn SQL.html
9. Optional OLTP Databases.mp4
2. What Is Data.mp4
7. Types Of Databases.mp4
5. What Is A Data Engineer 3.mp4
4. What Is A Data Engineer 2.mp4
13. Kafka and Stream Processing.mp4
3. What Is A Data Engineer.mp4
6. What Is A Data Engineer 4.mp4
1. Data Engineering Introduction.mp4
11. Hadoop, HDFS and MapReduce.mp4
12. Apache Spark and Apache Flink.mp4
15. Storytelling + Communication How To Present Your Work
5. Weekend Project Principle.srt
4. Communicating With Co-Workers.srt
2. Communicating Your Work.srt
3. Communicating With Managers.srt
6. Communicating With Outside World.srt
7. Storytelling.srt
8. Communicating and sharing your work Further reading.html
2.1 How to Think About Communicating and Sharing Your Work (blog post).html
6.1 fast_template by fast.ai (a template you can use for your blog on GitHub Pages).html
6.2 Devblog by Hashnode (an easy and free way to create a blog you own).html
5. Weekend Project Principle.mp4
2. Communicating Your Work.mp4
4. Communicating With Co-Workers.mp4
3. Communicating With Managers.mp4
6. Communicating With Outside World.mp4
7. Storytelling.mp4
1. Section Overview.mp4
1. Section Overview.srt
10. Supervised Learning Classification + Regression
1. Milestone Projects!.html
20. Where To Go From Here
2. Thank You.srt
1. Become An Alumni.html
3. Course Review.html
4. The Final Challenge.html
2. Thank You.mp4
21. BONUS SECTION
1. Bonus Lecture.html
19. Bonus Learn Advanced Statistics and Mathematics for FREE!
1. Statistics and Mathematics.html
TutsNode.com.txt
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