udemy-机器学习和数据科学训练营

📁 udemy-机器学习和数据科学训练营Complete Machine Learning & Data Science Bootcamp
📁 4 – The 2 Paths
📁 6 – Pandas Data Analysis
📁 20 – Where To Go From Here
📁 12 – Milestone Project 2 Supervised Learning Time Series Data
📁 1 – Introduction
📁 10 – Supervised Learning Classification Regression
📁 9 – Scikitlearn Creating Machine Learning Models
📁 21 – BONUS SECTION
📁 18 – Learn Python Part 2
📁 1 – Introduction
📁 16 – Career Advice Extra Bits
📁 11 – Milestone Project 1 Supervised Learning Classification
📁 8 – Matplotlib Plotting and Data Visualization
📁 19 – Extra Learn Advanced Statistics and Mathematics for FREE
📁 14 – Neural Networks Deep Learning Transfer Learning and TensorFlow 2
📁 7 – NumPy
📁 2 – Machine Learning 101
📁 13 – Data Engineering
📁 5 – Data Science Environment Setup
📁 17 – Learn Python
📁 15 – Storytelling Communication How To Present Your Work
📁 3 – Machine Learning and Data Science Framework
📄 48 – Pandas Documentation.txt
📄 48 – Pandas Introduction.srt
📄 57 – Assignment Pandas Practice.html
📄 52 – car-sales.csv
📄 55 – Manipulating Data 2.srt
📄 49 – Series Data Frames and CSVs.srt
📄 56 – Manipulating Data 3.srt
📄 58 – How To Download The Course Assignments.srt
📄 49 – car-sales.csv
📄 52 – Selecting and Viewing Data with Pandas.mp4
📄 56 – Manipulating Data 3.mp4
📄 52 – Selecting and Viewing Data with Pandas.srt
📄 46 – Section Overview.mp4
📄 54 – Manipulating Data.srt
📄 51 – Describing Data with Pandas.mp4
📄 56 – Introduction to Pandas Jupyter Notebook from the videos.txt
📄 48 – Introduction to Pandas Jupyter Notebook with annotations.txt
📄 46 – Section Overview.srt
📄 55 – pandas-anatomy-of-a-dataframe.png
📄 58 – Course notebooks Github.txt
📄 48 – Pandas Introduction.mp4
📄 51 – Describing Data with Pandas.srt
📄 53 – Selecting and Viewing Data with Pandas Part 2.mp4
📄 55 – Manipulating Data 2.mp4
📄 56 – Introduction to Pandas Jupyter Notebook with annotations.txt
📄 47 – Downloading Workbooks and Assignments.html
📄 54 – Manipulating Data.mp4
📄 58 – How To Download The Course Assignments.mp4
📄 49 – pandas-anatomy-of-a-dataframe.png
📄 50 – Data from URLs.html
📄 49 – Series Data Frames and CSVs.mp4
📄 48 – 10 minutes to pandas from the documentation.txt
📄 48 – Introduction to Pandas Jupyter Notebook from the upcoming videos.txt
📄 54 – car-sales-missing-data.csv
📄 58 – Google Colab.txt
📄 53 – Selecting and Viewing Data with Pandas Part 2.srt
📄 54 – Jake VanderPlass Data Manipulation with Pandas.txt
📄 32 – Endorsements On LinkedIN.html
📄 31 – Python Machine Learning Monthly.html
📄 30 – The 2 Paths.mp4
📄 30 – The 2 Paths.srt
📄 376 – Thank You.mp4
📄 375 – Become An Alumni.html
📄 377 – Thank You Part 2.html
📄 376 – Thank You.srt
📄 194 – Endtoend Bluebook Bulldozer Regression Notebook same as in videos.txt
📄 181 – Feature Engineering.mp4
📄 193 – Making Predictions.mp4
📄 188 – Custom Evaluation Function.mp4
📄 194 – Feature Importance.srt
📄 175 – Structured Data Projects on GitHub.txt
📄 185 – Fitting A Machine Learning Model.srt
📄 194 – Feature Importance.mp4
📄 189 – Reducing Data.srt
📄 177 – Project Environment Setup.srt
📄 193 – Making Predictions.srt
📄 184 – Filling Missing Categorical Values.srt
📄 183 – Pandas Categorical Datatype Documentation.txt
📄 175 – Endtoend Bluebook Bulldozer Regression Notebook same as in videos.txt
📄 183 – Filling Missing Numerical Values.srt
📄 191 – Improving Hyperparameters.srt
📄 186 – Splitting Data.mp4
📄 179 – Exploring Our Data.srt
📄 181 – Feature Engineering.srt
📄 191 – Improving Hyperparameters.mp4
📄 192 – Preproccessing Our Data.srt
📄 178 – Step 14 Framework Setup.srt
📄 184 – Filling Missing Categorical Values.mp4
📄 176 – Downloading the data for the next two projects.html
📄 175 – Project Overview.srt
📄 189 – Reducing Data.mp4
📄 174 – Section Overview.mp4
📄 185 – Fitting A Machine Learning Model.mp4
📄 187 – Challenge Whats wrong with splitting data after filling it.html
📄 180 – Exploring Our Data 2.srt
📄 179 – Exploring Our Data.mp4
📄 183 – Filling Missing Numerical Values.mp4
📄 186 – Splitting Data.srt
📄 182 – Turning Data Into Numbers.srt
📄 174 – Section Overview.srt
📄 188 – Custom Evaluation Function.srt
📄 194 – Endtoend Bluebook Bulldozer Regression Notebook with annotations.txt
📄 192 – Preproccessing Our Data.mp4
📄 175 – Project Overview.mp4
📄 190 – RandomizedSearchCV.srt
📄 190 – RandomizedSearchCV.mp4
📄 182 – Turning Data Into Numbers.mp4
📄 175 – Kaggle Bluebook for Bulldozers Competition.txt
📄 177 – Project Environment Setup.mp4
📄 175 – Endtoend Bluebook Bulldozer Regression Notebook with annotations.txt
📄 178 – Step 14 Framework Setup.mp4
📄 180 – Exploring Our Data 2.mp4
📄 378 – Special Bonus Lecture.html
📄 150 – Milestone Projects.html
📄 1 – Course Outline.srt
📄 142 – Tuning Hyperparameters 3.srt
📄 133 – NEW Evaluating A Regression Model 1 R2 Score.mp4
📄 132 – Evaluating A Classification Model 6 Classification Report.mp4
📄 145 – Saving And Loading A Model.srt
📄 125 – Evaluating A Machine Learning Model 2 Cross Validation.mp4
📄 149 – ScikitLearn Practice.html
📄 147 – Reading extension ScikitLearns Pipeline class explained.txt
📄 103 – Scikitlearn Cheatsheet.mp4
📄 103 – Scikitlearn Cheatsheet.srt
📄 131 – NEW Evaluating A Classification Model 5 Confusion Matrix.srt
📄 122 – NEW Making Predictions With Our Model Regression.mp4
📄 114 – NEW Choosing The Right Model For Your Data.srt
📄 122 – NEW Making Predictions With Our Model Regression.srt
📄 148 – Putting It All Together 2.srt
📄 118 – Choosing The Right Model For Your Data 3 Classification.srt
📄 104 – Typical scikitlearn Workflow.srt
📄 146 – Saving And Loading A Model 2.srt
📄 127 – Evaluating A Classification Model 2 ROC Curve.srt
📄 119 – Fitting A Model To The Data.srt
📄 128 – Evaluating A Classification Model 3 ROC Curve.srt
📄 114 – NEW Choosing The Right Model For Your Data.mp4
📄 129 – Reading Extension ROC Curve AUC.html
📄 119 – Fitting A Model To The Data.mp4
📄 130 – Evaluating A Classification Model 4 Confusion Matrix.mp4
📄 107 – Quick Tip Clean Transform Reduce.srt
📄 101 – Refresher What Is Machine Learning.mp4
📄 130 – Evaluating A Classification Model 4 Confusion Matrix.srt
📄 128 – Evaluating A Classification Model 3 ROC Curve.mp4
📄 106 – Getting Your Data Ready Splitting Your Data.srt
📄 139 – Improving A Machine Learning Model.mp4
📄 147 – Putting It All Together.srt
📄 109 – Note Update to next video OneHotEncoder can handle NaNNone values.html
📄 108 – Getting Your Data Ready Convert Data To Numbers.srt
📄 115 – NEW Choosing The Right Model For Your Data 2 Regression.mp4
📄 127 – Evaluating A Classification Model 2 ROC Curve.mp4
📄 116 – Quick Note Decision Trees.html
📄 141 – Tuning Hyperparameters 2.mp4
📄 139 – Improving A Machine Learning Model.srt
📄 99 – Introduction to ScikitLearn Jupyter Notebook from the upcoming videos.txt
📄 142 – Tuning Hyperparameters 3.mp4
📄 106 – scikit-learn-data.zip
📄 111 – Extension Feature Scaling.html
📄 112 – Note Correction in the upcoming video splitting data.html
📄 108 – Getting Your Data Ready Convert Data To Numbers.mp4
📄 147 – Putting It All Together.mp4
📄 117 – Quick Tip How ML Algorithms Work.mp4
📄 125 – Evaluating A Machine Learning Model 2 Cross Validation.srt
📄 141 – Tuning Hyperparameters 2.srt
📄 148 – Introduction to ScikitLearn Jupyter Notebook with annotations.txt
📄 140 – Tuning Hyperparameters.mp4
📄 104 – Typical scikitlearn Workflow.mp4
📄 117 – Quick Tip How ML Algorithms Work.srt
📄 99 – ScikitLearn Documentation.txt
📄 137 – NEW Evaluating A Model With Cross Validation and Scoring Parameter.mp4
📄 135 – NEW Evaluating A Regression Model 3 MSE.mp4
📄 130 – Notebook from video with updated confusion matrix labels.txt
📄 121 – predict vs predictproba.mp4
📄 123 – NEW Evaluating A Machine Learning Model Score Part 1.mp4
📄 102 – Quick Note Upcoming Videos.html
📄 107 – Quick Tip Clean Transform Reduce.mp4
📄 120 – Making Predictions With Our Model.mp4
📄 148 – Introduction to ScikitLearn Jupyter Notebook from the videos.txt
📄 131 – NEW Evaluating A Classification Model 5 Confusion Matrix.mp4
📄 123 – NEW Evaluating A Machine Learning Model Score Part 1.srt
📄 138 – NEW Evaluating A Model With Scikitlearn Functions.mp4
📄 137 – NEW Evaluating A Model With Cross Validation and Scoring Parameter.srt
📄 144 – Quick Tip Correlation Analysis.mp4
📄 135 – NEW Evaluating A Regression Model 3 MSE.srt
📄 98 – Section Overview.mp4
📄 134 – NEW Evaluating A Regression Model 2 MAE.srt
📄 124 – NEW Evaluating A Machine Learning Model Score Part 2.srt
📄 110 – Getting Your Data Ready Handling Missing Values With Pandas.srt
📄 145 – Saving And Loading A Model.mp4
📄 105 – Optional Debugging Warnings In Jupyter.mp4
📄 126 – Evaluating A Classification Model 1 Accuracy.mp4
📄 144 – Quick Tip Correlation Analysis.srt
📄 140 – Tuning Hyperparameters.srt
📄 132 – Evaluating A Classification Model 6 Classification Report.srt
📄 146 – Saving And Loading A Model 2.mp4
📄 121 – predict vs predictproba.srt
📄 105 – Optional Debugging Warnings In Jupyter.srt
📄 134 – NEW Evaluating A Regression Model 2 MAE.mp4
📄 100 – Quick Note Upcoming Video.html
📄 124 – NEW Evaluating A Machine Learning Model Score Part 2.mp4
📄 118 – Choosing The Right Model For Your Data 3 Classification.mp4
📄 103 – ScikitLearn Reference Notebook.txt
📄 106 – Getting Your Data Ready Splitting Your Data.mp4
📄 99 – Scikitlearn Introduction.mp4
📄 133 – NEW Evaluating A Regression Model 1 R2 Score.srt
📄 120 – Making Predictions With Our Model.srt
📄 113 – Getting Your Data Ready Handling Missing Values With Scikitlearn.mp4
📄 101 – Refresher What Is Machine Learning.srt
📄 115 – NEW Choosing The Right Model For Your Data 2 Regression.srt
📄 99 – Scikitlearn Introduction.srt
📄 113 – Getting Your Data Ready Handling Missing Values With Scikitlearn.srt
📄 104 – Example ScikitLearn Workflow Notebook.txt
📄 126 – Evaluating A Classification Model 1 Accuracy.srt
📄 110 – Getting Your Data Ready Handling Missing Values With Pandas.mp4
📄 114 – ScikitLearn machine learning map how to choose the right machine learning model【微信号 itcodeba 】【更多教程 todo1024.com】.txt
📄 143 – Note Metric Comparison Improvement.html
📄 148 – Putting It All Together 2.mp4
📄 99 – Introduction to ScikitLearn Jupyter Notebook with annotations.txt
📄 138 – NEW Evaluating A Model With Scikitlearn Functions.srt
📄 136 – Machine Learning Model Evaluation.html
📄 98 – Section Overview.srt
📄 264 – Quick Note Upcoming Videos.html
📄 270 – Contributing To Open Source.srt
📄 265 – JTS Learn to Learn.mp4
📄 272 – Exercise Contribute To Open Source.html
📄 267 – Quick Note Upcoming Videos.html
📄 263 – Learning Guideline.html
📄 268 – CWD Git Github.mp4
📄 270 – Contributing To Open Source.mp4
📄 271 – Contributing To Open Source 2.srt
📄 266 – JTS Start With Why.mp4
📄 268 – CWD Git Github.srt
📄 269 – CWD Git Github 2.mp4
📄 273 – Coding Challenges.html
📄 271 – Contributing To Open Source 2.mp4
📄 269 – CWD Git Github 2.srt
📄 260 – Endorsements On LinkedIn.html
📄 261 – Quick Note Upcoming Video.html
📄 262 – What If I Dont Have Enough Experience.srt
📄 265 – JTS Learn to Learn.srt
📄 266 – JTS Start With Why.srt
📄 262 – What If I Dont Have Enough Experience.mp4
📄 2 – Join Our Online Classroom.mp4
📄 4 – Your First Day.srt
📄 2 – Join Our Online Classroom.srt
📄 1 – Course Outline.mp4
📄 3 – Exercise Meet Your Classmates and Instructor.html
📄 173 – Reviewing The Project.srt
📄 152 – Structured Data Projects on GitHub.txt
📄 163 – Experimenting With Machine Learning Models.mp4
📄 159 – Finding Patterns 2.mp4
📄 169 – Evaluating Our Model.mp4
📄 161 – Preparing Our Data For Machine Learning.srt
📄 165 – Tuning Hyperparameters.mp4
📄 172 – Finding The Most Important Features.mp4
📄 159 – Finding Patterns 2.srt
📄 151 – Section Overview.mp4
📄 169 – Evaluating Our Model.srt
📄 162 – Choosing The Right Models.srt
📄 163 – Experimenting With Machine Learning Models.srt
📄 162 – Choosing The Right Models.mp4
📄 160 – Finding Patterns 3.mp4
📄 167 – Tuning Hyperparameters 3.srt
📄 156 – Getting Our Tools Ready.srt
📄 170 – Evaluating Our Model 2.mp4
📄 167 – Tuning Hyperparameters 3.mp4
📄 156 – Getting Our Tools Ready.mp4
📄 157 – Exploring Our Data.srt
📄 173 – Reviewing The Project.mp4
📄 152 – Endtoend Heart Disease Classification Notebook with annotations.txt
📄 164 – TuningImproving Our Model.mp4
📄 161 – Preparing Our Data For Machine Learning.mp4
📄 158 – Finding Patterns.mp4
📄 173 – Endtoend Heart Disease Classification Notebook same as in videos.txt
📄 164 – TuningImproving Our Model.srt
📄 152 – Project Overview.mp4
📄 158 – Finding Patterns.srt
📄 171 – Evaluating Our Model 3.srt
📄 155 – Step 14 Framework Setup.mp4
📄 152 – Project Overview.srt
📄 157 – Exploring Our Data.mp4
📄 172 – Finding The Most Important Features.srt
📄 160 – Finding Patterns 3.srt
📄 171 – Evaluating Our Model 3.mp4
📄 168 – Quick Note Confusion Matrix Labels.html
📄 154 – Optional Windows Project Environment Setup.srt
📄 166 – Tuning Hyperparameters 2.mp4
📄 151 – Section Overview.srt
📄 153 – Project Environment Setup.mp4
📄 153 – Project Environment Setup.srt
📄 152 – Endtoend Heart Disease Classification Notebook same as in videos.txt
📄 157 – heart-disease.csv
📄 155 – Step 14 Framework Setup.srt
📄 173 – Endtoend Heart Disease Classification Notebook with annotations.txt
📄 165 – Tuning Hyperparameters.srt
📄 154 – Optional Windows Project Environment Setup.mp4
📄 170 – Evaluating Our Model 2.srt
📄 166 – Tuning Hyperparameters 2.srt
📄 338 – While Loops 2.mp4
📄 370 – Packages in Python.mp4
📄 365 – Exercise Repl.txt
📄 342 – Solution Repl.txt
📄 352 – Solution Repl.txt
📄 362 – reduce.srt
📄 331 – is vs.srt
📄 341 – DEVELOPER FUNDAMENTALS IV.srt
📄 342 – Exercise Find Duplicates.mp4
📄 352 – Exercise Functions.srt
📄 369 – Optional PyCharm.mp4
📄 335 – range.srt
📄 368 – Quick Note Upcoming Videos.html
📄 344 – Parameters and Arguments.mp4
📄 340 – Solution Repl.txt
📄 347 – Exercise Tesla.html
📄 333 – Iterables.srt
📄 348 – Methods vs Functions.mp4
📄 356 – nonlocal Keyword.srt
📄 349 – Docstrings.mp4
📄 343 – Functions.mp4
📄 353 – Scope.mp4
📄 357 – Why Do We Need Scope.mp4
📄 362 – reduce.mp4
📄 360 – filter.srt
📄 372 – Next Steps.html
📄 334 – Exercise Tricky Counter.mp4
📄 324 – Conditional Logic.mp4
📄 355 – global Keyword.mp4
📄 326 – Truthy vs Falsey.srt
📄 326 – Truthy vs Falsey Stackoverflow.txt
📄 341 – DEVELOPER FUNDAMENTALS IV.mp4
📄 335 – range.mp4
📄 325 – Indentation In Python.mp4
📄 348 – Methods vs Functions.srt
📄 346 – return.mp4
📄 339 – break continue pass.srt
📄 344 – Parameters and Arguments.srt
📄 361 – zip.srt
📄 328 – Short Circuiting.srt
📄 345 – Default Parameters and Keyword Arguments.mp4
📄 329 – Logical Operators.mp4
📄 323 – Breaking The Flow.mp4
📄 365 – Exercise Comprehensions.srt
📄 327 – Ternary Operator.srt
📄 359 – map.srt
📄 360 – filter.mp4
📄 346 – return.srt
📄 351 – args and kwargs.mp4
📄 364 – Set Comprehensions.srt
📄 333 – Iterables.mp4
📄 323 – Breaking The Flow.srt
📄 327 – Ternary Operator.mp4
📄 353 – Scope.srt
📄 359 – map.mp4
📄 355 – global Keyword.srt
📄 369 – Optional PyCharm.srt
📄 340 – Exercise Repl.txt
📄 366 – Python Exam Testing Your Understanding.html
📄 365 – Exercise Comprehensions.mp4
📄 325 – Indentation In Python.srt
📄 331 – is vs.mp4
📄 326 – Truthy vs Falsey.mp4
📄 324 – Conditional Logic.srt
📄 373 – Bonus Resource Python Cheatsheet.html
📄 356 – nonlocal Keyword.mp4
📄 367 – Modules in Python.mp4
📄 340 – Our First GUI.srt
📄 329 – Logical Operators.srt
📄 349 – Docstrings.srt
📄 367 – Modules in Python.srt
📄 354 – Scope Rules.srt
📄 351 – args and kwargs.srt
📄 336 – enumerate.srt
📄 339 – break continue pass.mp4
📄 350 – Clean Code.srt
📄 342 – Exercise Find Duplicates.srt
📄 350 – Clean Code.mp4
📄 363 – List Comprehensions.srt
📄 356 – Solution Repl.txt
📄 330 – Exercise Logical Operators.srt
📄 340 – Our First GUI.mp4
📄 361 – zip.mp4
📄 334 – Solution Repl.txt
📄 334 – Exercise Tricky Counter.srt
📄 371 – Different Ways To Import.srt
📄 364 – Set Comprehensions.mp4
📄 352 – Exercise Functions.mp4
📄 358 – Pure Functions.srt
📄 336 – enumerate.mp4
📄 370 – Packages in Python.srt
📄 343 – Functions.srt
📄 371 – Different Ways To Import.mp4
📄 338 – While Loops 2.srt
📄 328 – Short Circuiting.mp4
📄 358 – Pure Functions.mp4
📄 345 – Default Parameters and Keyword Arguments.srt
📄 354 – Scope Rules.mp4
📄 330 – Exercise Logical Operators.mp4
📄 332 – For Loops.mp4
📄 337 – While Loops.mp4
📄 332 – For Loops.srt
📄 337 – While Loops.srt
📄 363 – List Comprehensions.mp4
📄 365 – Solution Repl.txt
📄 374 – Statistics and Mathematics.html
📄 74 – numpy-images.zip
📄 70 – Matrix Multiplication Explained.txt
📄 72 – Comparison Operators.mp4
📄 66 – Manipulating Arrays.mp4
📄 62 – NumPy DataTypes and Attributes.mp4
📄 76 – Assignment NumPy Practice.html
📄 61 – Quick Note Correction In Next Video.html
📄 68 – Standard Deviation and Variance.srt
📄 59 – Section Overview.mp4
📄 70 – Dot Product vs Element Wise.srt
📄 67 – Manipulating Arrays 2.srt
📄 77 – Optional Extra NumPy resources.html
📄 75 – Exercise Imposter Syndrome.srt
📄 75 – Exercise Imposter Syndrome.mp4
📄 60 – NumPy Documentation.txt
📄 67 – Manipulating Arrays 2.mp4
📄 59 – Section Overview.srt
📄 69 – Reshape and Transpose.mp4
📄 64 – NumPy Random Seed.mp4
📄 66 – Manipulating Arrays.srt
📄 60 – NumPy Introduction.srt
📄 67 – Standard deviation and variance explained.txt
📄 62 – NumPy DataTypes and Attributes.srt
📄 63 – Creating NumPy Arrays.srt
📄 60 – Introduction to NumPy Jupyter Notebook from the upcoming videos.txt
📄 68 – Standard deviation and variance explained.txt
📄 60 – NumPy Introduction.mp4
📄 73 – Sorting Arrays.srt
📄 74 – Introduction to NumPy Jupyter Notebook with annotations.txt
📄 71 – Exercise Nut Butter Store Sales.mp4
📄 71 – Exercise Nut Butter Store Sales.srt
📄 68 – Standard Deviation and Variance.mp4
📄 70 – Dot Product vs Element Wise.mp4
📄 66 – Standard deviation and variance explained.txt
📄 64 – NumPy Random Seed.srt
📄 63 – Creating NumPy Arrays.mp4
📄 74 – Turn Images Into NumPy Arrays.mp4
📄 65 – Viewing Arrays and Matrices.mp4
📄 60 – Introduction to NumPy Jupyter Notebook with annotations.txt
📄 74 – Introduction to NumPy Jupyter Notebook from the videos.txt
📄 74 – Turn Images Into NumPy Arrays.srt
📄 65 – Viewing Arrays and Matrices.srt
📄 69 – Reshape and Transpose.srt
📄 72 – Comparison Operators.srt
📄 73 – Sorting Arrays.mp4
📄 83 – Histograms And Subplots.mp4
📄 83 – Histograms And Subplots.srt
📄 84 – Subplots Option 2.mp4
📄 95 – Customizing Your Plots 2.srt
📄 81 – Anatomy Of A Matplotlib Figure.srt
📄 81 – matplotlib-anatomy-of-a-plot-with-code.png
📄 97 – Assignment Matplotlib Practice.html
📄 78 – Section Overview.mp4
📄 90 – Plotting from Pandas DataFrames 4.mp4
📄 79 – Matplotlib Introduction.srt
📄 84 – Subplots Option 2.srt
📄 80 – Importing And Using Matplotlib.mp4
📄 79 – Introduction to Matplotlib Jupyter Notebook from the upcoming videos.txt
📄 88 – Plotting From Pandas DataFrames 2.mp4
📄 95 – Customizing Your Plots 2.mp4
📄 94 – Customizing Your Plots.srt
📄 82 – Scatter Plot And Bar Plot.mp4
📄 92 – Plotting from Pandas DataFrames 6.srt
📄 96 – Introduction to Matplotlib Notebook from the videos.txt
📄 87 – Quick Note Regular Expressions.html
📄 91 – Plotting from Pandas DataFrames 5.mp4
📄 89 – Plotting from Pandas DataFrames 3.srt
📄 92 – Plotting from Pandas DataFrames 6.mp4
📄 93 – Plotting from Pandas DataFrames 7.srt
📄 78 – Section Overview.srt
📄 86 – Plotting From Pandas DataFrames.srt
📄 81 – Anatomy Of A Matplotlib Figure.mp4
📄 89 – Plotting from Pandas DataFrames 3.mp4
📄 88 – Plotting From Pandas DataFrames 2.srt
📄 79 – Matplotlib Documentation.txt
📄 94 – Customizing Your Plots.mp4
📄 80 – Importing And Using Matplotlib.srt
📄 85 – Quick Tip Data Visualizations.mp4
📄 96 – Saving And Sharing Your Plots.mp4
📄 90 – heart-disease.csv
📄 91 – Plotting from Pandas DataFrames 5.srt
📄 82 – Scatter Plot And Bar Plot.srt
📄 86 – Plotting From Pandas DataFrames.mp4
📄 79 – Matplotlib Introduction.mp4
📄 90 – Plotting from Pandas DataFrames 4.srt
📄 85 – Quick Tip Data Visualizations.srt
📄 81 – matplotlib-anatomy-of-a-plot.png
📄 93 – Plotting from Pandas DataFrames 7.mp4
📄 96 – Saving And Sharing Your Plots.srt
📄 234 – The Softmax Function activation function we use in our model.txt
📄 212 – Google Colab our workspace for the upcoming project.txt
📄 240 – Evaluating Performance With TensorBoard.mp4
📄 213 – Uploading Project Data.srt
📄 212 – Google Colab Workspace.srt
📄 235 – Article How to choose loss & activation functions when building a deep learning model.txt
📄 235 – Building A Deep Learning Model 4.srt
📄 248 – Making Predictions On Test Images.mp4
📄 217 – Optional TensorFlow 20 Default Issue.srt
📄 243 – Visualizing Model Predictions.mp4
📄 244 – Visualizing And Evaluate Model Predictions 2.mp4
📄 250 – Endtoend Dog Vision Notebook with annotations.txt
📄 227 – Turning Data Into Batches.srt
📄 224 – Blog post by Rachel Thomas of fastai on how and why you should create a validation set.txt
📄 247 – Training Model On Full Dataset.mp4
📄 221 – Loading Our Data Labels.srt
📄 246 – Saving And Loading A Trained Model.srt
📄 221 – Documentation on how many images Google recommends for image problems】.txt
📄 234 – Step by step breakdown of a convolutional neural network what MobileNetV2 is made of.txt
📄 234 – Building A Deep Learning Model 3.mp4
📄 232 – Andrei Karpathys talk on AI at Tesla.txt
📄 231 – Optional How machines learn and whats going on behind the scenes.html
📄 208 – Section Overview.srt
📄 220 – Optional Reloading Colab Notebook.mp4
📄 211 – Introduction to Google Colab example notebook.txt
📄 228 – Yann LeCuns OG of deep learning Tweet on Batch Sizes.txt
📄 210 – Setting Up With Google.html
📄 239 – Training Your Deep Neural Network.srt
📄 236 – Summarizing Our Model.mp4
📄 217 – Loading TensorFlow 20 into a Colab Notebook if it isnt the default.txt
📄 215 – Setting Up Our Data 2.srt
📄 226 – Preprocess Images 2.mp4
📄 211 – Setting Up Google Colab.srt
📄 216 – Importing TensorFlow 2.srt
📄 230 – Preparing Our Inputs and Outputs.mp4
📄 211 – Google Colab our workspace for the upcoming project.txt
📄 221 – Loading Our Data Labels.mp4
📄 242 – TensorFlow documentation for the unbatch function.txt
📄 234 – Building A Deep Learning Model 3.srt
📄 245 – Visualizing And Evaluate Model Predictions 3.srt
📄 250 – Making Predictions On Our Images.mp4
📄 235 – Building A Deep Learning Model 4.mp4
📄 225 – Preprocess Images.srt
📄 219 – Introduction to Google Colab example notebook.txt
📄 232 – TensorFlow Hub resource for pretrained deep learning models and more.txt
📄 220 – Optional Reloading Colab Notebook.srt
📄 217 – Optional TensorFlow 20 Default Issue.mp4
📄 211 – Endtoend Dog Vision Notebook the project well be working through.txt
📄 211 – Google Colab IO example how to get data in and out of your Colab notebook.txt
📄 241 – Make And Transform Predictions.mp4
📄 250 – Making Predictions On Our Images.srt
📄 222 – Preparing The Images.mp4
📄 218 – Using A GPU.srt
📄 212 – Google Colab FAQ things you should know about Google Colab.txt
📄 219 – Optional GPU and Google Colab.mp4
📄 249 – Dog Vision Predictions with MobileNetV2 Ready for Kaggle Submission.txt
📄 227 – Turning Data Into Batches.mp4
📄 230 – TensorFlow Hub resource for pretrained deep learning models and more.txt
📄 242 – Transform Predictions To Text.mp4
📄 248 – Dog Vision Prediction Probabilities Array.txt
📄 213 – Google Colab IO example how to get data in and out of your Colab notebook.txt
📄 214 – Setting Up Our Data.srt
📄 241 – Make And Transform Predictions.srt
📄 211 – Setting Up Google Colab.mp4
📄 222 – Preparing The Images.srt
📄 233 – Building A Deep Learning Model 2.mp4
📄 212 – Google Colab Workspace.mp4
📄 250 – Endtoend Dog Vision Notebook from the videos.txt
📄 239 – Training Your Deep Neural Network.mp4
📄 211 – Kaggle Dog Breed Identification Competition the basis of our upcoming project.txt
📄 249 – Submitting Model to Kaggle.mp4
📄 234 – MobileNetV2 the model were using architecture explanation by SikHo Tsang.txt
📄 228 – Turning Data Into Batches 2.mp4
📄 247 – Training Model On Full Dataset.srt
📄 248 – Making Predictions On Test Images.srt
📄 232 – Papers with Code a great resource for .txt
📄 236 – Summarizing Our Model.srt
📄 214 – Setting Up Our Data.mp4
📄 240 – Evaluating Performance With TensorBoard.srt
📄 224 – Creating Our Own Validation Set.srt
📄 229 – Visualizing Our Data.srt
📄 251 – Finishing Dog Vision Where to next.html
📄 238 – Preventing Overfitting.mp4
📄 225 – Preprocess Images.mp4
📄 213 – Uploading Project Data.mp4
📄 245 – Visualizing And Evaluate Model Predictions 3.mp4
📄 223 – Turning Data Labels Into Numbers.mp4
📄 244 – Visualizing And Evaluate Model Predictions 2.srt
📄 224 – Creating Our Own Validation Set.mp4
📄 228 – Turning Data Into Batches 2.srt
📄 209 – Deep Learning and Unstructured Data.srt
📄 208 – Section Overview.mp4
📄 232 – MobileNetV2 the model were using on TensorFlow Hub.txt
📄 215 – Setting Up Our Data 2.mp4
📄 237 – Evaluating Our Model.mp4
📄 246 – Saving And Loading A Trained Model.mp4
📄 238 – Preventing Overfitting.srt
📄 218 – Using A GPU.mp4
📄 242 – Transform Predictions To Text.srt
📄 223 – Turning Data Labels Into Numbers.srt
📄 216 – Importing TensorFlow 2.mp4
📄 238 – Early Stopping Callback a way to stop your model from training when it stops .txt
📄 218 – Google Colab example GPU usage.txt
📄 209 – Deep Learning and Unstructured Data.mp4
📄 213 – Kaggle Dog Breed Identification Competition Data.txt
📄 219 – Optional GPU and Google Colab.srt
📄 243 – Visualizing Model Predictions.srt
📄 225 – TensorFlow guidelines for loading all kinds of data turning your data into Tensors.txt
📄 232 – PyTorch Hub PyTorch version of TensorFlow Hub.txt
📄 230 – Preparing Our Inputs and Outputs.srt
📄 237 – TensorBoard Callback Documentation.txt
📄 219 – Google Colab Example of GPU speed up versus CPU.txt
📄 237 – Evaluating Our Model.srt
📄 233 – Keras in TensorFlow Overview Documentation.txt
📄 232 – Building A Deep Learning Model.mp4
📄 226 – Preprocess Images 2.srt
📄 225 – Documentation for loading images in TensorFlow.txt
📄 233 – Building A Deep Learning Model 2.srt
📄 229 – Visualizing Our Data.mp4
📄 249 – Submitting Model to Kaggle.srt
📄 232 – Building A Deep Learning Model.srt
📄 201 – OLTP vs OLAP.txt
📄 198 – What Is A Data Engineer 2.mp4
📄 199 – What Is A Data Engineer 3.srt
📄 207 – Kafka and Stream Processing.srt
📄 199 – What Is A Data Engineer 3.mp4
📄 196 – Kaggle.txt
📄 204 – Optional Learn SQL.html
📄 195 – Data Engineering Introduction.srt
📄 205 – Hadoop HDFS and MapReduce.mp4
📄 207 – Kafka and Stream Processing.mp4
📄 206 – Apache Spark and Apache Flink.mp4
📄 205 – Hadoop HDFS and MapReduce.srt
📄 201 – Types Of Databases.srt
📄 201 – A Primer on ACID Transactions.txt
📄 200 – What Is A Data Engineer 4.srt
📄 202 – Quick Note Upcoming Video.html
📄 198 – What Is A Data Engineer 2.srt
📄 203 – Optional OLTP Databases.srt
📄 206 – Apache Spark and Apache Flink.srt
📄 201 – Types Of Databases.mp4
📄 197 – What Is A Data Engineer.mp4
📄 196 – What Is Data.mp4
📄 195 – Data Engineering Introduction.mp4
📄 203 – Optional OLTP Databases.mp4
📄 197 – What Is A Data Engineer.srt
📄 200 – What Is A Data Engineer 4.mp4
📄 196 – What Is Data.srt
📄 7 – Teachable Machine.txt
📄 9 – Machine Learning Playground.txt
📄 5 – What Is Machine Learning.mp4
📄 6 – AIMachine LearningData Science.mp4
📄 8 – How Did We Get Here.srt
📄 10 – Types of Machine Learning.srt
📄 13 – Section Review.srt
📄 12 – What Is Machine Learning Round 2.srt
📄 10 – Types of Machine Learning.mp4
📄 9 – Exercise YouTube Recommendation Engine.mp4
📄 14 – Monthly Coding Challenges Free Resources and Guides.html
📄 12 – What Is Machine Learning Round 2.mp4
📄 13 – Section Review.mp4
📄 9 – Exercise YouTube Recommendation Engine.srt
📄 6 – AIMachine LearningData Science.srt
📄 11 – Are You Getting It Yet.html
📄 5 – What Is Machine Learning.srt
📄 7 – Exercise Machine Learning Playground.mp4
📄 8 – How Did We Get Here.mp4
📄 7 – Exercise Machine Learning Playground.srt
📄 36 – Conda Environments.mp4
📄 44 – Jupyter Notebook Walkthrough 2.srt
📄 35 – conda-cheatsheet.pdf
📄 38 – Mac Environment Setup 2.srt
📄 43 – heart-disease.csv
📄 34 – Introducing Our Tools.srt
📄 43 – 6-step-ml-framework.png
📄 36 – Conda Environments.srt
📄 37 – Mac Environment Setup.srt
📄 41 – Linux Environment Setup.html
📄 43 – Dataquest Jupyter Notebook for Beginners Tutorial.txt
📄 45 – Jupyter Notebook Walkthrough 3.mp4
📄 38 – Mac Environment Setup 2.mp4
📄 33 – Section Overview.srt
📄 45 – Jupyter Notebook Walkthrough 3.srt
📄 37 – Miniconda download documentation.txt
📄 40 – Windows Environment Setup 2.srt
📄 40 – Windows Environment Setup 2.mp4
📄 35 – Getting your computer ready for machine learning How what and why you should use Anaconda Miniconda and Conda blog post.txt
📄 39 – Windows Environment Setup.srt
📄 37 – Mac Environment Setup.mp4
📄 43 – Jupyter Notebook Walkthrough.mp4
📄 39 – Miniconda download documentation.txt
📄 39 – Windows Environment Setup.mp4
📄 43 – Jupyter Notebook documentation.txt
📄 35 – What is Conda.mp4
📄 42 – Conda documentation on sharing an environment.txt
📄 43 – Jupyter Notebook Walkthrough.srt
📄 35 – Conda documentation.txt
📄 33 – Section Overview.mp4
📄 35 – Getting started with Conda documentation.txt
📄 35 – What is Conda.srt
📄 44 – Jupyter Notebook Walkthrough 2.mp4
📄 42 – Sharing your Conda Environment.html
📄 285 – Math Functions.srt
📄 284 – Floating point numbers.txt
📄 300 – String Methods.txt
📄 276 – Replit.txt
📄 321 – Sets.srt
📄 298 – Exercise Repl.txt
📄 302 – Exercise Type Conversion.srt
📄 292 – Augmented Assignment Operator.srt
📄 287 – Operator Precedence.srt
📄 300 – Built in Functions.txt
📄 297 – Exercise Repl.txt
📄 303 – DEVELOPER FUNDAMENTALS II.srt
📄 318 – Dictionary Methods 2.srt
📄 298 – String Indexes.mp4
📄 306 – List Slicing.mp4
📄 298 – String Indexes.srt
📄 317 – Dictionary Methods.srt
📄 306 – Exercise Repl.txt
📄 279 – Python 2 vs Python 3.txt
📄 308 – List Methods.mp4
📄 316 – Dictionary Keys.mp4
📄 295 – Type Conversion.mp4
📄 322 – Exercise Repl.txt
📄 311 – Common List Patterns.mp4
📄 302 – Exercise Type Conversion.mp4
📄 306 – List Slicing.srt
📄 308 – List Methods.srt
📄 286 – DEVELOPER FUNDAMENTALS I.mp4
📄 309 – List Methods 2.mp4
📄 277 – Our First Python Program.mp4
📄 318 – Exercise Repl.txt
📄 307 – Matrix.srt
📄 307 – Matrix.mp4
📄 304 – Exercise Password Checker.srt
📄 281 – Learning Python.mp4
📄 307 – Exercise Repl.txt
📄 310 – List Methods 3.mp4
📄 289 – Optional bin and complex.srt
📄 297 – Formatted Strings.mp4
📄 279 – Python 2 vs Python 3 another one.txt
📄 319 – Tuples.srt
📄 290 – Python Keywords.txt
📄 277 – Our First Python Program.srt
📄 287 – Operator Precedence.mp4
📄 288 – Exercise Repl.txt
📄 279 – The Story of Python.txt
📄 284 – Numbers.srt
📄 299 – Immutability.srt
📄 295 – Type Conversion.srt
📄 322 – Sets Methods.txt
📄 317 – Dictionary Methods.txt
📄 310 – List Methods 3.srt
📄 301 – Booleans.mp4
📄 314 – Dictionaries.srt
📄 281 – Learning Python.srt
📄 318 – Dictionary Methods 2.mp4
📄 313 – None.mp4
📄 300 – BuiltIn Functions Methods.mp4
📄 311 – Exercise Repl.txt
📄 280 – Exercise How Does Python Work.mp4
📄 300 – BuiltIn Functions Methods.srt
📄 308 – List Methods.txt
📄 287 – Exercise Repl.txt
📄 284 – Numbers.mp4
📄 304 – Exercise Password Checker.mp4
📄 290 – Variables.srt
📄 311 – Common List Patterns.srt
📄 274 – What Is A Programming Language.srt
📄 317 – Dictionary Methods.mp4
📄 276 – How To Run Python Code.srt
📄 312 – List Unpacking.mp4
📄 297 – Formatted Strings.srt
📄 274 – What Is A Programming Language.mp4
📄 278 – Latest Version Of Python.mp4
📄 279 – Python 2 vs Python 3.mp4
📄 313 – None.srt
📄 275 – Python Interpreter.mp4
📄 291 – Expressions vs Statements.srt
📄 285 – Math Functions.mp4
📄 315 – DEVELOPER FUNDAMENTALS III.srt
📄 289 – Base Numbers.txt
📄 288 – Exercise Operator Precedence.html
📄 294 – String Concatenation.srt
📄 294 – String Concatenation.mp4
📄 315 – DEVELOPER FUNDAMENTALS III.mp4
📄 303 – DEVELOPER FUNDAMENTALS II.mp4
📄 282 – Python Data Types.srt
📄 312 – List Unpacking.srt
📄 276 – Glotio.txt
📄 290 – Variables.mp4
📄 299 – Immutability.mp4
📄 289 – Optional bin and complex.mp4
📄 292 – Augmented Assignment Operator.mp4
📄 282 – Python Data Types.mp4
📄 305 – Lists.srt
📄 296 – Escape Sequences.mp4
📄 293 – Strings.srt
📄 303 – Python Comments Best Practices.txt
📄 309 – Exercise Repl.txt
📄 276 – How To Run Python Code.mp4
📄 309 – List Methods 2.srt
📄 280 – Exercise How Does Python Work.srt
📄 296 – Escape Sequences.srt
📄 305 – Lists.mp4
📄 320 – Tuples 2.mp4
📄 321 – Sets.mp4
📄 275 – Python Interpreter.srt
📄 316 – Dictionary Keys.srt
📄 319 – Tuples.mp4
📄 322 – Sets 2.mp4
📄 314 – Dictionaries.mp4
📄 283 – How To Succeed.html
📄 322 – Sets 2.srt
📄 293 – Strings.mp4
📄 320 – Tuple Methods.txt
📄 275 – pythonorg.txt
📄 278 – Latest Version Of Python.srt
📄 309 – Python Keywords.txt
📄 292 – Exercise Repl.txt
📄 279 – Python 2 vs Python 3.srt
📄 301 – Booleans.srt
📄 320 – Tuples 2.srt
📄 291 – Expressions vs Statements.mp4
📄 257 – Communicating With Outside World.srt
📄 254 – Communicating With Managers.mp4
📄 252 – Section Overview.srt
📄 255 – Communicating With CoWorkers.mp4
📄 257 – Communicating With Outside World.mp4
📄 258 – Storytelling.mp4
📄 253 – Communicating Your Work.mp4
📄 253 – Communicating Your Work.srt
📄 255 – Communicating With CoWorkers.srt
📄 253 – How to Think About Communicating and Sharing Your Work blog post.txt
📄 257 – Devblog by Hashnode an easy and free way to create a blog you own.txt
📄 258 – Storytelling.srt
📄 259 – Communicating and sharing your work Further reading.html
📄 254 – Communicating With Managers.srt
📄 256 – Weekend Project Principle.mp4
📄 256 – Weekend Project Principle.srt
📄 252 – Section Overview.mp4
📄 257 – fasttemplate by fastai a template you can use for your blog on GitHub Pages.txt
📄 28 – Tools We Will Use.srt
📄 15 – Section Overview.mp4
📄 19 – Types of Data.srt
📄 18 – Types of Machine Learning Problems.srt
📄 28 – Tools We Will Use.mp4
📄 19 – Types of Data.mp4
📄 18 – Types of Machine Learning Problems.mp4
📄 27 – Experimentation.srt
📄 24 – Modelling Tuning.mp4
📄 23 – Modelling Picking the Model.srt
📄 20 – Types of Evaluation.mp4
📄 27 – Experimentation.mp4
📄 15 – Section Overview.srt
📄 25 – Modelling Comparison.srt
📄 25 – Modelling Comparison.mp4
📄 17 – A 6 Step Field Guide for Machine Learning Modelling blog post.txt
📄 21 – Features In Data.mp4
📄 22 – Modelling Splitting Data.srt
📄 29 – Optional Elements of AI.html
📄 23 – Modelling Picking the Model.mp4
📄 22 – Modelling Splitting Data.mp4
📄 16 – Introducing Our Framework.mp4
📄 24 – Modelling Tuning.srt
📄 21 – Features In Data.srt
📄 17 – 6 Step Machine Learning Framework.mp4
📄 26 – Overfitting and Underfitting Definitions.html
📄 20 – Types of Evaluation.srt
📄 16 – Introducing Our Framework.srt