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Udemy - Complete Machine Learning and Data Science Bootcamp 2023 VO WEB 1080p x264
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Udemy - Complete Machine Learning and Data Science Bootcamp 2023 VO WEB 1080p x264 (Size: 29.19 GB)
| 1 - Introduction | |||
| 1 - Course Outline.mp4 | 77.27 MB | ||
| 2 - Join Our Online Classroom.mp4 | 151.56 MB | ||
| 3 - Exercise Meet Your Classmates and Instructor.html | 3.74 KB | ||
| 10 - Supervised Learning Classification Regression | |||
| 150 - Milestone Projects.html | 738 B | ||
| 11 - Milestone Project 1 Supervised Learning Classification | |||
| 151 - Section Overview.mp4 | 6.99 MB | ||
| 152 - Endtoend Heart Disease Classification Notebook same as in videos.txt | 146 B | ||
| 152 - Endtoend Heart Disease Classification Notebook with annotations.txt | 140 B | ||
| 152 - Project Overview.mp4 | 27.02 MB | ||
| 152 - Structured Data Projects on GitHub.txt | 94 B | ||
| 153 - Project Environment Setup.mp4 | 174.02 MB | ||
| 154 - Optional Windows Project Environment Setup.mp4 | 58.83 MB | ||
| 155 - Step 14 Framework Setup.mp4 | 190.82 MB | ||
| 156 - Getting Our Tools Ready.mp4 | 142.35 MB | ||
| 157 - Exploring Our Data.mp4 | 118.89 MB | ||
| 157 - heart-disease.csv | 11.06 KB | ||
| 158 - Finding Patterns.mp4 | 106.47 MB | ||
| 159 - Finding Patterns 2.mp4 | 51.64 MB | ||
| 160 - Finding Patterns 3.mp4 | 248.7 MB | ||
| 161 - Preparing Our Data For Machine Learning.mp4 | 128.89 MB | ||
| 162 - Choosing The Right Models.mp4 | 174.81 MB | ||
| 163 - Experimenting With Machine Learning Models.mp4 | 100.08 MB | ||
| 164 - TuningImproving Our Model.mp4 | 102.77 MB | ||
| 165 - Tuning Hyperparameters.mp4 | 77.49 MB | ||
| 166 - Tuning Hyperparameters 2.mp4 | 188.09 MB | ||
| 167 - Tuning Hyperparameters 3.mp4 | 113.19 MB | ||
| 168 - Quick Note Confusion Matrix Labels.html | 1.11 KB | ||
| 169 - Evaluating Our Model.mp4 | 122.8 MB | ||
| 170 - Evaluating Our Model 2.mp4 | 71 MB | ||
| 171 - Evaluating Our Model 3.mp4 | 111.86 MB | ||
| 172 - Finding The Most Important Features.mp4 | 222.58 MB | ||
| 173 - Endtoend Heart Disease Classification Notebook same as in videos.txt | 146 B | ||
| 173 - Endtoend Heart Disease Classification Notebook with annotations.txt | 140 B | ||
| 173 - Reviewing The Project.mp4 | 156.89 MB | ||
| 12 - Milestone Project 2 Supervised Learning Time Series Data | |||
| 174 - Section Overview.mp4 | 15.78 MB | ||
| 175 - Endtoend Bluebook Bulldozer Regression Notebook same as in videos.txt | 153 B | ||
| 175 - Endtoend Bluebook Bulldozer Regression Notebook with annotations.txt | 147 B | ||
| 175 - Kaggle Bluebook for Bulldozers Competition.txt | 57 B | ||
| 175 - Project Overview.mp4 | 22.1 MB | ||
| 175 - Structured Data Projects on GitHub.txt | 94 B | ||
| 176 - Downloading the data for the next two projects.html | 1.64 KB | ||
| 177 - Project Environment Setup.mp4 | 179 MB | ||
| 178 - Step 14 Framework Setup.mp4 | 155.07 MB | ||
| 179 - Exploring Our Data.mp4 | 251.42 MB | ||
| 180 - Exploring Our Data 2.mp4 | 90.81 MB | ||
| 181 - Feature Engineering.mp4 | 290.84 MB | ||
| 182 - Turning Data Into Numbers.mp4 | 265.83 MB | ||
| 183 - Filling Missing Numerical Values.mp4 | 190.06 MB | ||
| 183 - Pandas Categorical Datatype Documentation.txt | 82 B | ||
| 184 - Filling Missing Categorical Values.mp4 | 117.75 MB | ||
| 185 - Fitting A Machine Learning Model.mp4 | 95.8 MB | ||
| 186 - Splitting Data.mp4 | 146.01 MB | ||
| 187 - Challenge Whats wrong with splitting data after filling it.html | 1.72 KB | ||
| 188 - Custom Evaluation Function.mp4 | 183.95 MB | ||
| 189 - Reducing Data.mp4 | 168.6 MB | ||
| 190 - RandomizedSearchCV.mp4 | 155.51 MB | ||
| 191 - Improving Hyperparameters.mp4 | 144.18 MB | ||
| 192 - Preproccessing Our Data.mp4 | 258.83 MB | ||
| 193 - Making Predictions.mp4 | 142.08 MB | ||
| 194 - Endtoend Bluebook Bulldozer Regression Notebook same as in videos.txt | 153 B | ||
| 194 - Endtoend Bluebook Bulldozer Regression Notebook with annotations.txt | 147 B | ||
| 194 - Feature Importance.mp4 | 253.96 MB | ||
| 13 - Data Engineering | |||
| 195 - Data Engineering Introduction.mp4 | 9.58 MB | ||
| 196 - Kaggle.txt | 31 B | ||
| 196 - What Is Data.mp4 | 62.89 MB | ||
| 197 - What Is A Data Engineer.mp4 | 18.76 MB | ||
| 198 - What Is A Data Engineer 2.mp4 | 24.21 MB | ||
| 199 - What Is A Data Engineer 3.mp4 | 20.99 MB | ||
| 200 - What Is A Data Engineer 4.mp4 | 11.3 MB | ||
| 201 - A Primer on ACID Transactions.txt | 56 B | ||
| 201 - OLTP vs OLAP.txt | 65 B | ||
| 201 - Types Of Databases.mp4 | 41.74 MB | ||
| 202 - Quick Note Upcoming Video.html | 481 B | ||
| 203 - Optional OLTP Databases.mp4 | 47.47 MB | ||
| 204 - Optional Learn SQL.html | 410 B | ||
| 205 - Hadoop HDFS and MapReduce.mp4 | 6.92 MB | ||
| 206 - Apache Spark and Apache Flink.mp4 | 4.4 MB | ||
| 207 - Kafka and Stream Processing.mp4 | 22.92 MB | ||
| 14 - Neural Networks Deep Learning Transfer Learning and TensorFlow 2 | |||
| 208 - Section Overview.mp4 | 15.09 MB | ||
| 209 - Deep Learning and Unstructured Data.mp4 | 119.78 MB | ||
| 210 - Setting Up With Google.html | 568 B | ||
| 211 - Endtoend Dog Vision Notebook the project well be working through.txt | 121 B | ||
| 211 - Google Colab IO example how to get data in and out of your Colab notebook.txt | 52 B | ||
| 211 - Google Colab our workspace for the upcoming project.txt | 34 B | ||
| 211 - Introduction to Google Colab example notebook.txt | 55 B | ||
| 211 - Kaggle Dog Breed Identification Competition the basis of our upcoming project.txt | 58 B | ||
| 211 - Setting Up Google Colab.mp4 | 80.31 MB | ||
| 212 - Google Colab FAQ things you should know about Google Colab.txt | 49 B | ||
| 212 - Google Colab Workspace.mp4 | 58.5 MB | ||
| 212 - Google Colab our workspace for the upcoming project.txt | 34 B | ||
| 213 - Google Colab IO example how to get data in and out of your Colab notebook.txt | 52 B | ||
| 213 - Kaggle Dog Breed Identification Competition Data.txt | 54 B | ||
| 213 - Uploading Project Data.mp4 | 91.84 MB | ||
| 214 - Setting Up Our Data.mp4 | 75.89 MB | ||
| 215 - Setting Up Our Data 2.mp4 | 37.36 MB | ||
| 216 - Importing TensorFlow 2.mp4 | 210.55 MB | ||
| 217 - Loading TensorFlow 20 into a Colab Notebook if it isnt the default.txt | 68 B | ||
| 217 - Optional TensorFlow 20 Default Issue.mp4 | 34.96 MB | ||
| 218 - Google Colab example GPU usage.txt | 53 B | ||
| 218 - Using A GPU.mp4 | 146.32 MB | ||
| 219 - Google Colab Example of GPU speed up versus CPU.txt | 53 B | ||
| 219 - Introduction to Google Colab example notebook.txt | 55 B | ||
| 219 - Optional GPU and Google Colab.mp4 | 69.05 MB | ||
| 220 - Optional Reloading Colab Notebook.mp4 | 167.03 MB | ||
| 221 - Documentation on how many images Google recommends for image problems.txt | 68 B | ||
| 221 - Loading Our Data Labels.mp4 | 205.93 MB | ||
| 222 - Preparing The Images.mp4 | 243.6 MB | ||
| 223 - Turning Data Labels Into Numbers.mp4 | 195.68 MB | ||
| 224 - Blog post by Rachel Thomas of fastai on how and why you should create a validation set.txt | 47 B | ||
| 224 - Creating Our Own Validation Set.mp4 | 94.83 MB | ||
| 225 - Documentation for loading images in TensorFlow.txt | 53 B | ||
| 225 - Preprocess Images.mp4 | 158.13 MB | ||
| 225 - TensorFlow guidelines for loading all kinds of data turning your data into Tensors.txt | 37 B | ||
| 226 - Preprocess Images 2.mp4 | 190.39 MB | ||
| 227 - Turning Data Into Batches.mp4 | 155.25 MB | ||
| 228 - Turning Data Into Batches 2.mp4 | 265.76 MB | ||
| 228 - Yann LeCuns OG of deep learning Tweet on Batch Sizes.txt | 57 B | ||
| 229 - Visualizing Our Data.mp4 | 222.05 MB | ||
| 230 - Preparing Our Inputs and Outputs.mp4 | 84.68 MB | ||
| 230 - TensorFlow Hub resource for pretrained deep learning models and more.txt | 18 B | ||
| 231 - Optional How machines learn and whats going on behind the scenes.html | 2.72 KB | ||
| 232 - Andrei Karpathys talk on AI at Tesla.txt | 34 B | ||
| 232 - Building A Deep Learning Model.mp4 | 224.53 MB | ||
| 232 - MobileNetV2 the model were using on TensorFlow Hub.txt | 71 B | ||
| 232 - Papers with Code a great resource for some of the best machine learning papers with code examples.txt | 27 B | ||
| 232 - PyTorch Hub PyTorch version of TensorFlow Hub.txt | 24 B | ||
| 232 - TensorFlow Hub resource for pretrained deep learning models and more.txt | 18 B | ||
| 233 - Building A Deep Learning Model 2.mp4 | 192.16 MB | ||
| 233 - Keras in TensorFlow Overview Documentation.txt | 47 B | ||
| 234 - Building A Deep Learning Model 3.mp4 | 197.58 MB | ||
| 234 - MobileNetV2 the model were using architecture explanation by SikHo Tsang.txt | 102 B | ||
| 234 - Step by step breakdown of a convolutional neural network what MobileNetV2 is made of.txt | 111 B | ||
| 234 - The Softmax Function activation function we use in our model.txt | 46 B | ||
| 235 - Article How to choose loss & activation functions when building a deep learning model.txt | 108 B | ||
| 235 - Building A Deep Learning Model 4.mp4 | 155.09 MB | ||
| 236 - Summarizing Our Model.mp4 | 82.03 MB | ||
| 237 - Evaluating Our Model.mp4 | 116.01 MB | ||
| 237 - TensorBoard Callback Documentation.txt | 73 B | ||
| 238 - Early Stopping Callback a way to stop your model from training when it stops improving Documentation.txt | 0 B | ||
| 238 - Preventing Overfitting.mp4 | 54.9 MB | ||
| 239 - Training Your Deep Neural Network.mp4 | 297.23 MB | ||
| 240 - Evaluating Performance With TensorBoard.mp4 | 132.89 MB | ||
| 241 - Make And Transform Predictions.mp4 | 279.06 MB | ||
| 242 - TensorFlow documentation for the unbatch function.txt | 66 B | ||
| 242 - Transform Predictions To Text.mp4 | 228.54 MB | ||
| 243 - Visualizing Model Predictions.mp4 | 209.51 MB | ||
| 244 - Visualizing And Evaluate Model Predictions 2.mp4 | 253.7 MB | ||
| 245 - Visualizing And Evaluate Model Predictions 3.mp4 | 74.92 MB | ||
| 246 - Saving And Loading A Trained Model.mp4 | 226.83 MB | ||
| 247 - Training Model On Full Dataset.mp4 | 80.94 MB | ||
| 248 - Dog Vision Prediction Probabilities Array.txt | 109 B | ||
| 248 - Making Predictions On Test Images.mp4 | 245.09 MB | ||
| 249 - Dog Vision Predictions with MobileNetV2 Ready for Kaggle Submission.txt | 119 B | ||
| 249 - Submitting Model to Kaggle.mp4 | 74.88 MB | ||
| 250 - Endtoend Dog Vision Notebook from the videos.txt | 130 B | ||
| 250 - Endtoend Dog Vision Notebook with annotations.txt | 124 B | ||
| 250 - Making Predictions On Our Images.mp4 | 119.39 MB | ||
| 251 - Finishing Dog Vision Where to next.html | 3.86 KB | ||
| 15 - Storytelling Communication How To Present Your Work | |||
| 252 - Section Overview.mp4 | 11.41 MB | ||
| 253 - Communicating Your Work.mp4 | 21.12 MB | ||
| 253 - How to Think About Communicating and Sharing Your Work blog post.txt | 81 B | ||
| 254 - Communicating With Managers.mp4 | 10.94 MB | ||
| 255 - Communicating With CoWorkers.mp4 | 11.39 MB | ||
| 256 - Weekend Project Principle.mp4 | 14.81 MB | ||
| 257 - Communicating With Outside World.mp4 | 9.16 MB | ||
| 257 - Devblog by Hashnode an easy and free way to create a blog you own.txt | 28 B | ||
| 257 - fasttemplate by fastai a template you can use for your blog on GitHub Pages.txt | 45 B | ||
| 258 - Storytelling.mp4 | 6.86 MB | ||
| 259 - Communicating and sharing your work Further reading.html | 3.14 KB | ||
| 16 - Career Advice Extra Bits | |||
| 260 - Endorsements On LinkedIn.html | 1.37 KB | ||
| 261 - Quick Note Upcoming Video.html | 587 B | ||
| 262 - What If I Dont Have Enough Experience.mp4 | 312.14 MB | ||
| 263 - Learning Guideline.html | 336 B | ||
| 264 - Quick Note Upcoming Videos.html | 565 B | ||
| 265 - JTS Learn to Learn.mp4 | 10.42 MB | ||
| 266 - JTS Start With Why.mp4 | 14.27 MB | ||
| 267 - Quick Note Upcoming Videos.html | 352 B | ||
| 268 - CWD Git Github.mp4 | 361.96 MB | ||
| 269 - CWD Git Github 2.mp4 | 228.66 MB | ||
| 270 - Contributing To Open Source.mp4 | 235.6 MB | ||
| 271 - Contributing To Open Source 2.mp4 | 213.67 MB | ||
| 272 - Exercise Contribute To Open Source.html | 1.9 KB | ||
| 273 - Coding Challenges.html | 948 B | ||
| 17 - Learn Python | |||
| 274 - What Is A Programming Language.mp4 | 87.87 MB | ||
| 275 - Python Interpreter.mp4 | 138.04 MB | ||
| 275 - pythonorg.txt | 23 B | ||
| 276 - Glotio.txt | 16 B | ||
| 276 - How To Run Python Code.mp4 | 72.35 MB | ||
| 276 - Replit.txt | 16 B | ||
| 277 - Our First Python Program.mp4 | 62.47 MB | ||
| 278 - Latest Version Of Python.mp4 | 12.95 MB | ||
| 279 - Python 2 vs Python 3 another one.txt | 100 B | ||
| 279 - Python 2 vs Python 3.mp4 | 144.02 MB | ||
| 279 - Python 2 vs Python 3.txt | 67 B | ||
| 279 - The Story of Python.txt | 43 B | ||
| 280 - Exercise How Does Python Work.mp4 | 28.84 MB | ||
| 281 - Learning Python.mp4 | 12.06 MB | ||
| 282 - Python Data Types.mp4 | 19.17 MB | ||
| 283 - How To Succeed.html | 280 B | ||
| 284 - Floating point numbers.txt | 43 B | ||
| 284 - Numbers.mp4 | 80.19 MB | ||
| 285 - Math Functions.mp4 | 18.2 MB | ||
| 286 - DEVELOPER FUNDAMENTALS I.mp4 | 107.13 MB | ||
| 287 - Exercise Repl.txt | 45 B | ||
| 287 - Operator Precedence.mp4 | 8.34 MB | ||
| 288 - Exercise Operator Precedence.html | 683 B | ||
| 288 - Exercise Repl.txt | 45 B | ||
| 289 - Base Numbers.txt | 50 B | ||
| 289 - Optional bin and complex.mp4 | 23.73 MB | ||
| 290 - Python Keywords.txt | 56 B | ||
| 290 - Variables.mp4 | 99.55 MB | ||
| 291 - Expressions vs Statements.mp4 | 4.8 MB | ||
| 292 - Augmented Assignment Operator.mp4 | 8.46 MB | ||
| 292 - Exercise Repl.txt | 55 B | ||
| 293 - Strings.mp4 | 21.07 MB | ||
| 294 - String Concatenation.mp4 | 3.76 MB | ||
| 295 - Type Conversion.mp4 | 24.93 MB | ||
| 296 - Escape Sequences.mp4 | 12.95 MB | ||
| 297 - Exercise Repl.txt | 43 B | ||
| 297 - Formatted Strings.mp4 | 33.11 MB | ||
| 298 - Exercise Repl.txt | 40 B | ||
| 298 - String Indexes.mp4 | 25.64 MB | ||
| 299 - Immutability.mp4 | 12.69 MB | ||
| 300 - Built in Functions.txt | 48 B | ||
| 300 - BuiltIn Functions Methods.mp4 | 90.98 MB | ||
| 300 - String Methods.txt | 54 B | ||
| 301 - Booleans.mp4 | 17.57 MB | ||
| 302 - Exercise Type Conversion.mp4 | 39.03 MB | ||
| 303 - DEVELOPER FUNDAMENTALS II.mp4 | 39.49 MB | ||
| 303 - Python Comments Best Practices.txt | 45 B | ||
| 304 - Exercise Password Checker.mp4 | 38.72 MB | ||
| 305 - Lists.mp4 | 12.97 MB | ||
| 306 - Exercise Repl.txt | 31 B | ||
| 306 - List Slicing.mp4 | 26.8 MB | ||
| 307 - Exercise Repl.txt | 32 B | ||
| 307 - Matrix.mp4 | 12.85 MB | ||
| 308 - List Methods.mp4 | 65.1 MB | ||
| 308 - List Methods.txt | 52 B | ||
| 309 - Exercise Repl.txt | 33 B | ||
| 309 - List Methods 2.mp4 | 29.2 MB | ||
| 309 - Python Keywords.txt | 56 B | ||
| 310 - List Methods 3.mp4 | 29.46 MB | ||
| 311 - Common List Patterns.mp4 | 30.72 MB | ||
| 311 - Exercise Repl.txt | 33 B | ||
| 312 - List Unpacking.mp4 | 9.3 MB | ||
| 313 - None.mp4 | 4.49 MB | ||
| 314 - Dictionaries.mp4 | 18.74 MB | ||
| 315 - DEVELOPER FUNDAMENTALS III.mp4 | 14.91 MB | ||
| 316 - Dictionary Keys.mp4 | 11.93 MB | ||
| 317 - Dictionary Methods.mp4 | 15.01 MB | ||
| 317 - Dictionary Methods.txt | 58 B | ||
| 318 - Dictionary Methods 2.mp4 | 46.3 MB | ||
| 318 - Exercise Repl.txt | 36 B | ||
| 319 - Tuples.mp4 | 15.07 MB | ||
| 320 - Tuple Methods.txt | 53 B | ||
| 320 - Tuples 2.mp4 | 11.57 MB | ||
| 321 - Sets.mp4 | 39.02 MB | ||
| 322 - Exercise Repl.txt | 30 B | ||
| 322 - Sets 2.mp4 | 70.81 MB | ||
| 322 - Sets Methods.txt | 51 B | ||
| 18 - Learn Python Part 2 | |||
| 323 - Breaking The Flow.mp4 | 12.73 MB | ||
| 324 - Conditional Logic.mp4 | 92.52 MB | ||
| 325 - Indentation In Python.mp4 | 21.98 MB | ||
| 326 - Truthy vs Falsey Stackoverflow.txt | 109 B | ||
| 326 - Truthy vs Falsey.mp4 | 73.71 MB | ||
| 327 - Ternary Operator.mp4 | 12.51 MB | ||
| 328 - Short Circuiting.mp4 | 12.23 MB | ||
| 329 - Logical Operators.mp4 | 25.36 MB | ||
| 330 - Exercise Logical Operators.mp4 | 37.05 MB | ||
| 331 - is vs.mp4 | 30.78 MB | ||
| 332 - For Loops.mp4 | 25.22 MB | ||
| 333 - Iterables.mp4 | 54.29 MB | ||
| 334 - Exercise Tricky Counter.mp4 | 14.14 MB | ||
| 334 - Solution Repl.txt | 31 B | ||
| 335 - range.mp4 | 34.29 MB | ||
| 336 - enumerate.mp4 | 17.66 MB | ||
| 337 - While Loops.mp4 | 21.04 MB | ||
| 338 - While Loops 2.mp4 | 16.74 MB | ||
| 339 - break continue pass.mp4 | 14.12 MB | ||
| 340 - Exercise Repl.txt | 38 B | ||
| 340 - Our First GUI.mp4 | 79.47 MB | ||
| 340 - Solution Repl.txt | 38 B | ||
| 341 - DEVELOPER FUNDAMENTALS IV.mp4 | 41.33 MB | ||
| 342 - Exercise Find Duplicates.mp4 | 15.82 MB | ||
| 342 - Solution Repl.txt | 41 B | ||
| 343 - Functions.mp4 | 34.06 MB | ||
| 344 - Parameters and Arguments.mp4 | 17.45 MB | ||
| 345 - Default Parameters and Keyword Arguments.mp4 | 29.14 MB | ||
| 346 - return.mp4 | 56.71 MB | ||
| 347 - Exercise Tesla.html | 402 B | ||
| 348 - Methods vs Functions.mp4 | 37.64 MB | ||
| 349 - Docstrings.mp4 | 15.72 MB | ||
| 350 - Clean Code.mp4 | 13.03 MB | ||
| 351 - args and kwargs.mp4 | 33.08 MB | ||
| 352 - Exercise Functions.mp4 | 16.38 MB | ||
| 352 - Solution Repl.txt | 47 B | ||
| 353 - Scope.mp4 | 12.48 MB | ||
| 354 - Scope Rules.mp4 | 29.31 MB | ||
| 355 - global Keyword.mp4 | 33.36 MB | ||
| 356 - Solution Repl.txt | 34 B | ||
| 356 - nonlocal Keyword.mp4 | 14.54 MB | ||
| 357 - Why Do We Need Scope.mp4 | 16.05 MB | ||
| 358 - Pure Functions.mp4 | 60.35 MB | ||
| 359 - map.mp4 | 48.26 MB | ||
| 360 - filter.mp4 | 14.97 MB | ||
| 361 - zip.mp4 | 16.01 MB | ||
| 362 - reduce.mp4 | 65.82 MB | ||
| 363 - List Comprehensions.mp4 | 38.85 MB | ||
| 364 - Set Comprehensions.mp4 | 26.61 MB | ||
| 365 - Exercise Comprehensions.mp4 | 14.59 MB | ||
| 365 - Exercise Repl.txt | 39 B | ||
| 365 - Solution Repl.txt | 41 B | ||
| 366 - Python Exam Testing Your Understanding.html | 1.12 KB | ||
| 367 - Modules in Python.mp4 | 168.43 MB | ||
| 368 - Quick Note Upcoming Videos.html | 448 B | ||
| 369 - Optional PyCharm.mp4 | 65.24 MB | ||
| 370 - Packages in Python.mp4 | 109.34 MB | ||
| 371 - Different Ways To Import.mp4 | 39.03 MB | ||
| 372 - Next Steps.html | 959 B | ||
| 373 - Bonus Resource Python Cheatsheet.html | 489 B | ||
| 19 - Extra Learn Advanced Statistics and Mathematics for FREE | |||
| 374 - Statistics and Mathematics.html | 710 B | ||
| 2 - Machine Learning 101 | |||
| 10 - Types of Machine Learning.mp4 | 14.82 MB | ||
| 11 - Are You Getting It Yet.html | 160 B | ||
| 12 - What Is Machine Learning Round 2.mp4 | 18.9 MB | ||
| 13 - Section Review.mp4 | 3.98 MB | ||
| 14 - Monthly Coding Challenges Free Resources and Guides.html | 1.6 KB | ||
| 5 - What Is Machine Learning.mp4 | 28.3 MB | ||
| 6 - AIMachine LearningData Science.mp4 | 19.67 MB | ||
| 7 - Exercise Machine Learning Playground.mp4 | 42.62 MB | ||
| 7 - Teachable Machine.txt | 40 B | ||
| 8 - How Did We Get Here.mp4 | 30.5 MB | ||
| 9 - Exercise YouTube Recommendation Engine.mp4 | 12.92 MB | ||
| 9 - Machine Learning Playground.txt | 27 B | ||
| 20 - Where To Go From Here | |||
| 375 - Become An Alumni.html | 944 B | ||
| 376 - Thank You.mp4 | 15.4 MB | ||
| 377 - Thank You Part 2.html | 730 B | ||
| 21 - BONUS SECTION | |||
| 378 - Special Bonus Lecture.html | 1.22 KB | ||
| 3 - Machine Learning and Data Science Framework | |||
| 15 - Section Overview.mp4 | 9.48 MB | ||
| 16 - Introducing Our Framework.mp4 | 6.24 MB | ||
| 17 - 6 Step Machine Learning Framework.mp4 | 23.47 MB | ||
| 17 - A 6 Step Field Guide for Machine Learning Modelling blog post.txt | 86 B | ||
| 18 - Types of Machine Learning Problems.mp4 | 32.62 MB | ||
| 19 - Types of Data.mp4 | 33.2 MB | ||
| 20 - Types of Evaluation.mp4 | 9.62 MB | ||
| 21 - Features In Data.mp4 | 28.83 MB | ||
| 22 - Modelling Splitting Data.mp4 | 19.89 MB | ||
| 23 - Modelling Picking the Model.mp4 | 12.88 MB | ||
| 24 - Modelling Tuning.mp4 | 9.14 MB | ||
| 25 - Modelling Comparison.mp4 | 26.84 MB | ||
| 26 - Overfitting and Underfitting Definitions.html | 1.97 KB | ||
| 27 - Experimentation.mp4 | 18.71 MB | ||
| 28 - Tools We Will Use.mp4 | 20.36 MB | ||
| 29 - Optional Elements of AI.html | 975 B | ||
| 4 - The 2 Paths | |||
| 30 - The 2 Paths.mp4 | 6.83 MB | ||
| 31 - Python Machine Learning Monthly.html | 917 B | ||
| 32 - Endorsements On LinkedIN.html | 1.37 KB | ||
| 5 - Data Science Environment Setup | |||
| 1669295595.mp4mtwda23s.tmp | 8 MB | ||
| 33 - Section Overview.mp4 | 3.88 MB | ||
| 35 - Conda documentation.txt | 32 B | ||
| 35 - Getting started with Conda documentation.txt | 78 B | ||
| 35 - Getting your computer ready for machine learning How what and why you should use Anaconda Miniconda and Conda blog post.txt | 106 B | ||
| 35 - What is Conda.mp4 | 7.26 MB | ||
| 35 - conda-cheatsheet.pdf | 201.29 KB | ||
| 36 - Conda Environments.mp4 | 28.5 MB | ||
| 37 - Mac Environment Setup.mp4 | 261.42 MB | ||
| 37 - Miniconda download documentation.txt | 46 B | ||
| 38 - Mac Environment Setup 2.mp4 | 222.79 MB | ||
| 39 - Miniconda download documentation.txt | 46 B | ||
| 39 - Windows Environment Setup.mp4 | 58.91 MB | ||
| 40 - Windows Environment Setup 2.mp4 | 416.87 MB | ||
| 41 - Linux Environment Setup.html | 1.03 KB | ||
| 42 - Conda documentation on sharing an environment.txt | 111 B | ||
| 42 - Sharing your Conda Environment.html | 2.41 KB | ||
| 43 - 6-step-ml-framework.png | 324.24 KB | ||
| 43 - Dataquest Jupyter Notebook for Beginners Tutorial.txt | 56 B | ||
| 43 - Jupyter Notebook Walkthrough.mp4 | 101.98 MB | ||
| 43 - Jupyter Notebook documentation.txt | 50 B | ||
| 43 - heart-disease.csv | 11.06 KB | ||
| 44 - Jupyter Notebook Walkthrough 2.mp4 | 56.58 MB | ||
| 45 - Jupyter Notebook Walkthrough 3.mp4 | 127.47 MB | ||
| 6 - Pandas Data Analysis | |||
| 46 - Section Overview.mp4 | 11.33 MB | ||
| 47 - Downloading Workbooks and Assignments.html | 967 B | ||
| 48 - 10 minutes to pandas from the documentation.txt | 70 B | ||
| 48 - Introduction to Pandas Jupyter Notebook from the upcoming videos.txt | 130 B | ||
| 48 - Introduction to Pandas Jupyter Notebook with annotations.txt | 124 B | ||
| 48 - Pandas Documentation.txt | 45 B | ||
| 48 - Pandas Introduction.mp4 | 17.69 MB | ||
| 49 - Series Data Frames and CSVs.mp4 | 165.29 MB | ||
| 49 - car-sales.csv | 369 B | ||
| 49 - pandas-anatomy-of-a-dataframe.png | 333.24 KB | ||
| 50 - Data from URLs.html | 1.08 KB | ||
| 51 - Describing Data with Pandas.mp4 | 111.78 MB | ||
| 52 - Selecting and Viewing Data with Pandas.mp4 | 86.05 MB | ||
| 52 - car-sales.csv | 369 B | ||
| 53 - Selecting and Viewing Data with Pandas Part 2.mp4 | 186.47 MB | ||
| 54 - Jake VanderPlass Data Manipulation with Pandas.txt | 85 B | ||
| 54 - Manipulating Data.mp4 | 68.83 MB | ||
| 54 - car-sales-missing-data.csv | 287 B | ||
| 55 - Manipulating Data 2.mp4 | 155.34 MB | ||
| 55 - pandas-anatomy-of-a-dataframe.png | 333.24 KB | ||
| 56 - Introduction to Pandas Jupyter Notebook from the videos.txt | 130 B | ||
| 56 - Introduction to Pandas Jupyter Notebook with annotations.txt | 124 B | ||
| 56 - Manipulating Data 3.mp4 | 136.98 MB | ||
| 57 - Assignment Pandas Practice.html | 2.05 KB | ||
| 58 - Course notebooks Github.txt | 47 B | ||
| 58 - Google Colab.txt | 34 B | ||
| 58 - How To Download The Course Assignments.mp4 | 119.98 MB | ||
| 7 - NumPy | |||
| 59 - Section Overview.mp4 | 22.44 MB | ||
| 60 - Introduction to NumPy Jupyter Notebook from the upcoming videos.txt | 129 B | ||
| 60 - Introduction to NumPy Jupyter Notebook with annotations.txt | 123 B | ||
| 60 - NumPy Documentation.txt | 22 B | ||
| 60 - NumPy Introduction.mp4 | 21.32 MB | ||
| 61 - Quick Note Correction In Next Video.html | 1.24 KB | ||
| 62 - NumPy DataTypes and Attributes.mp4 | 113.59 MB | ||
| 63 - Creating NumPy Arrays.mp4 | 97.74 MB | ||
| 64 - NumPy Random Seed.mp4 | 61.5 MB | ||
| 65 - Viewing Arrays and Matrices.mp4 | 81.8 MB | ||
| 66 - Manipulating Arrays.mp4 | 118.86 MB | ||
| 66 - Standard deviation and variance explained.txt | 55 B | ||
| 67 - Manipulating Arrays 2.mp4 | 115.87 MB | ||
| 67 - Standard deviation and variance explained.txt | 55 B | ||
| 68 - Standard Deviation and Variance.mp4 | 61.5 MB | ||
| 68 - Standard deviation and variance explained.txt | 55 B | ||
| 69 - Reshape and Transpose.mp4 | 91.27 MB | ||
| 70 - Dot Product vs Element Wise.mp4 | 122.36 MB | ||
| 70 - Matrix Multiplication Explained.txt | 58 B | ||
| 71 - Exercise Nut Butter Store Sales.mp4 | 154.7 MB | ||
| 72 - Comparison Operators.mp4 | 38.23 MB | ||
| 73 - Sorting Arrays.mp4 | 38.86 MB | ||
| 74 - Introduction to NumPy Jupyter Notebook from the videos.txt | 129 B | ||
| 74 - Introduction to NumPy Jupyter Notebook with annotations.txt | 123 B | ||
| 74 - Turn Images Into NumPy Arrays.mp4 | 164.8 MB | ||
| 74 - numpy-images.zip | 7.27 MB | ||
| 75 - Exercise Imposter Syndrome.mp4 | 53.7 MB | ||
| 76 - Assignment NumPy Practice.html | 2.17 KB | ||
| 77 - Optional Extra NumPy resources.html | 1.02 KB | ||
| 8 - Matplotlib Plotting and Data Visualization | |||
| 78 - Section Overview.mp4 | 6 MB | ||
| 79 - Introduction to Matplotlib Jupyter Notebook from the upcoming videos.txt | 134 B | ||
| 79 - Matplotlib Documentation.txt | 42 B | ||
| 79 - Matplotlib Introduction.mp4 | 34.69 MB | ||
| 80 - Importing And Using Matplotlib.mp4 | 146.57 MB | ||
| 81 - Anatomy Of A Matplotlib Figure.mp4 | 119.25 MB | ||
| 81 - matplotlib-anatomy-of-a-plot-with-code.png | 654.77 KB | ||
| 81 - matplotlib-anatomy-of-a-plot.png | 369.39 KB | ||
| 82 - Scatter Plot And Bar Plot.mp4 | 95.45 MB | ||
| 83 - Histograms And Subplots.mp4 | 116.53 MB | ||
| 84 - Subplots Option 2.mp4 | 54.35 MB | ||
| 85 - Quick Tip Data Visualizations.mp4 | 8.34 MB | ||
| 86 - Plotting From Pandas DataFrames.mp4 | 89.45 MB | ||
| 87 - Quick Note Regular Expressions.html | 632 B | ||
| 88 - Plotting From Pandas DataFrames 2.mp4 | 176.81 MB | ||
| 89 - Plotting from Pandas DataFrames 3.mp4 | 127.11 MB | ||
| 90 - Plotting from Pandas DataFrames 4.mp4 | 53.06 MB | ||
| 90 - heart-disease.csv | 11.06 KB | ||
| 91 - Plotting from Pandas DataFrames 5.mp4 | 92.84 MB | ||
| 92 - Plotting from Pandas DataFrames 6.mp4 | 121 MB | ||
| 93 - Plotting from Pandas DataFrames 7.mp4 | 220.01 MB | ||
| 94 - Customizing Your Plots.mp4 | 161.13 MB | ||
| 95 - Customizing Your Plots 2.mp4 | 123.59 MB | ||
| 96 - Introduction to Matplotlib Notebook from the videos.txt | 134 B | ||
| 96 - Saving And Sharing Your Plots.mp4 | 92.56 MB | ||
| 97 - Assignment Matplotlib Practice.html | 2.05 KB | ||
| 9 - Scikitlearn Creating Machine Learning Models | |||
| 100 - Quick Note Upcoming Video.html | 390 B | ||
| 101 - Refresher What Is Machine Learning.mp4 | 33.43 MB | ||
| 102 - Quick Note Upcoming Videos.html | 1018 B | ||
| 103 - ScikitLearn Reference Notebook.txt | 133 B | ||
| 103 - Scikitlearn Cheatsheet.mp4 | 75.13 MB | ||
| 104 - Example ScikitLearn Workflow Notebook.txt | 131 B | ||
| 104 - Typical scikitlearn Workflow.mp4 | 335.88 MB | ||
| 105 - Optional Debugging Warnings In Jupyter.mp4 | 322.05 MB | ||
| 106 - Getting Your Data Ready Splitting Your Data.mp4 | 108.98 MB | ||
| 106 - scikit-learn-data.zip | 20.83 KB | ||
| 107 - Quick Tip Clean Transform Reduce.mp4 | 20.17 MB | ||
| 108 - Getting Your Data Ready Convert Data To Numbers.mp4 | 236.88 MB | ||
| 109 - Note Update to next video OneHotEncoder can handle NaNNone values.html | 1.52 KB | ||
| 110 - Getting Your Data Ready Handling Missing Values With Pandas.mp4 | 186.79 MB | ||
| 111 - Extension Feature Scaling.html | 2.93 KB | ||
| 112 - Note Correction in the upcoming video splitting data.html | 2.16 KB | ||
| 113 - Getting Your Data Ready Handling Missing Values With Scikitlearn.mp4 | 241.43 MB | ||
| 114 - NEW Choosing The Right Model For Your Data.mp4 | 435.22 MB | ||
| 114 - ScikitLearn machine learning map how to choose the right machine learning model.txt | 72 B | ||
| 115 - NEW Choosing The Right Model For Your Data 2 Regression.mp4 | 239.88 MB | ||
| 116 - Quick Note Decision Trees.html | 221 B | ||
| 117 - Quick Tip How ML Algorithms Work.mp4 | 10.92 MB | ||
| 118 - Choosing The Right Model For Your Data 3 Classification.mp4 | 216.27 MB | ||
| 119 - Fitting A Model To The Data.mp4 | 100.81 MB | ||
| 120 - Making Predictions With Our Model.mp4 | 117.91 MB | ||
| 121 - predict vs predictproba.mp4 | 56.5 MB | ||
| 122 - NEW Making Predictions With Our Model Regression.mp4 | 145.9 MB | ||
| 123 - NEW Evaluating A Machine Learning Model Score Part 1.mp4 | 153.18 MB | ||
| 124 - NEW Evaluating A Machine Learning Model Score Part 2.mp4 | 115.21 MB | ||
| 125 - Evaluating A Machine Learning Model 2 Cross Validation.mp4 | 142.19 MB | ||
| 126 - Evaluating A Classification Model 1 Accuracy.mp4 | 45.61 MB | ||
| 127 - Evaluating A Classification Model 2 ROC Curve.mp4 | 113.7 MB | ||
| 128 - Evaluating A Classification Model 3 ROC Curve.mp4 | 85.33 MB | ||
| 129 - Reading Extension ROC Curve AUC.html | 1.48 KB | ||
| 130 - Evaluating A Classification Model 4 Confusion Matrix.mp4 | 136.17 MB | ||
| 130 - Notebook from video with updated confusion matrix labels.txt | 130 B | ||
| 131 - NEW Evaluating A Classification Model 5 Confusion Matrix.mp4 | 189.56 MB | ||
| 132 - Evaluating A Classification Model 6 Classification Report.mp4 | 151.34 MB | ||
| 133 - NEW Evaluating A Regression Model 1 R2 Score.mp4 | 181.28 MB | ||
| 134 - NEW Evaluating A Regression Model 2 MAE.mp4 | 73.97 MB | ||
| 135 - NEW Evaluating A Regression Model 3 MSE.mp4 | 161.59 MB | ||
| 136 - Machine Learning Model Evaluation.html | 7.12 KB | ||
| 137 - NEW Evaluating A Model With Cross Validation and Scoring Parameter.mp4 | 410.15 MB | ||
| 138 - NEW Evaluating A Model With Scikitlearn Functions.mp4 | 241.27 MB | ||
| 139 - Improving A Machine Learning Model.mp4 | 161.3 MB | ||
| 140 - Tuning Hyperparameters.mp4 | 118.94 MB | ||
| 141 - Tuning Hyperparameters 2.mp4 | 202.73 MB | ||
| 142 - Tuning Hyperparameters 3.mp4 | 212.07 MB | ||
| 143 - Note Metric Comparison Improvement.html | 2.18 KB | ||
| 144 - Quick Tip Correlation Analysis.mp4 | 30.94 MB | ||
| 145 - Saving And Loading A Model.mp4 | 91.29 MB | ||
| 146 - Saving And Loading A Model 2.mp4 | 85.87 MB | ||
| 147 - Putting It All Together.mp4 | 261.14 MB | ||
| 147 - Reading extension ScikitLearns Pipeline class explained.txt | 85 B | ||
| 148 - Introduction to ScikitLearn Jupyter Notebook from the videos.txt | 136 B | ||
| 148 - Introduction to ScikitLearn Jupyter Notebook with annotations.txt | 130 B | ||
| 148 - Putting It All Together 2.mp4 | 211.27 MB | ||
| 149 - ScikitLearn Practice.html | 2.07 KB | ||
| 98 - Section Overview.mp4 | 8.62 MB | ||
| 99 - Introduction to ScikitLearn Jupyter Notebook from the upcoming videos.txt | 136 B | ||
| 99 - Introduction to ScikitLearn Jupyter Notebook with annotations.txt | 130 B | ||
| 99 - ScikitLearn Documentation.txt | 47 B | ||
| 99 - Scikitlearn Introduction.mp4 | 26.58 MB | ||
| ▲ 524 total files | |||
Description
Complete Machine Learning and Data Science Bootcamp 2023
Langue: VO
Il s'agit d'un cours de Machine Learning et de Data Science très populaire, mis à jour ce mois-ci avec les dernières tendances et compétences pour 2023 ! Devenez un Data Scientist et ingénieur en Machine Learning complet ! Rejoignez une communauté en ligne active de plus de 900 000 ingénieurs et suivez un cours dispensé par des experts de l'industrie ayant réellement travaillé pour de grandes entreprises à Silicon Valley et à Toronto. Les diplômés des cours d'Andrei travaillent maintenant chez Google, Tesla, Amazon, Apple, IBM, JP Morgan, Meta, et d'autres grandes entreprises technologiques. Vous passerez de zéro à la maîtrise !
Apprenez la Data Science et le Machine Learning à partir de zéro, décrochez un emploi et amusez-vous en cours de route avec le cours de Data Science le plus moderne et à jour sur Udemy (nous utilisons la dernière version de Python, Tensorflow 2.0 et d'autres bibliothèques). Ce cours est axé sur l'efficacité : ne perdez jamais de temps avec des tutoriels de Machine Learning confus, obsolètes ou incomplets. Nous sommes assez confiants que c'est le cours le plus complet et moderne que vous trouverez sur le sujet (une déclaration audacieuse, nous le savons).
Ce cours complet et basé sur des projets vous initiera à toutes les compétences modernes d'un Data Scientist, et en cours de route, nous construirons de nombreux projets concrets à ajouter à votre portfolio. Vous aurez accès à tout le code, aux cahiers de travail et aux modèles (Jupyter Notebooks) sur Github, afin de pouvoir les intégrer à votre portfolio immédiatement ! Nous croyons que ce cours résout le plus grand défi pour entrer dans le domaine de la Data Science et du Machine Learning : avoir toutes les ressources nécessaires au même endroit et apprendre les dernières tendances et les compétences requises par les employeurs.
Le programme sera très pratique, vous guidant du début à la fin pour devenir un ingénieur professionnel en Machine Learning et Data Science. Le cours couvre 2 parcours. Si vous connaissez déjà la programmation, vous pouvez plonger directement et sauter la section où nous vous enseignons Python à partir de zéro. Si vous êtes totalement novice, nous vous prendrons depuis le tout début et vous enseignerons réellement Python et comment l'utiliser dans le monde réel pour nos projets. Ne vous inquiétez pas, une fois que nous aurons abordé les bases telles que le Machine Learning 101 et Python, nous passerons ensuite à des sujets avancés tels que les Réseaux Neuronaux, le Deep Learning et le Transfer Learning afin que vous puissiez pratiquer dans des situations réelles et être prêt pour le monde réel (nous vous montrerons des projets complets de Data Science et de Machine Learning et vous fournirons des ressources et des fiches de triche en programmation) !
Les sujets abordés dans ce cours sont :
- Exploration des données et visualisations
- Réseaux neuronaux et Deep Learning
- Évaluation et analyse des modèles
- Python 3
- Tensorflow 2.0
- Numpy
- Scikit-Learn
- Projets et flux de travail de Data Science et de Machine Learning
- Visualisation des données en Python avec MatPlotLib et Seaborn
- Transfer Learning
- Reconnaissance et classification d'images
- Entraînement/Test et validation croisée
- Apprentissage supervisé : classification, régression et séries temporelles
- Arbres de décision et forêts aléatoires
- Apprentissage ensembliste
- Ajustement des hyperparamètres
- Utilisation des DataFrames Pandas pour résoudre des tâches complexes
- Utilisation de Pandas pour manipuler des fichiers CSV
- Deep Learning / Réseaux neuronaux avec TensorFlow 2.0 et Keras
- Utilisation de Kaggle et participation à des compétitions de Machine Learning
- Comment présenter vos résultats et impressionner votre patron
- Comment nettoyer et préparer vos données pour l'analyse
- K Nearest Neighbours
- Machines à vecteurs de support
- Analyse de régression (régression linéaire/régression polynomiale)
- Comment Hadoop, Apache Spark, Kafka et Apache Flink sont utilisés
- Configuration de votre environnement avec Conda, MiniConda et Jupyter Notebooks
- Utilisation des GPU avec Google Colab
À la fin de ce cours, vous serez un Data Scientist complet pouvant être embauché par de grandes entreprises. Nous allons utiliser tout ce que nous avons appris dans le cours pour construire des projets réels et professionnels tels que la détection de maladies cardiaques, le prédicteur de prix des bulldozers, le classificateur d'images de races de chiens, et bien d'autres. À la fin, vous aurez une pile de projets que vous avez construits et que vous pouvez montrer aux autres.
Voici la vérité : la plupart des cours vous enseignent la Data Science et s'arrêtent là. Ils vous montrent comment commencer. Mais le problème, c'est que vous ne savez pas où aller à partir de là ni comment construire vos propres projets. Ou bien ils vous montrent beaucoup de code et de mathématiques complexes à l'écran, mais ils n'expliquent pas vraiment assez bien les choses pour que vous puissiez résoudre par vous-même des problèmes réels de Machine Learning.
Que vous soyez nouveau dans la programmation, que vous souhaitiez améliorer vos compétences en Data Science ou que vous veniez d'un secteur différent, ce cours est fait pour vous. Ce cours ne vise pas seulement à vous faire coder sans comprendre les principes, de sorte que lorsque vous aurez terminé le cours, vous ne saurez pas quoi faire d'autre que de regarder un autre tutoriel. Non ! Ce cours vous poussera et vous mettra au défi de passer d'un débutant absolu sans expérience en Data Science à quelqu'un qui peut partir, oublier Daniel et Andrei, et construire ses propres flux de travail en Data Science et en Machine Learning.
Le Machine Learning a des applications dans le marketing et la finance, les soins de santé, la cybersécurité, la vente au détail, le transport et la logistique, l'agriculture, l'Internet des objets, les jeux et le divertissement, le diagnostic des patients, la détection de fraude, la détection d'anomalies dans la fabrication, le gouvernement, l'académie/la recherche, les systèmes de recommandation
et bien plus encore. Les compétences apprises dans ce cours vous offriront de nombreuses options pour votre carrière.
Vous entendez des termes tels que Réseau Neuronal Artificiel, ou Intelligence Artificielle (IA), et à la fin de ce cours, vous comprendrez enfin ce que cela signifie !
Qualité : WEB 1080p
Format : MP4
Vidéo : AVC à ~ 1 000 kb/s
Audio : HE-AAC 2.0 à 62,8 kb/s
Langue : Anglais
Poids : 29.19Go
Nombre de fichiers : 524
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