Complete Machine Learning Data Science Bootcamp 2021
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Complete Machine Learning Data Science Bootcamp 2021
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Complete Machine Learning Data Science Bootcamp 2021
8. Windows Environment Setup 2.mp4 -
READ_ME.txt -
1. Course Outline.mp4 -
1. Course Outline.srt -
2. Join Our Online Classroom!.html -
3. Exercise Meet The Community.html -
4. Your First Day.mp4 -
4. Your First Day.srt -
READ_ME.txt -
1. What Is Machine Learning.mp4 -
1. What Is Machine Learning.srt -
2. AIMachine LearningData Science.mp4 -
2. AIMachine LearningData Science.srt -
3. Exercise Machine Learning Playground.mp4 -
3. Exercise Machine Learning Playground.srt -
3.1 Teachable Machine.html -
4. How Did We Get Here.mp4 -
4. How Did We Get Here.srt -
5. Exercise YouTube Recommendation Engine.mp4 -
5. Exercise YouTube Recommendation Engine.srt -
5.1 Machine Learning Playground.html -
6. Types of Machine Learning.mp4 -
6. Types of Machine Learning.srt -
7. Are You Getting It Yet.html -
8. What Is Machine Learning Round 2.mp4 -
8. What Is Machine Learning Round 2.srt -
9. Section Review.mp4 -
9. Section Review.srt -
1. Section Overview.mp4 -
1. Section Overview.srt -
2. Introducing Our Framework.mp4 -
2. Introducing Our Framework.srt -
3. 6 Step Machine Learning Framework.mp4 -
3. 6 Step Machine Learning Framework.srt -
3.1 A 6 Step Field Guide for Machine Learning Modelling (blog post).html -
4. Types of Machine Learning Problems.mp4 -
4. Types of Machine Learning Problems.srt -
5. Types of Data.mp4 -
5. Types of Data.srt -
6. Types of Evaluation.mp4 -
6. Types of Evaluation.srt -
7. Features In Data.mp4 -
7. Features In Data.srt -
8. Modelling - Splitting Data.mp4 -
8. Modelling - Splitting Data.srt -
9. Modelling - Picking the Model.mp4 -
9. Modelling - Picking the Model.srt -
10. Modelling - Tuning.mp4 -
10. Modelling - Tuning.srt -
11. Modelling - Comparison.mp4 -
11. Modelling - Comparison.srt -
12. Overfitting and Underfitting Definitions.html -
13. Experimentation.mp4 -
13. Experimentation.srt -
14. Tools We Will Use.mp4 -
14. Tools We Will Use.srt -
15. Optional Elements of AI.html -
1. The 2 Paths.mp4 -
1. The 2 Paths.srt -
2. Python + Machine Learning Monthly.html -
3. Endorsements On LinkedIN.html -
1. Section Overview.mp4 -
1. Section Overview.srt -
2. Introducing Our Tools.mp4 -
2. Introducing Our Tools.srt -
3. What is Conda.mp4 -
3. What is Conda.srt -
3.1 Getting started with Conda (documentation).html -
3.2 conda-cheatsheet.pdf -
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 -
4. Conda Environments.mp4 -
4. Conda Environments.srt -
5. Mac Environment Setup.mp4 -
5. Mac Environment Setup.srt -
5.1 Miniconda download documentation.html -
6. Mac Environment Setup 2.mp4 -
6. Mac Environment Setup 2.srt -
7. Windows Environment Setup.mp4 -
7. Windows Environment Setup.srt -
7.1 Miniconda download documentation.html -
READ_ME.txt -
8. Windows Environment Setup 2.srt -
9. Linux Environment Setup.html -
10. Sharing your Conda Environment.html -
10.1 Conda documentation on sharing an environment.html -
11. Jupyter Notebook Walkthrough.mp4 -
11. Jupyter Notebook Walkthrough.srt -
11.1 6-step-ml-framework.png -
11.2 heart-disease.csv -
11.3 Dataquest Jupyter Notebook for Beginners Tutorial.html -
11.4 Jupyter Notebook documentation.html -
12. Jupyter Notebook Walkthrough 2.mp4 -
12. Jupyter Notebook Walkthrough 2.srt -
13. Jupyter Notebook Walkthrough 3.mp4 -
13. Jupyter Notebook Walkthrough 3.srt -
1. Section Overview.mp4 -
1. Section Overview.srt -
2. Downloading Workbooks and Assignments.html -
3. Pandas Introduction.mp4 -
3. Pandas Introduction.srt -
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 -
4. Series, Data Frames and CSVs.mp4 -
4. Series, Data Frames and CSVs.srt -
4.1 pandas-anatomy-of-a-dataframe.png -
5. Data from URLs.html -
6. Describing Data with Pandas.mp4 -
6. Describing Data with Pandas.srt -
7. Selecting and Viewing Data with Pandas.mp4 -
7. Selecting and Viewing Data with Pandas.srt -
7.1 car-sales.csv -
8. Selecting and Viewing Data with Pandas Part 2.mp4 -
8. Selecting and Viewing Data with Pandas Part 2.srt -
9. Manipulating Data.mp4 -
9. Manipulating Data.srt -
9.1 Jake VanderPlas's Data Manipulation with Pandas.html -
9.2 car-sales-missing-data.csv -
10. Manipulating Data 2.mp4 -
10. Manipulating Data 2.srt -
10.1 pandas-anatomy-of-a-dataframe.png -
11. Manipulating Data 3.mp4 -
11. Manipulating Data 3.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 -
13. How To Download The Course Assignments.mp4 -
13. How To Download The Course Assignments.srt -
13.1 Google Colab.html -
13.2 Course notebooks - Github.html -
1. Section Overview.mp4 -
1. Section Overview.srt -
2. NumPy Introduction.mp4 -
2. NumPy Introduction.srt -
2.1 Introduction to NumPy Jupyter Notebook (with annotations).html -
2.2 NumPy Documentation.html -
2.3 Introduction to NumPy Jupyter Notebook (from the upcoming videos).html -
3. Quick Note Correction In Next Video.html -
4. NumPy DataTypes and Attributes.mp4 -
4. NumPy DataTypes and Attributes.srt -
5. Creating NumPy Arrays.mp4 -
5. Creating NumPy Arrays.srt -
6. NumPy Random Seed.mp4 -
6. NumPy Random Seed.srt -
7. Viewing Arrays and Matrices.mp4 -
7. Viewing Arrays and Matrices.srt -
8. Manipulating Arrays.mp4 -
8. Manipulating Arrays.srt -
8.1 Standard deviation and variance explained.html -
9. Manipulating Arrays 2.mp4 -
9. Manipulating Arrays 2.srt -
9.1 Standard deviation and variance explained.html -
10. Standard Deviation and Variance.mp4 -
10. Standard Deviation and Variance.srt -
10.1 Standard deviation and variance explained.html -
11. Reshape and Transpose.mp4 -
11. Reshape and Transpose.srt -
12. Dot Product vs Element Wise.mp4 -
12. Dot Product vs Element Wise.srt -
12.1 Matrix Multiplication Explained.html -
13. Exercise Nut Butter Store Sales.mp4 -
13. Exercise Nut Butter Store Sales.srt -
14. Comparison Operators.mp4 -
14. Comparison Operators.srt -
15. Sorting Arrays.mp4 -
15. Sorting Arrays.srt -
16. Turn Images Into NumPy Arrays.mp4 -
16. Turn Images Into NumPy Arrays.srt -
16.1 Introduction to NumPy Jupyter Notebook (from the videos).html -
16.2 Introduction to NumPy Jupyter Notebook (with annotations).html -
16.3 numpy-images.zip -
17. Assignment NumPy Practice.html -
18. Optional Extra NumPy resources.html -
1. Section Overview.mp4 -
1. Section Overview.srt -
2. Matplotlib Introduction.mp4 -
2. Matplotlib Introduction.srt -
2.1 Introduction to Matplotlib Jupyter Notebook (from the upcoming videos).html -
2.2 Matplotlib Documentation.html -
3. Importing And Using Matplotlib.mp4 -
3. Importing And Using Matplotlib.srt -
4. Anatomy Of A Matplotlib Figure.mp4 -
4. Anatomy Of A Matplotlib Figure.srt -
4.1 matplotlib-anatomy-of-a-plot-with-code.png -
4.2 matplotlib-anatomy-of-a-plot.png -
5. Scatter Plot And Bar Plot.mp4 -
5. Scatter Plot And Bar Plot.srt -
6. Histograms And Subplots.mp4 -
6. Histograms And Subplots.srt -
7. Subplots Option 2.mp4 -
7. Subplots Option 2.srt -
8. Quick Tip Data Visualizations.mp4 -
8. Quick Tip Data Visualizations.srt -
9. Plotting From Pandas DataFrames.mp4 -
9. Plotting From Pandas DataFrames.srt -
10. Quick Note Regular Expressions.html -
11. Plotting From Pandas DataFrames 2.mp4 -
11. Plotting From Pandas DataFrames 2.srt -
12. Plotting from Pandas DataFrames 3.mp4 -
12. Plotting from Pandas DataFrames 3.srt -
13. Plotting from Pandas DataFrames 4.mp4 -
13. Plotting from Pandas DataFrames 4.srt -
13.1 heart-disease.csv -
14. Plotting from Pandas DataFrames 5.mp4 -
14. Plotting from Pandas DataFrames 5.srt -
15. Plotting from Pandas DataFrames 6.mp4 -
15. Plotting from Pandas DataFrames 6.srt -
16. Plotting from Pandas DataFrames 7.mp4 -
16. Plotting from Pandas DataFrames 7.srt -
17. Customizing Your Plots.mp4 -
17. Customizing Your Plots.srt -
18. Customizing Your Plots 2.mp4 -
18. Customizing Your Plots 2.srt -
19. Saving And Sharing Your Plots.mp4 -
19. Saving And Sharing Your Plots.srt -
19.1 Introduction to Matplotlib Notebook (from the videos).html -
20. Assignment Matplotlib Practice.html -
1. Section Overview.mp4 -
1. Section Overview.srt -
2. Scikit-learn Introduction.mp4 -
2. Scikit-learn Introduction.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 -
4. Refresher What Is Machine Learning.mp4 -
4. Refresher What Is Machine Learning.srt -
5. Quick Note Upcoming Videos.html -
6. Scikit-learn Cheatsheet.mp4 -
6. Scikit-learn Cheatsheet.srt -
6.1 Scikit-Learn Reference Notebook.html -
7. Typical scikit-learn Workflow.mp4 -
7. Typical scikit-learn Workflow.srt -
7.1 Example Scikit-Learn Workflow Notebook.html -
8. Optional Debugging Warnings In Jupyter.mp4 -
8. Optional Debugging Warnings In Jupyter.srt -
9. Getting Your Data Ready Splitting Your Data.mp4 -
9. Getting Your Data Ready Splitting Your Data.srt -
9.1 scikit-learn-data.zip -
10. Quick Tip Clean, Transform, Reduce.mp4 -
10. Quick Tip Clean, Transform, Reduce.srt -
11. Getting Your Data Ready Convert Data To Numbers.mp4 -
11. Getting Your Data Ready Convert Data To Numbers.srt -
12. Getting Your Data Ready Handling Missing Values With Pandas.mp4 -
12. Getting Your Data Ready Handling Missing Values With Pandas.srt -
13. Extension Feature Scaling.html -
14. Note Correction in the upcoming video (splitting data).html -
15. Getting Your Data Ready Handling Missing Values With Scikit-learn.mp4 -
15. Getting Your Data Ready Handling Missing Values With Scikit-learn.srt -
16. Choosing The Right Model For Your Data.mp4 -
16. Choosing The Right Model For Your Data.srt -
16.1 Scikit-Learn machine learning map (how to choose the right machine learning model).html -
17. Choosing The Right Model For Your Data 2 (Regression).mp4 -
17. Choosing The Right Model For Your Data 2 (Regression).srt -
18. Quick Note Decision Trees.html -
19. Quick Tip How ML Algorithms Work.mp4 -
19. Quick Tip How ML Algorithms Work.srt -
20. Choosing The Right Model For Your Data 3 (Classification).mp4 -
20. Choosing The Right Model For Your Data 3 (Classification).srt -
21. Fitting A Model To The Data.mp4 -
21. Fitting A Model To The Data.srt -
22. Making Predictions With Our Model.mp4 -
22. Making Predictions With Our Model.srt -
23. predict() vs predict_proba().mp4 -
23. predict() vs predict_proba().srt -
24. Making Predictions With Our Model (Regression).mp4 -
24. Making Predictions With Our Model (Regression).srt -
25. Evaluating A Machine Learning Model (Score).mp4 -
25. Evaluating A Machine Learning Model (Score).srt -
26. Evaluating A Machine Learning Model 2 (Cross Validation).mp4 -
26. Evaluating A Machine Learning Model 2 (Cross Validation).srt -
27. Evaluating A Classification Model 1 (Accuracy).mp4 -
27. Evaluating A Classification Model 1 (Accuracy).srt -
28. Evaluating A Classification Model 2 (ROC Curve).mp4 -
28. Evaluating A Classification Model 2 (ROC Curve).srt -
29. Evaluating A Classification Model 3 (ROC Curve).mp4 -
29. Evaluating A Classification Model 3 (ROC Curve).srt -
30. Reading Extension ROC Curve + AUC.html -
31. Evaluating A Classification Model 4 (Confusion Matrix).mp4 -
31. Evaluating A Classification Model 4 (Confusion Matrix).srt -
31.1 Notebook from video with updated confusion matrix labels.html -
32. Evaluating A Classification Model 5 (Confusion Matrix).mp4 -
32. Evaluating A Classification Model 5 (Confusion Matrix).srt -
33. Evaluating A Classification Model 6 (Classification Report).mp4 -
33. Evaluating A Classification Model 6 (Classification Report).srt -
34. Evaluating A Regression Model 1 (R2 Score).mp4 -
34. Evaluating A Regression Model 1 (R2 Score).srt -
35. Evaluating A Regression Model 2 (MAE).mp4 -
35. Evaluating A Regression Model 2 (MAE).srt -
36. Evaluating A Regression Model 3 (MSE).mp4 -
36. Evaluating A Regression Model 3 (MSE).srt -
37. Machine Learning Model Evaluation.html -
38. Evaluating A Model With Cross Validation and Scoring Parameter.mp4 -
38. Evaluating A Model With Cross Validation and Scoring Parameter.srt -
39. Evaluating A Model With Scikit-learn Functions.mp4 -
39. Evaluating A Model With Scikit-learn Functions.srt -
40. Improving A Machine Learning Model.mp4 -
40. Improving A Machine Learning Model.srt -
41. Tuning Hyperparameters.mp4 -
41. Tuning Hyperparameters.srt -
42. Tuning Hyperparameters 2.mp4 -
42. Tuning Hyperparameters 2.srt -
43. Tuning Hyperparameters 3.mp4 -
43. Tuning Hyperparameters 3.srt -
44. Note Metric Comparison Improvement.html -
45. Quick Tip Correlation Analysis.mp4 -
45. Quick Tip Correlation Analysis.srt -
46. Saving And Loading A Model.mp4 -
46. Saving And Loading A Model.srt -
47. Saving And Loading A Model 2.mp4 -
47. Saving And Loading A Model 2.srt -
48. Putting It All Together.mp4 -
48. Putting It All Together.srt -
48.1 Reading extension Scikit-Learn's Pipeline class explained.html -
49. Putting It All Together 2.mp4 -
49. Putting It All Together 2.srt -
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 -
car-sales-extended-missing-data.csv -
car-sales-extended.csv -
car-sales-missing-data.csv -
heart-disease.csv -
1. Milestone Projects!.html -
1. Section Overview.mp4 -
1. Section Overview.srt -
2. Project Overview.mp4 -
2. Project Overview.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 -
3. Project Environment Setup.mp4 -
3. Project Environment Setup.srt -
4. Optional Windows Project Environment Setup.mp4 -
4. Optional Windows Project Environment Setup.srt -
5. Step 1~4 Framework Setup.mp4 -
5. Step 1~4 Framework Setup.srt -
6. Getting Our Tools Ready.mp4 -
6. Getting Our Tools Ready.srt -
7. Exploring Our Data.mp4 -
7. Exploring Our Data.srt -
7.1 heart-disease.csv -
8. Finding Patterns.mp4 -
8. Finding Patterns.srt -
9. Finding Patterns 2.mp4 -
9. Finding Patterns 2.srt -
10. Finding Patterns 3.mp4 -
10. Finding Patterns 3.srt -
11. Preparing Our Data For Machine Learning.mp4 -
11. Preparing Our Data For Machine Learning.srt -
12. Choosing The Right Models.mp4 -
12. Choosing The Right Models.srt -
13. Experimenting With Machine Learning Models.mp4 -
13. Experimenting With Machine Learning Models.srt -
14. TuningImproving Our Model.mp4 -
14. TuningImproving Our Model.srt -
15. Tuning Hyperparameters.mp4 -
15. Tuning Hyperparameters.srt -
16. Tuning Hyperparameters 2.mp4 -
16. Tuning Hyperparameters 2.srt -
17. Tuning Hyperparameters 3.mp4 -
17. Tuning Hyperparameters 3.srt -
18. Quick Note Confusion Matrix Labels.html -
19. Evaluating Our Model.mp4 -
19. Evaluating Our Model.srt -
20. Evaluating Our Model 2.mp4 -
20. Evaluating Our Model 2.srt -
21. Evaluating Our Model 3.mp4 -
21. Evaluating Our Model 3.srt -
22. Finding The Most Important Features.mp4 -
22. Finding The Most Important Features.srt -
23. Reviewing The Project.mp4 -
23. Reviewing The Project.srt -
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.mp4 -
1. Section Overview.srt -
2. Project Overview.mp4 -
2. Project 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 -
3. Project Environment Setup.mp4 -
3. Project Environment Setup.srt -
4. Step 1~4 Framework Setup.mp4 -
4. Step 1~4 Framework Setup.srt -
5. Downloading the data for the next two projects.html -
6. Exploring Our Data.mp4 -
6. Exploring Our Data.srt -
7. Exploring Our Data 2.mp4 -
7. Exploring Our Data 2.srt -
8. Feature Engineering.mp4 -
8. Feature Engineering.srt -
9. Turning Data Into Numbers.mp4 -
9. Turning Data Into Numbers.srt -
10. Filling Missing Numerical Values.mp4 -
10. Filling Missing Numerical Values.srt -
10.1 Pandas Categorical Datatype Documentation.html -
11. Filling Missing Categorical Values.mp4 -
11. Filling Missing Categorical Values.srt -
12. Fitting A Machine Learning Model.mp4 -
12. Fitting A Machine Learning Model.srt -
13. Splitting Data.mp4 -
13. Splitting Data.srt -
14. Challenge What's wrong with splitting data after filling it.html -
15. Custom Evaluation Function.mp4 -
15. Custom Evaluation Function.srt -
16. Reducing Data.mp4 -
16. Reducing Data.srt -
17. RandomizedSearchCV.mp4 -
17. RandomizedSearchCV.srt -
18. Improving Hyperparameters.mp4 -
18. Improving Hyperparameters.srt -
19. Preproccessing Our Data.mp4 -
19. Preproccessing Our Data.srt -
20. Making Predictions.mp4 -
20. Making Predictions.srt -
21. Feature Importance.mp4 -
21. Feature Importance.srt -
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 -
1. Data Engineering Introduction.mp4 -
1. Data Engineering Introduction.srt -
2. What Is Data.mp4 -
2. What Is Data.srt -
2.1 Kaggle.html -
3. What Is A Data Engineer.mp4 -
3. What Is A Data Engineer.srt -
4. What Is A Data Engineer 2.mp4 -
4. What Is A Data Engineer 2.srt -
5. What Is A Data Engineer 3.mp4 -
5. What Is A Data Engineer 3.srt -
6. What Is A Data Engineer 4.mp4 -
6. What Is A Data Engineer 4.srt -
7. Types Of Databases.mp4 -
7. Types Of Databases.srt -
7.1 OLTP vs OLAP.html -
7.2 A Primer on ACID Transactions.html -
8. Quick Note Upcoming Video.html -
9. Optional OLTP Databases.mp4 -
9. Optional OLTP Databases.srt -
10. Optional Learn SQL.html -
11. Hadoop, HDFS and MapReduce.mp4 -
11. Hadoop, HDFS and MapReduce.srt -
12. Apache Spark and Apache Flink.mp4 -
12. Apache Spark and Apache Flink.srt -
13. Kafka and Stream Processing.mp4 -
13. Kafka and Stream Processing.srt -
1. Section Overview.mp4 -
1. Section Overview.srt -
2. Deep Learning and Unstructured Data.mp4 -
2. Deep Learning and Unstructured Data.srt -
3. Setting Up With Google.html -
4. Setting Up Google Colab.mp4 -
4. Setting Up Google Colab.srt -
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 -
4.5 End-to-end Dog Vision Notebook (the project we'll be working through).html -
5. Google Colab Workspace.mp4 -
5. Google Colab Workspace.srt -
5.1 Google Colab FAQ (things you should know about Google Colab).html -
5.2 Google Colab (our workspace for the upcoming project).html -
6. Uploading Project Data.mp4 -
6. Uploading Project Data.srt -
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 -
7. Setting Up Our Data.mp4 -
7. Setting Up Our Data.srt -
8. Setting Up Our Data 2.mp4 -
8. Setting Up Our Data 2.srt -
9. Importing TensorFlow 2.mp4 -
9. Importing TensorFlow 2.srt -
10. Optional TensorFlow 2.0 Default Issue.mp4 -
10. Optional TensorFlow 2.0 Default Issue.srt -
10.1 Loading TensorFlow 2.0 into a Colab Notebook (if it isn't the default).html -
11. Using A GPU.mp4 -
11. Using A GPU.srt -
11.1 Google Colab example GPU usage.html -
12. Optional GPU and Google Colab.mp4 -
12. Optional GPU and Google Colab.srt -
12.1 Introduction to Google Colab example notebook.html -
12.2 Google Colab Example of GPU speed up versus CPU.html -
13. Optional Reloading Colab Notebook.mp4 -
13. Optional Reloading Colab Notebook.srt -
14. Loading Our Data Labels.mp4 -
14. Loading Our Data Labels.srt -
14.1 Documentation on how many images Google recommends for image problems.html -
15. Preparing The Images.mp4 -
15. Preparing The Images.srt -
16. Turning Data Labels Into Numbers.mp4 -
16. Turning Data Labels Into Numbers.srt -
17. Creating Our Own Validation Set.mp4 -
17. Creating Our Own Validation Set.srt -
17.1 Blog post by Rachel Thomas (of fast.ai) on how and why you should create a validation set.html -
18. Preprocess Images.mp4 -
18. Preprocess Images.srt -
18.1 Documentation for loading images in TensorFlow.html -
18.2 TensorFlow guidelines for loading all kinds of data (turning your data into Tensors).html -
19. Preprocess Images 2.mp4 -
19. Preprocess Images 2.srt -
20. Turning Data Into Batches.mp4 -
20. Turning Data Into Batches.srt -
21. Turning Data Into Batches 2.mp4 -
21. Turning Data Into Batches 2.srt -
21.1 Yann LeCun's (OG of deep learning) Tweet on Batch Sizes.html -
22. Visualizing Our Data.mp4 -
22. Visualizing Our Data.srt -
23. Preparing Our Inputs and Outputs.mp4 -
23. Preparing Our Inputs and Outputs.srt -
23.1 TensorFlow Hub (resource for pre-trained deep learning models and more).html -
24. Optional How machines learn and what's going on behind the scenes.html -
25. Building A Deep Learning Model.mp4 -
25. Building A Deep Learning Model.srt -
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 -
25.3 MobileNetV2 (the model we're using) on TensorFlow Hub.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 -
26. Building A Deep Learning Model 2.mp4 -
26. Building A Deep Learning Model 2.srt -
26.1 Keras in TensorFlow Overview Documentation.html -
27. Building A Deep Learning Model 3.mp4 -
27. Building A Deep Learning Model 3.srt -
27.1 The Softmax Function (activation function we use in our model).html -
27.2 Step by step breakdown of a convolutional neural network (what MobileNetV2 is made of).html -
27.3 MobileNetV2 (the model we're using) architecture explanation by Sik-Ho Tsang.html -
28. Building A Deep Learning Model 4.mp4 -
28. Building A Deep Learning Model 4.srt -
28.1 [Article] How to choose loss & activation functions when building a deep learning model.html -
29. Summarizing Our Model.mp4 -
29. Summarizing Our Model.srt -
30. Evaluating Our Model.mp4 -
30. Evaluating Our Model.srt -
30.1 TensorBoard Callback Documentation.html -
31. Preventing Overfitting.mp4 -
31. Preventing Overfitting.srt -
31.1 Early Stopping Callback (a way to stop your model from training when it stops improving) Documentation.html -
32. Training Your Deep Neural Network.mp4 -
32. Training Your Deep Neural Network.srt -
33. Evaluating Performance With TensorBoard.mp4 -
33. Evaluating Performance With TensorBoard.srt -
34. Make And Transform Predictions.mp4 -
34. Make And Transform Predictions.srt -
35. Transform Predictions To Text.mp4 -
35. Transform Predictions To Text.srt -
35.1 TensorFlow documentation for the unbatch() function.html -
36. Visualizing Model Predictions.mp4 -
36. Visualizing Model Predictions.srt -
37. Visualizing And Evaluate Model Predictions 2.mp4 -
37. Visualizing And Evaluate Model Predictions 2.srt -
38. Visualizing And Evaluate Model Predictions 3.mp4 -
38. Visualizing And Evaluate Model Predictions 3.srt -
39. Saving And Loading A Trained Model.mp4 -
39. Saving And Loading A Trained Model.srt -
40. Training Model On Full Dataset.mp4 -
40. Training Model On Full Dataset.srt -
41. Making Predictions On Test Images.mp4 -
41. Making Predictions On Test Images.srt -
41.1 Dog Vision Prediction Probabilities Array.html -
42. Submitting Model to Kaggle.mp4 -
42. Submitting Model to Kaggle.srt -
42.1 Dog Vision Predictions with MobileNetV2 Ready for Kaggle Submission.html -
43. Making Predictions On Our Images.mp4 -
43. Making Predictions On Our Images.srt -
43.1 End-to-end Dog Vision Notebook (with annotations).html -
43.2 End-to-end Dog Vision Notebook (from the videos).html -
44. Finishing Dog Vision Where to next.html -
1. Section Overview.mp4 -
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2. Communicating Your Work.mp4 -
2. Communicating Your Work.srt -
2.1 How to Think About Communicating and Sharing Your Work (blog post).html -
3. Communicating With Managers.mp4 -
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4. Communicating With Co-Workers.mp4 -
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5. Weekend Project Principle.mp4 -
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6. Communicating With Outside World.mp4 -
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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 -
7. Storytelling.mp4 -
7. Storytelling.srt -
8. Communicating and sharing your work Further reading.html -
1. Endorsements On LinkedIn.html -
2. Quick Note Upcoming Video.html -
3. What If I Don't Have Enough Experience.mp4 -
3. What If I Don't Have Enough Experience.srt -
4. Learning Guideline.html -
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10. CWD Git + Github 2.mp4 -
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11. Contributing To Open Source.mp4 -
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12. Contributing To Open Source 2.mp4 -
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13. Coding Challenges.html -
14. Exercise Contribute To Open Source.html -
1. What Is A Programming Language.mp4 -
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2. Python Interpreter.mp4 -
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2.1 python.org.html -
3. How To Run Python Code.mp4 -
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3.1 Glot.io.html -
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4. Our First Python Program.mp4 -
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5. Latest Version Of Python.mp4 -
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6. Python 2 vs Python 3.mp4 -
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6.1 Python 2 vs Python 3.html -
6.2 The Story of Python.html -
6.3 Python 2 vs Python 3 - another one.html -
7. Exercise How Does Python Work.mp4 -
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8. Learning Python.mp4 -
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10. How To Succeed.html -
11. Numbers.mp4 -
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11.1 Floating point numbers.html -
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13. DEVELOPER FUNDAMENTALS I.mp4 -
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14. Operator Precedence.mp4 -
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14.1 Exercise Repl.html -
15. Exercise Operator Precedence.html -
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16. Optional bin() and complex.mp4 -
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17. Variables.mp4 -
17. Variables.srt -
17.1 Python Keywords.html -
18. Expressions vs Statements.mp4 -
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19. Augmented Assignment Operator.mp4 -
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20. Strings.mp4 -
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24. Formatted Strings.mp4 -
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25. String Indexes.mp4 -
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26. Immutability.mp4 -
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27. Built-In Functions + Methods.mp4 -
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27.1 String Methods.html -
27.2 Built in Functions.html -
28. Booleans.mp4 -
28. Booleans.srt -
29. Exercise Type Conversion.mp4 -
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30. DEVELOPER FUNDAMENTALS II.mp4 -
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30.1 Python Comments Best Practices.html -
31. Exercise Password Checker.mp4 -
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32. Lists.mp4 -
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33. List Slicing.mp4 -
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34. Matrix.mp4 -
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35. List Methods.mp4 -
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36. List Methods 2.mp4 -
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37. List Methods 3.mp4 -
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38. Common List Patterns.mp4 -
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40. None.mp4 -
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42. DEVELOPER FUNDAMENTALS III.mp4 -
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43. Dictionary Keys.mp4 -
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44. Dictionary Methods.mp4 -
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45. Dictionary Methods 2.mp4 -
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46. Tuples.mp4 -
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48. Sets.mp4 -
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1. Breaking The Flow.mp4 -
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4. Truthy vs Falsey.mp4 -
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5. Ternary Operator.mp4 -
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6. Short Circuiting.mp4 -
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7. Logical Operators.mp4 -
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8. Exercise Logical Operators.mp4 -
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10. For Loops.mp4 -
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11. Iterables.mp4 -
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12. Exercise Tricky Counter.mp4 -
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13. range().mp4 -
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14. enumerate().mp4 -
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15. While Loops.mp4 -
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17. break, continue, pass.mp4 -
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18. Our First GUI.mp4 -
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18.1 Exercise Repl.html -
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19. DEVELOPER FUNDAMENTALS IV.mp4 -
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20. Exercise Find Duplicates.mp4 -
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21. Functions.mp4 -
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22. Parameters and Arguments.mp4 -
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23. Default Parameters and Keyword Arguments.mp4 -
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24. return.mp4 -
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25. Exercise Tesla.html -
26. Methods vs Functions.mp4 -
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27. Docstrings.mp4 -
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28. Clean Code.mp4 -
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29. args and kwargs.mp4 -
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30. Exercise Functions.mp4 -
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31. Scope.mp4 -
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32. Scope Rules.mp4 -
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33. global Keyword.mp4 -
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34. nonlocal Keyword.mp4 -
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34.1 Solution Repl.html -
35. Why Do We Need Scope.mp4 -
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36. Pure Functions.mp4 -
36. Pure Functions.srt -
37. map().mp4 -
37. map().srt -
38. filter().mp4 -
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39. zip().mp4 -
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40. reduce().mp4 -
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41. List Comprehensions.mp4 -
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42. Set Comprehensions.mp4 -
42. Set Comprehensions.srt -
43. Exercise Comprehensions.mp4 -
43. Exercise Comprehensions.srt -
43.1 Exercise Repl.html -
43.2 Solution Repl.html -
44. Python Exam Testing Your Understanding.html -
45. Modules in Python.mp4 -
45. Modules in Python.srt -
46. Quick Note Upcoming Videos.html -
47. Optional PyCharm.mp4 -
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48. Packages in Python.mp4 -
48. Packages in Python.srt -
49. Different Ways To Import.mp4 -
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50. Next Steps.html -
51. Bonus Resource Python Cheatsheet.html -
1. Statistics and Mathematics.html -
1. Become An Alumni.html -
2. Thank You.mp4 -
2. Thank You.srt -
3. Course Review.html -
4. The Final Challenge.html -
1. Bonus Lecture.html -
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8. Windows Environment Setup 2.mp4 -
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7. Typical scikit-learn Workflow.mp4 -
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7. Typical scikit-learn Workflow.srt -
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7.1 Example Scikit-Learn Workflow Notebook.html -
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8. Optional Debugging Warnings In Jupyter.mp4 -
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9. Getting Your Data Ready Splitting Your Data.mp4 -
63.7 MB
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10. Quick Tip Clean, Transform, Reduce.mp4 -
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11. Getting Your Data Ready Convert Data To Numbers.mp4 -
135.0 MB
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12. Getting Your Data Ready Handling Missing Values With Pandas.mp4 -
104.8 MB
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13. Extension Feature Scaling.html -
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15. Getting Your Data Ready Handling Missing Values With Scikit-learn.mp4 -
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16. Choosing The Right Model For Your Data.mp4 -
143.3 MB
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16.1 Scikit-Learn machine learning map (how to choose the right machine learning model).html -
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17. Choosing The Right Model For Your Data 2 (Regression).mp4 -
86.9 MB
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18. Quick Note Decision Trees.html -
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11.1 MB
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20. Choosing The Right Model For Your Data 3 (Classification).mp4 -
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21. Fitting A Model To The Data.mp4 -
56.6 MB
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22. Making Predictions With Our Model.mp4 -
66.5 MB
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23. predict() vs predict_proba().mp4 -
54.3 MB
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24. Making Predictions With Our Model (Regression).mp4 -
44.9 MB
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25. Evaluating A Machine Learning Model (Score).mp4 -
87.1 MB
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26. Evaluating A Machine Learning Model 2 (Cross Validation).mp4 -
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66.0 MB
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50.6 MB
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33. Evaluating A Classification Model 6 (Classification Report).mp4 -
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34. Evaluating A Regression Model 1 (R2 Score).mp4 -
70.4 MB
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28.5 MB
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36. Evaluating A Regression Model 3 (MSE).mp4 -
54.9 MB
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37. Machine Learning Model Evaluation.html -
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38. Evaluating A Model With Cross Validation and Scoring Parameter.mp4 -
91.5 MB
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18.0 KB
39. Evaluating A Model With Scikit-learn Functions.mp4 -
94.8 MB
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40. Improving A Machine Learning Model.mp4 -
90.9 MB
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41. Tuning Hyperparameters.mp4 -
175.7 MB
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42. Tuning Hyperparameters 2.mp4 -
116.8 MB
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43. Tuning Hyperparameters 3.mp4 -
121.8 MB
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44. Note Metric Comparison Improvement.html -
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45. Quick Tip Correlation Analysis.mp4 -
16.9 MB
45. Quick Tip Correlation Analysis.srt -
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46. Saving And Loading A Model.mp4 -
52.6 MB
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47. Saving And Loading A Model 2.mp4 -
56.8 MB
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48. Putting It All Together.mp4 -
150.6 MB
48. Putting It All Together.srt -
29.6 KB
48.1 Reading extension Scikit-Learn's Pipeline class explained.html -
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116.9 MB
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49.1 Introduction to Scikit-Learn Jupyter Notebook (from the videos).html -
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50. Scikit-Learn Practice.html -
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car-sales-extended.csv -
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car-sales-missing-data.csv -
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heart-disease.csv -
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1. Milestone Projects!.html -
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10.2 MB
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2. Project Overview.mp4 -
34.4 MB
2. Project Overview.srt -
10.0 KB
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2.3 End-to-end Heart Disease Classification Notebook (same as in videos).html -
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100.8 MB
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4. Optional Windows Project Environment Setup.mp4 -
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5. Step 1~4 Framework Setup.mp4 -
105.5 MB
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6. Getting Our Tools Ready.mp4 -
79.4 MB
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7. Exploring Our Data.mp4 -
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10. Finding Patterns 3.mp4 -
137.9 MB
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11. Preparing Our Data For Machine Learning.mp4 -
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12. Choosing The Right Models.mp4 -
96.4 MB
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71.6 MB
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20. Evaluating Our Model 2.mp4 -
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21. Evaluating Our Model 3.mp4 -
64.8 MB
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22. Finding The Most Important Features.mp4 -
127.5 MB
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23. Reviewing The Project.mp4 -
86.1 MB
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23.1 End-to-end Heart Disease Classification Notebook (with annotations).html -
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207 bytes
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9.0 MB
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2. Project Overview.mp4 -
32.9 MB
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214 bytes
2.3 End-to-end Bluebook Bulldozer Regression Notebook (with annotations).html -
208 bytes
2.4 Kaggle Bluebook for Bulldozers Competition.html -
118 bytes
3. Project Environment Setup.mp4 -
101.3 MB
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4. Step 1~4 Framework Setup.mp4 -
85.7 MB
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12.4 KB
5. Downloading the data for the next two projects.html -
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6. Exploring Our Data.mp4 -
137.8 MB
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20.0 KB
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52.0 MB
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159.1 MB
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106.3 MB
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12. Fitting A Machine Learning Model.mp4 -
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13. Splitting Data.mp4 -
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13.5 KB
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103.4 MB
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16. Reducing Data.mp4 -
93.5 MB
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85.8 MB
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18. Improving Hyperparameters.mp4 -
79.3 MB
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19. Preproccessing Our Data.mp4 -
139.3 MB
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20. Making Predictions.mp4 -
79.2 MB
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21. Feature Importance.mp4 -
142.3 MB
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21.1 End-to-end Bluebook Bulldozer Regression Notebook (with annotations).html -
208 bytes
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214 bytes
1. Data Engineering Introduction.mp4 -
13.5 MB
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2. What Is Data.mp4 -
42.2 MB
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2.1 Kaggle.html -
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7. Types Of Databases.mp4 -
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7.1 OLTP vs OLAP.html -
126 bytes
7.2 A Primer on ACID Transactions.html -
117 bytes
8. Quick Note Upcoming Video.html -
481 bytes
9. Optional OLTP Databases.mp4 -
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2. Deep Learning and Unstructured Data.mp4 -
102.0 MB
2. Deep Learning and Unstructured Data.srt -
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3. Setting Up With Google.html -
568 bytes
4. Setting Up Google Colab.mp4 -
74.2 MB
4. Setting Up Google Colab.srt -
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4.1 Kaggle Dog Breed Identification Competition (the basis of our upcoming project).html -
119 bytes
4.2 Google Colab (our workspace for the upcoming project).html -
95 bytes
4.3 Google Colab IO example (how to get data in and out of your Colab notebook).html -
113 bytes
4.4 Introduction to Google Colab example notebook.html -
116 bytes
4.5 End-to-end Dog Vision Notebook (the project we'll be working through).html -
182 bytes
5. Google Colab Workspace.mp4 -
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5. Google Colab Workspace.srt -
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5.1 Google Colab FAQ (things you should know about Google Colab).html -
110 bytes
5.2 Google Colab (our workspace for the upcoming project).html -
95 bytes
6. Uploading Project Data.mp4 -
52.0 MB
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6.1 Kaggle Dog Breed Identification Competition Data.html -
115 bytes
6.2 Google Colab IO example (how to get data in and out of your Colab notebook).html -
113 bytes
7. Setting Up Our Data.mp4 -
42.3 MB
7. Setting Up Our Data.srt -
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8. Setting Up Our Data 2.mp4 -
20.9 MB
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9. Importing TensorFlow 2.mp4 -
116.8 MB
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28.1 MB
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129 bytes
11. Using A GPU.mp4 -
80.6 MB
11. Using A GPU.srt -
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11.1 Google Colab example GPU usage.html -
114 bytes
12. Optional GPU and Google Colab.mp4 -
45.9 MB
12. Optional GPU and Google Colab.srt -
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12.1 Introduction to Google Colab example notebook.html -
116 bytes
12.2 Google Colab Example of GPU speed up versus CPU.html -
114 bytes
13. Optional Reloading Colab Notebook.mp4 -
88.7 MB
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14. Loading Our Data Labels.mp4 -
114.8 MB
14. Loading Our Data Labels.srt -
16.1 KB
14.1 Documentation on how many images Google recommends for image problems.html -
129 bytes
15. Preparing The Images.mp4 -
133.9 MB
15. Preparing The Images.srt -
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16. Turning Data Labels Into Numbers.mp4 -
107.5 MB
16. Turning Data Labels Into Numbers.srt -
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17. Creating Our Own Validation Set.mp4 -
66.4 MB
17. Creating Our Own Validation Set.srt -
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17.1 Blog post by Rachel Thomas (of fast.ai) on how and why you should create a validation set.html -
108 bytes
18. Preprocess Images.mp4 -
90.1 MB
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18.1 Documentation for loading images in TensorFlow.html -
114 bytes
18.2 TensorFlow guidelines for loading all kinds of data (turning your data into Tensors).html -
98 bytes
19. Preprocess Images 2.mp4 -
105.1 MB
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20. Turning Data Into Batches.mp4 -
87.8 MB
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21. Turning Data Into Batches 2.mp4 -
149.4 MB
21. Turning Data Into Batches 2.srt -
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21.1 Yann LeCun's (OG of deep learning) Tweet on Batch Sizes.html -
118 bytes
22. Visualizing Our Data.mp4 -
122.0 MB
22. Visualizing Our Data.srt -
15.7 KB
23. Preparing Our Inputs and Outputs.mp4 -
50.1 MB
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23.1 TensorFlow Hub (resource for pre-trained deep learning models and more).html -
79 bytes
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25. Building A Deep Learning Model.mp4 -
121.9 MB
25. Building A Deep Learning Model.srt -
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25.1 Andrei Karpathy's talk on AI at Tesla.html -
95 bytes
25.2 Papers with Code (a great resource for some of the best machine learning papers with code examples).html -
88 bytes
25.3 MobileNetV2 (the model we're using) on TensorFlow Hub.html -
132 bytes
25.4 PyTorch Hub (PyTorch version of TensorFlow Hub).html -
85 bytes
25.5 TensorFlow Hub (resource for pre-trained deep learning models and more).html -
79 bytes
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105.9 MB
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26.1 Keras in TensorFlow Overview Documentation.html -
108 bytes
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105.9 MB
27. Building A Deep Learning Model 3.srt -
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27.1 The Softmax Function (activation function we use in our model).html -
107 bytes
27.2 Step by step breakdown of a convolutional neural network (what MobileNetV2 is made of).html -
172 bytes
27.3 MobileNetV2 (the model we're using) architecture explanation by Sik-Ho Tsang.html -
163 bytes
28. Building A Deep Learning Model 4.mp4 -
86.3 MB
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28.1 [Article] How to choose loss & activation functions when building a deep learning model.html -
169 bytes
29. Summarizing Our Model.mp4 -
45.4 MB
29. Summarizing Our Model.srt -
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30. Evaluating Our Model.mp4 -
79.3 MB
30. Evaluating Our Model.srt -
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30.1 TensorBoard Callback Documentation.html -
134 bytes
31. Preventing Overfitting.mp4 -
36.5 MB
31. Preventing Overfitting.srt -
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31.1 Early Stopping Callback (a way to stop your model from training when it stops improving) Documentation.html -
136 bytes
32. Training Your Deep Neural Network.mp4 -
166.6 MB
32. Training Your Deep Neural Network.srt -
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33. Evaluating Performance With TensorBoard.mp4 -
74.2 MB
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34. Make And Transform Predictions.mp4 -
155.0 MB
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35. Transform Predictions To Text.mp4 -
129.9 MB
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35.1 TensorFlow documentation for the unbatch() function.html -
127 bytes
36. Visualizing Model Predictions.mp4 -
119.3 MB
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37. Visualizing And Evaluate Model Predictions 2.mp4 -
143.8 MB
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38. Visualizing And Evaluate Model Predictions 3.mp4 -
113.2 MB
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39. Saving And Loading A Trained Model.mp4 -
127.0 MB
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40. Training Model On Full Dataset.mp4 -
139.8 MB
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41. Making Predictions On Test Images.mp4 -
140.8 MB
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41.1 Dog Vision Prediction Probabilities Array.html -
170 bytes
42. Submitting Model to Kaggle.mp4 -
121.3 MB
42. Submitting Model to Kaggle.srt -
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42.1 Dog Vision Predictions with MobileNetV2 Ready for Kaggle Submission.html -
180 bytes
43. Making Predictions On Our Images.mp4 -
119.2 MB
43. Making Predictions On Our Images.srt -
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43.1 End-to-end Dog Vision Notebook (with annotations).html -
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43.2 End-to-end Dog Vision Notebook (from the videos).html -
191 bytes
44. Finishing Dog Vision Where to next.html -
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1. Section Overview.mp4 -
10.9 MB
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4.9 MB
2. Communicating Your Work.mp4 -
20.2 MB
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18.4 MB
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106 bytes
6.2 Devblog by Hashnode (an easy and free way to create a blog you own).html -
89 bytes
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12.0 MB
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587 bytes
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161.0 MB
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176.1 MB
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130.3 MB
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113.1 MB
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104.8 MB
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78.0 MB
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52.9 MB
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77 bytes
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47.2 MB
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10.7 MB
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69.5 MB
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6.1 Python 2 vs Python 3.html -
128 bytes
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104 bytes
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161 bytes
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26.0 MB
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38.5 MB
8. Learning Python.srt -
2.6 KB
9. Python Data Types.mp4 -
28.8 MB
9. Python Data Types.srt -
5.2 KB
10. How To Succeed.html -
280 bytes
11. Numbers.mp4 -
72.7 MB
11. Numbers.srt -
11.1 KB
11.1 Floating point numbers.html -
104 bytes
12. Math Functions.mp4 -
41.8 MB
12. Math Functions.srt -
5.4 KB
13. DEVELOPER FUNDAMENTALS I.mp4 -
59.7 MB
13. DEVELOPER FUNDAMENTALS I.srt -
5.2 KB
14. Operator Precedence.mp4 -
14.4 MB
14. Operator Precedence.srt -
3.5 KB
14.1 Exercise Repl.html -
106 bytes
15. Exercise Operator Precedence.html -
683 bytes
15.1 Exercise Repl.html -
106 bytes
16. Optional bin() and complex.mp4 -
21.9 MB
16. Optional bin() and complex.srt -
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16.1 Base Numbers.html -
111 bytes
17. Variables.mp4 -
93.6 MB
17. Variables.srt -
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17.1 Python Keywords.html -
117 bytes
18. Expressions vs Statements.mp4 -
11.0 MB
18. Expressions vs Statements.srt -
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19. Augmented Assignment Operator.mp4 -
15.3 MB
19. Augmented Assignment Operator.srt -
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19.1 Exercise Repl.html -
116 bytes
20. Strings.mp4 -
31.0 MB
20. Strings.srt -
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21. String Concatenation.mp4 -
7.3 MB
21. String Concatenation.srt -
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22. Type Conversion.mp4 -
19.0 MB
22. Type Conversion.srt -
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23. Escape Sequences.mp4 -
23.2 MB
23. Escape Sequences.srt -
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24. Formatted Strings.mp4 -
49.3 MB
24. Formatted Strings.srt -
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24.1 Exercise Repl.html -
104 bytes
25. String Indexes.mp4 -
49.2 MB
25. String Indexes.srt -
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25.1 Exercise Repl.html -
101 bytes
26. Immutability.mp4 -
20.8 MB
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27. Built-In Functions + Methods.mp4 -
69.4 MB
27. Built-In Functions + Methods.srt -
10.3 KB
27.1 String Methods.html -
115 bytes
27.2 Built in Functions.html -
109 bytes
28. Booleans.mp4 -
16.5 MB
28. Booleans.srt -
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29. Exercise Type Conversion.mp4 -
50.3 MB
29. Exercise Type Conversion.srt -
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30. DEVELOPER FUNDAMENTALS II.mp4 -
29.2 MB
30. DEVELOPER FUNDAMENTALS II.srt -
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30.1 Python Comments Best Practices.html -
106 bytes
31. Exercise Password Checker.mp4 -
51.1 MB
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32. Lists.mp4 -
22.0 MB
32. Lists.srt -
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33. List Slicing.mp4 -
49.9 MB
33. List Slicing.srt -
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33.1 Exercise Repl.html -
92 bytes
34. Matrix.mp4 -
19.1 MB
34. Matrix.srt -
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34.1 Exercise Repl.html -
93 bytes
35. List Methods.mp4 -
61.8 MB
35. List Methods.srt -
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35.1 List Methods.html -
113 bytes
36. List Methods 2.mp4 -
27.4 MB
36. List Methods 2.srt -
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36.1 Python Keywords.html -
117 bytes
36.2 Exercise Repl.html -
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37. List Methods 3.mp4 -
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37. List Methods 3.srt -
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38. Common List Patterns.mp4 -
40.5 MB
38. Common List Patterns.srt -
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38.1 Exercise Repl.html -
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39. List Unpacking.mp4 -
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40. None.mp4 -
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40. None.srt -
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41. Dictionaries.mp4 -
32.7 MB
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42. DEVELOPER FUNDAMENTALS III.mp4 -
26.6 MB
42. DEVELOPER FUNDAMENTALS III.srt -
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43. Dictionary Keys.mp4 -
20.4 MB
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44. Dictionary Methods.mp4 -
27.2 MB
44. Dictionary Methods.srt -
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44.1 Dictionary Methods.html -
119 bytes
45. Dictionary Methods 2.mp4 -
42.4 MB
45. Dictionary Methods 2.srt -
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45.1 Exercise Repl.html -
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46. Tuples.mp4 -
25.6 MB
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47. Tuples 2.mp4 -
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47.1 Tuple Methods.html -
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48. Sets.mp4 -
37.0 MB
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49. Sets 2.mp4 -
64.3 MB
49. Sets 2.srt -
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49.1 Exercise Repl.html -
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49.2 Sets Methods.html -
112 bytes
1. Breaking The Flow.mp4 -
20.3 MB
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2. Conditional Logic.mp4 -
74.6 MB
2. Conditional Logic.srt -
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3. Indentation In Python.mp4 -
28.0 MB
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4. Truthy vs Falsey.mp4 -
42.8 MB
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4.1 Truthy vs Falsey Stackoverflow.html -
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5. Ternary Operator.mp4 -
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6. Short Circuiting.mp4 -
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7. Logical Operators.mp4 -
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8. Exercise Logical Operators.mp4 -
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9. is vs ==.mp4 -
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10. For Loops.mp4 -
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11. Iterables.mp4 -
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12. Exercise Tricky Counter.mp4 -
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18. Our First GUI.mp4 -
49.6 MB
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18.1 Exercise Repl.html -
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18.2 Solution Repl.html -
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19. DEVELOPER FUNDAMENTALS IV.mp4 -
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20. Exercise Find Duplicates.mp4 -
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20.1 Solution Repl.html -
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21. Functions.mp4 -
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22. Parameters and Arguments.mp4 -
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23. Default Parameters and Keyword Arguments.mp4 -
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24. return.mp4 -
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27. Docstrings.mp4 -
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28. Clean Code.mp4 -
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29. args and kwargs.mp4 -
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30. Exercise Functions.mp4 -
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32. Scope Rules.mp4 -
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34. nonlocal Keyword.mp4 -
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35. Why Do We Need Scope.mp4 -
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36. Pure Functions.mp4 -
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39. zip().mp4 -
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52.3 MB
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41. List Comprehensions.mp4 -
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42. Set Comprehensions.mp4 -
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42. Set Comprehensions.srt -
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43. Exercise Comprehensions.mp4 -
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43. Exercise Comprehensions.srt -
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43.1 Exercise Repl.html -
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43.2 Solution Repl.html -
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44. Python Exam Testing Your Understanding.html -
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45. Modules in Python.mp4 -
82.2 MB
45. Modules in Python.srt -
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46. Quick Note Upcoming Videos.html -
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47. Optional PyCharm.mp4 -
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48. Packages in Python.mp4 -
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48. Packages in Python.srt -
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49. Different Ways To Import.mp4 -
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50. Next Steps.html -
959 bytes
51. Bonus Resource Python Cheatsheet.html -
489 bytes
1. Statistics and Mathematics.html -
710 bytes
1. Become An Alumni.html -
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2. Thank You.mp4 -
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2. Thank You.srt -
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3. Course Review.html -
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4. The Final Challenge.html -
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