Udemy-机器学习与数据科学实战训练营

编辑点评

系统学习Scikit-Learn库，实战案例丰富，适合数据科学初学者。

⭐ 编辑推荐

本课程深入浅出地讲解机器学习与数据科学，通过Scikit-Learn实战，提升模型创建与评估能力。

✨ 课程亮点

课程目录

📁 9 - Scikitlearn Creating Machine Learning Models
9 - Scikitlearn Creating Machine Learning Models文档.png [493.5 KB]
98 - Section Overview.srt [4.3 KB]
136 - Machine Learning Model Evaluation.html [7.1 KB]
138 - NEW Evaluating A Model With Scikitlearn Functions.srt [19.2 KB]
99 - Introduction to ScikitLearn Jupyter Notebook with annotations.txt [130.0 B]
143 - Note Metric Comparison Improvement.html [2.2 KB]
114 - ScikitLearn machine learning map how to choose the right machine learning model【微信号 itcodeba 】.txt [72.0 B]
126 - Evaluating A Classification Model 1 Accuracy.srt [6.5 KB]
104 - Example ScikitLearn Workflow Notebook.txt [131.0 B]
113 - Getting Your Data Ready Handling Missing Values With Scikitlearn.srt [24.8 KB]
99 - Scikitlearn Introduction.srt [10.6 KB]
115 - NEW Choosing The Right Model For Your Data 2 Regression.srt [15.7 KB]
101 - Refresher What Is Machine Learning.srt [6.4 KB]
120 - Making Predictions With Our Model.srt [12.9 KB]
133 - NEW Evaluating A Regression Model 1 R2 Score.srt [13.9 KB]
103 - ScikitLearn Reference Notebook.txt [133.0 B]
100 - Quick Note Upcoming Video.html [390.0 B]
105 - Optional Debugging Warnings In Jupyter.srt [26.8 KB]
121 - predict vs predictproba.srt [12.3 KB]
132 - Evaluating A Classification Model 6 Classification Report.srt [14.9 KB]
140 - Tuning Hyperparameters.srt [32.8 KB]
144 - Quick Tip Correlation Analysis.srt [2.9 KB]
110 - Getting Your Data Ready Handling Missing Values With Pandas.srt [18.7 KB]
124 - NEW Evaluating A Machine Learning Model Score Part 2.srt [9.2 KB]
134 - NEW Evaluating A Regression Model 2 MAE.srt [9.3 KB]
135 - NEW Evaluating A Regression Model 3 MSE.srt [13.2 KB]
137 - NEW Evaluating A Model With Cross Validation and Scoring Parameter.srt [34.2 KB]
123 - NEW Evaluating A Machine Learning Model Score Part 1.srt [14.2 KB]
148 - Introduction to ScikitLearn Jupyter Notebook from the videos.txt [136.0 B]
102 - Quick Note Upcoming Videos.html [1018.0 B]
130 - Notebook from video with updated confusion matrix labels.txt [130.0 B]
99 - ScikitLearn Documentation.txt [47.0 B]
117 - Quick Tip How ML Algorithms Work.srt [1.9 KB]
148 - Introduction to ScikitLearn Jupyter Notebook with annotations.txt [130.0 B]
141 - Tuning Hyperparameters 2.srt [18.1 KB]
125 - Evaluating A Machine Learning Model 2 Cross Validation.srt [18.1 KB]
112 - Note Correction in the upcoming video splitting data.html [2.2 KB]
111 - Extension Feature Scaling.html [2.9 KB]
106 - scikit-learn-data.zip [20.8 KB]
99 - Introduction to ScikitLearn Jupyter Notebook from the upcoming videos.txt [136.0 B]
139 - Improving A Machine Learning Model.srt [14.3 KB]
116 - Quick Note Decision Trees.html [221.0 B]
108 - Getting Your Data Ready Convert Data To Numbers.srt [24.5 KB]
109 - Note Update to next video OneHotEncoder can handle NaNNone values.html [1.5 KB]
147 - Putting It All Together.srt [28.6 KB]
106 - Getting Your Data Ready Splitting Your Data.srt [12.2 KB]
130 - Evaluating A Classification Model 4 Confusion Matrix.srt [16.8 KB]
107 - Quick Tip Clean Transform Reduce.srt [6.5 KB]
129 - Reading Extension ROC Curve AUC.html [1.5 KB]
128 - Evaluating A Classification Model 3 ROC Curve.srt [10.6 KB]
119 - Fitting A Model To The Data.srt [9.6 KB]
127 - Evaluating A Classification Model 2 ROC Curve.srt [12.6 KB]
146 - Saving And Loading A Model 2.srt [9.7 KB]
104 - Typical scikitlearn Workflow.srt [34.3 KB]
118 - Choosing The Right Model For Your Data 3 Classification.srt [19.6 KB]
148 - Putting It All Together 2.srt [16.3 KB]
122 - NEW Making Predictions With Our Model Regression.srt [12.2 KB]
114 - NEW Choosing The Right Model For Your Data.srt [29.9 KB]
131 - NEW Evaluating A Classification Model 5 Confusion Matrix.srt [20.3 KB]
103 - Scikitlearn Cheatsheet.srt [9.8 KB]
147 - Reading extension ScikitLearns Pipeline class explained.txt [85.0 B]
📁 15 - Storytelling Communication How To Present Your Work
15 - Storytelling Communication How To Present Your Work说明.png [493.5 KB]
256 - Weekend Project Principle.srt [9.1 KB]
254 - Communicating With Managers.srt [4.1 KB]
259 - Communicating and sharing your work Further reading.html [3.1 KB]
258 - Storytelling.srt [4.1 KB]
257 - Devblog by Hashnode an easy and free way to create a blog you own.txt [28.0 B]
253 - How to Think About Communicating and Sharing Your Work blog post.txt [81.0 B]
255 - Communicating With CoWorkers.srt [5.1 KB]
253 - Communicating Your Work.srt [4.8 KB]
252 - Section Overview.srt [3.1 KB]
257 - Communicating With Outside World.srt [4.4 KB]
257 - fasttemplate by fastai a template you can use for your blog on GitHub Pages.txt [45.0 B]
📁 19 - Extra Learn Advanced Statistics and Mathematics for FREE
19 - Extra Learn Advanced Statistics and Mathematics for FREE资料.png [493.5 KB]
374 - Statistics and Mathematics.html [710.0 B]
📁 21 - BONUS SECTION
21 - BONUS SECTION必看.png [493.5 KB]
378 - Special Bonus Lecture.html [1.2 KB]
📁 6 - Pandas Data Analysis
6 - Pandas Data Analysis必看.zip [1.8 MB]
54 - Jake VanderPlass Data Manipulation with Pandas.txt [85.0 B]
53 - Selecting and Viewing Data with Pandas Part 2.srt [18.9 KB]
58 - Google Colab.txt [34.0 B]
54 - car-sales-missing-data.csv [287.0 B]
48 - Introduction to Pandas Jupyter Notebook from the upcoming videos.txt [130.0 B]
48 - 10 minutes to pandas from the documentation.txt [70.0 B]
50 - Data from URLs.html [1.1 KB]
49 - pandas-anatomy-of-a-dataframe.png [333.2 KB]
47 - Downloading Workbooks and Assignments.html [967.0 B]
56 - Introduction to Pandas Jupyter Notebook with annotations.txt [124.0 B]
51 - Describing Data with Pandas.srt [13.7 KB]
58 - Course notebooks Github.txt [47.0 B]
55 - pandas-anatomy-of-a-dataframe.png [333.2 KB]
46 - Section Overview.srt [3.5 KB]
48 - Introduction to Pandas Jupyter Notebook with annotations.txt [124.0 B]
56 - Introduction to Pandas Jupyter Notebook from the videos.txt [130.0 B]
54 - Manipulating Data.srt [19.5 KB]
52 - Selecting and Viewing Data with Pandas.srt [16.3 KB]
49 - car-sales.csv [369.0 B]
58 - How To Download The Course Assignments.srt [11.1 KB]
56 - Manipulating Data 3.srt [14.3 KB]
49 - Series Data Frames and CSVs.srt [18.3 KB]
55 - Manipulating Data 2.srt [16.2 KB]
52 - car-sales.csv [369.0 B]
57 - Assignment Pandas Practice.html [2.1 KB]
48 - Pandas Introduction.srt [6.6 KB]
48 - Pandas Documentation.txt [45.0 B]
📁 8 - Matplotlib Plotting and Data Visualization
8 - Matplotlib Plotting and Data Visualization文档.zip [1.8 MB]
96 - Saving And Sharing Your Plots.srt [6.1 KB]
81 - matplotlib-anatomy-of-a-plot.png [369.4 KB]
85 - Quick Tip Data Visualizations.srt [2.3 KB]
90 - Plotting from Pandas DataFrames 4.srt [10.6 KB]
82 - Scatter Plot And Bar Plot.srt [16.4 KB]
91 - Plotting from Pandas DataFrames 5.srt [11.9 KB]
90 - heart-disease.csv [11.1 KB]
80 - Importing And Using Matplotlib.srt [17.9 KB]
79 - Matplotlib Documentation.txt [42.0 B]
88 - Plotting From Pandas DataFrames 2.srt [14.8 KB]
86 - Plotting From Pandas DataFrames.srt [9.8 KB]
78 - Section Overview.srt [2.5 KB]
93 - Plotting from Pandas DataFrames 7.srt [16.8 KB]
89 - Plotting from Pandas DataFrames 3.srt [12.1 KB]
87 - Quick Note Regular Expressions.html [632.0 B]
96 - Introduction to Matplotlib Notebook from the videos.txt [134.0 B]
92 - Plotting from Pandas DataFrames 6.srt [12.2 KB]
94 - Customizing Your Plots.srt [15.0 KB]
79 - Introduction to Matplotlib Jupyter Notebook from the upcoming videos.txt [134.0 B]
84 - Subplots Option 2.srt [7.0 KB]
79 - Matplotlib Introduction.srt [7.8 KB]
97 - Assignment Matplotlib Practice.html [2.1 KB]
81 - matplotlib-anatomy-of-a-plot-with-code.png [654.8 KB]
81 - Anatomy Of A Matplotlib Figure.srt [15.4 KB]
95 - Customizing Your Plots 2.srt [14.7 KB]
83 - Histograms And Subplots.srt [14.5 KB]
📁 11 - Milestone Project 1 Supervised Learning Classification
11 - Milestone Project 1 Supervised Learning Classification说明.png [493.5 KB]
166 - Tuning Hyperparameters 2.srt [16.3 KB]
170 - Evaluating Our Model 2.srt [8.2 KB]
165 - Tuning Hyperparameters.srt [15.2 KB]
173 - Endtoend Heart Disease Classification Notebook with annotations.txt [140.0 B]
155 - Step 14 Framework Setup.srt [16.8 KB]
157 - heart-disease.csv [11.1 KB]
152 - Endtoend Heart Disease Classification Notebook same as in videos.txt [146.0 B]
153 - Project Environment Setup.srt [16.2 KB]
151 - Section Overview.srt [3.1 KB]
154 - Optional Windows Project Environment Setup.srt [5.2 KB]
168 - Quick Note Confusion Matrix Labels.html [1.1 KB]
160 - Finding Patterns 3.srt [20.2 KB]
172 - Finding The Most Important Features.srt [24.0 KB]
152 - Project Overview.srt [10.1 KB]
171 - Evaluating Our Model 3.srt [12.9 KB]
158 - Finding Patterns.srt [13.3 KB]
164 - TuningImproving Our Model.srt [18.3 KB]
173 - Endtoend Heart Disease Classification Notebook same as in videos.txt [146.0 B]
152 - Endtoend Heart Disease Classification Notebook with annotations.txt [140.0 B]
157 - Exploring Our Data.srt [11.7 KB]
156 - Getting Our Tools Ready.srt [12.5 KB]
167 - Tuning Hyperparameters 3.srt [9.6 KB]
163 - Experimenting With Machine Learning Models.srt [9.7 KB]
162 - Choosing The Right Models.srt [13.9 KB]
169 - Evaluating Our Model.srt [16.5 KB]
159 - Finding Patterns 2.srt [22.7 KB]
161 - Preparing Our Data For Machine Learning.srt [13.1 KB]
152 - Structured Data Projects on GitHub.txt [94.0 B]
173 - Reviewing The Project.srt [14.7 KB]
📁 12 - Milestone Project 2 Supervised Learning Time Series Data
12 - Milestone Project 2 Supervised Learning Time Series Data文档.png [493.5 KB]
175 - Endtoend Bluebook Bulldozer Regression Notebook with annotations.txt [147.0 B]
175 - Kaggle Bluebook for Bulldozers Competition.txt [57.0 B]
190 - RandomizedSearchCV.srt [13.0 KB]
194 - Endtoend Bluebook Bulldozer Regression Notebook with annotations.txt [147.0 B]
188 - Custom Evaluation Function.srt [16.3 KB]
174 - Section Overview.srt [1.9 KB]
182 - Turning Data Into Numbers.srt [24.6 KB]
186 - Splitting Data.srt [13.8 KB]
180 - Exploring Our Data 2.srt [8.7 KB]
187 - Challenge Whats wrong with splitting data after filling it.html [1.7 KB]
175 - Project Overview.srt [6.9 KB]
176 - Downloading the data for the next two projects.html [1.6 KB]
178 - Step 14 Framework Setup.srt [12.2 KB]
192 - Preproccessing Our Data.srt [18.9 KB]
181 - Feature Engineering.srt [24.2 KB]
179 - Exploring Our Data.srt [21.9 KB]
191 - Improving Hyperparameters.srt [11.7 KB]
183 - Filling Missing Numerical Values.srt [18.1 KB]
183 - Pandas Categorical Datatype Documentation.txt [82.0 B]
184 - Filling Missing Categorical Values.srt [11.8 KB]
193 - Making Predictions.srt [11.8 KB]
177 - Project Environment Setup.srt [16.5 KB]
189 - Reducing Data.srt [16.5 KB]
185 - Fitting A Machine Learning Model.srt [10.5 KB]
175 - Structured Data Projects on GitHub.txt [94.0 B]
194 - Feature Importance.srt [18.3 KB]
194 - Endtoend Bluebook Bulldozer Regression Notebook same as in videos.txt [153.0 B]
175 - Endtoend Bluebook Bulldozer Regression Notebook same as in videos.txt [153.0 B]
📁 1 - Introduction
1 - Introduction必看.png [493.5 KB]
3 - Exercise Meet Your Classmates and Instructor.html [3.7 KB]
2 - Join Our Online Classroom.srt [5.6 KB]
4 - Your First Day.srt [5.5 KB]
📁 2 - Machine Learning 101
2 - Machine Learning 101说明.png [493.5 KB]
7 - Exercise Machine Learning Playground.srt [8.2 KB]
5 - What Is Machine Learning.srt [8.4 KB]
11 - Are You Getting It Yet.html [160.0 B]
6 - AIMachine LearningData Science.srt [5.9 KB]
9 - Exercise YouTube Recommendation Engine.srt [5.6 KB]
14 - Monthly Coding Challenges Free Resources and Guides.html [1.6 KB]
12 - What Is Machine Learning Round 2.srt [5.4 KB]
13 - Section Review.srt [2.3 KB]
10 - Types of Machine Learning.srt [5.0 KB]
8 - How Did We Get Here.srt [7.1 KB]
9 - Machine Learning Playground.txt [27.0 B]
7 - Teachable Machine.txt [40.0 B]
📁 3 - Machine Learning and Data Science Framework
16 - Introducing Our Framework.srt [3.4 KB]
20 - Types of Evaluation.srt [4.1 KB]
26 - Overfitting and Underfitting Definitions.html [2.0 KB]
21 - Features In Data.srt [6.4 KB]
24 - Modelling Tuning.srt [4.8 KB]
29 - Optional Elements of AI.html [975.0 B]
22 - Modelling Splitting Data.srt [7.3 KB]
17 - A 6 Step Field Guide for Machine Learning Modelling blog post.txt [86.0 B]
25 - Modelling Comparison.srt [11.9 KB]
15 - Section Overview.srt [4.4 KB]
23 - Modelling Picking the Model.srt [5.9 KB]
27 - Experimentation.srt [5.2 KB]
18 - Types of Machine Learning Problems.srt [13.2 KB]
19 - Types of Data.srt [5.8 KB]
28 - Tools We Will Use.srt [5.7 KB]
📁 17 - Learn Python
320 - Tuples 2.srt [4.1 KB]
301 - Booleans.srt [4.3 KB]
279 - Python 2 vs Python 3.srt [7.9 KB]
292 - Exercise Repl.txt [55.0 B]
309 - Python Keywords.txt [56.0 B]
278 - Latest Version Of Python.srt [2.2 KB]
275 - pythonorg.txt [23.0 B]
320 - Tuple Methods.txt [53.0 B]
322 - Sets 2.srt [11.2 KB]
283 - How To Succeed.html [280.0 B]
316 - Dictionary Keys.srt [5.1 KB]
275 - Python Interpreter.srt [8.4 KB]
296 - Escape Sequences.srt [5.6 KB]
280 - Exercise How Does Python Work.srt [2.8 KB]
309 - List Methods 2.srt [5.9 KB]
309 - Exercise Repl.txt [33.0 B]
303 - Python Comments Best Practices.txt [45.0 B]
293 - Strings.srt [6.9 KB]
305 - Lists.srt [6.5 KB]
276 - Glotio.txt [16.0 B]
312 - List Unpacking.srt [3.8 KB]
282 - Python Data Types.srt [5.9 KB]
294 - String Concatenation.srt [1.8 KB]
288 - Exercise Operator Precedence.html [683.0 B]
289 - Base Numbers.txt [50.0 B]
315 - DEVELOPER FUNDAMENTALS III.srt [3.5 KB]
291 - Expressions vs Statements.srt [1.9 KB]
313 - None.srt [2.2 KB]
297 - Formatted Strings.srt [10.6 KB]
276 - How To Run Python Code.srt [6.4 KB]
274 - What Is A Programming Language.srt [7.4 KB]
311 - Common List Patterns.srt [7.2 KB]
290 - Variables.srt [16.2 KB]
287 - Exercise Repl.txt [45.0 B]
308 - List Methods.txt [52.0 B]
300 - BuiltIn Functions Methods.srt [11.9 KB]
311 - Exercise Repl.txt [33.0 B]
281 - Learning Python.srt [2.5 KB]
314 - Dictionaries.srt [8.8 KB]
310 - List Methods 3.srt [6.4 KB]
317 - Dictionary Methods.txt [58.0 B]
322 - Sets Methods.txt [51.0 B]
295 - Type Conversion.srt [3.8 KB]
299 - Immutability.srt [3.8 KB]
284 - Numbers.srt [12.9 KB]
279 - The Story of Python.txt [43.0 B]
288 - Exercise Repl.txt [45.0 B]
277 - Our First Python Program.srt [10.3 KB]
290 - Python Keywords.txt [56.0 B]
319 - Tuples.srt [6.4 KB]
279 - Python 2 vs Python 3 another one.txt [100.0 B]
289 - Optional bin and complex.srt [5.1 KB]
307 - Exercise Repl.txt [32.0 B]
304 - Exercise Password Checker.srt [8.7 KB]
307 - Matrix.srt [5.0 KB]
318 - Exercise Repl.txt [36.0 B]
308 - List Methods.srt [13.4 KB]
306 - List Slicing.srt [9.8 KB]
📁 5 - Data Science Environment Setup
42 - Sharing your Conda Environment.html [2.4 KB]
35 - What is Conda.srt [3.4 KB]
35 - Getting started with Conda documentation.txt [78.0 B]
35 - Conda documentation.txt [32.0 B]
43 - Jupyter Notebook Walkthrough.srt [15.4 KB]
42 - Conda documentation on sharing an environment.txt [111.0 B]
43 - Jupyter Notebook documentation.txt [50.0 B]
39 - Miniconda download documentation.txt [46.0 B]
39 - Windows Environment Setup.srt [7.4 KB]
40 - Windows Environment Setup 2.srt [33.4 KB]
37 - Miniconda download documentation.txt [46.0 B]
45 - Jupyter Notebook Walkthrough 3.srt [11.5 KB]
33 - Section Overview.srt [2.0 KB]
43 - Dataquest Jupyter Notebook for Beginners Tutorial.txt [56.0 B]
41 - Linux Environment Setup.html [1.0 KB]
37 - Mac Environment Setup.srt [24.1 KB]
36 - Conda Environments.srt [5.7 KB]
43 - 6-step-ml-framework.png [324.2 KB]
34 - Introducing Our Tools.srt [4.2 KB]
43 - heart-disease.csv [11.1 KB]
38 - Mac Environment Setup 2.srt [22.0 KB]
35 - conda-cheatsheet.pdf [201.3 KB]
44 - Jupyter Notebook Walkthrough 2.srt [23.3 KB]
35 - Getting your computer ready for machine learning How what and why you should use Anaconda Miniconda and Conda blog post.txt [106.0 B]
📁 13 - Data Engineering
196 - What Is Data.srt [7.5 KB]
197 - What Is A Data Engineer.srt [5.0 KB]
206 - Apache Spark and Apache Flink.srt [2.3 KB]
203 - Optional OLTP Databases.srt [11.6 KB]
198 - What Is A Data Engineer 2.srt [6.4 KB]
202 - Quick Note Upcoming Video.html [481.0 B]
200 - What Is A Data Engineer 4.srt [3.8 KB]
201 - A Primer on ACID Transactions.txt [56.0 B]
201 - Types Of Databases.srt [8.1 KB]
205 - Hadoop HDFS and MapReduce.srt [5.1 KB]
195 - Data Engineering Introduction.srt [4.2 KB]
204 - Optional Learn SQL.html [410.0 B]
196 - Kaggle.txt [31.0 B]
207 - Kafka and Stream Processing.srt [5.0 KB]
199 - What Is A Data Engineer 3.srt [5.1 KB]
201 - OLTP vs OLAP.txt [65.0 B]
📁 7 - NumPy
72 - Comparison Operators.srt [5.8 KB]
69 - Reshape and Transpose.srt [10.5 KB]
65 - Viewing Arrays and Matrices.srt [14.4 KB]
74 - Turn Images Into NumPy Arrays.srt [11.7 KB]
74 - Introduction to NumPy Jupyter Notebook from the videos.txt [129.0 B]
60 - Introduction to NumPy Jupyter Notebook with annotations.txt [123.0 B]
64 - NumPy Random Seed.srt [10.8 KB]
66 - Standard deviation and variance explained.txt [55.0 B]
71 - Exercise Nut Butter Store Sales.srt [18.9 KB]
74 - Introduction to NumPy Jupyter Notebook with annotations.txt [123.0 B]
73 - Sorting Arrays.srt [10.4 KB]
68 - Standard deviation and variance explained.txt [55.0 B]
60 - Introduction to NumPy Jupyter Notebook from the upcoming videos.txt [129.0 B]
63 - Creating NumPy Arrays.srt [13.9 KB]
62 - NumPy DataTypes and Attributes.srt [20.3 KB]
67 - Standard deviation and variance explained.txt [55.0 B]
60 - NumPy Introduction.srt [7.3 KB]
66 - Manipulating Arrays.srt [17.3 KB]
59 - Section Overview.srt [2.9 KB]
60 - NumPy Documentation.txt [22.0 B]
75 - Exercise Imposter Syndrome.srt [4.2 KB]
77 - Optional Extra NumPy resources.html [1.0 KB]
67 - Manipulating Arrays 2.srt [12.0 KB]
70 - Dot Product vs Element Wise.srt [16.5 KB]
68 - Standard Deviation and Variance.srt [9.7 KB]
61 - Quick Note Correction In Next Video.html [1.2 KB]
76 - Assignment NumPy Practice.html [2.2 KB]
70 - Matrix Multiplication Explained.txt [58.0 B]
74 - numpy-images.zip [7.3 MB]
📁 14 - Neural Networks Deep Learning Transfer Learning and TensorFlow 2
232 - Building A Deep Learning Model.srt [17.3 KB]
249 - Submitting Model to Kaggle.srt [19.4 KB]
233 - Building A Deep Learning Model 2.srt [13.6 KB]
225 - Documentation for loading images in TensorFlow.txt [53.0 B]
226 - Preprocess Images 2.srt [14.7 KB]
233 - Keras in TensorFlow Overview Documentation.txt [47.0 B]
237 - Evaluating Our Model.srt [12.2 KB]
219 - Google Colab Example of GPU speed up versus CPU.txt [53.0 B]
237 - TensorBoard Callback Documentation.txt [73.0 B]
230 - Preparing Our Inputs and Outputs.srt [8.1 KB]
232 - PyTorch Hub PyTorch version of TensorFlow Hub.txt [24.0 B]
225 - TensorFlow guidelines for loading all kinds of data turning your data into Tensors.txt [37.0 B]
243 - Visualizing Model Predictions.srt [20.3 KB]
219 - Optional GPU and Google Colab.srt [6.6 KB]
213 - Kaggle Dog Breed Identification Competition Data.txt [54.0 B]
218 - Google Colab example GPU usage.txt [53.0 B]
238 - Early Stopping Callback a way to stop your model from training when it stops .txt [75.0 B]
223 - Turning Data Labels Into Numbers.srt [15.5 KB]
242 - Transform Predictions To Text.srt [20.4 KB]
238 - Preventing Overfitting.srt [5.8 KB]
232 - MobileNetV2 the model were using on TensorFlow Hub.txt [71.0 B]
209 - Deep Learning and Unstructured Data.srt [20.2 KB]
228 - Turning Data Into Batches 2.srt [22.8 KB]
244 - Visualizing And Evaluate Model Predictions 2.srt [21.0 KB]
251 - Finishing Dog Vision Where to next.html [3.9 KB]
229 - Visualizing Our Data.srt [16.2 KB]
224 - Creating Our Own Validation Set.srt [11.4 KB]
240 - Evaluating Performance With TensorBoard.srt [10.9 KB]
236 - Summarizing Our Model.srt [5.7 KB]
248 - Making Predictions On Test Images.srt [22.2 KB]
247 - Training Model On Full Dataset.srt [21.6 KB]
211 - Kaggle Dog Breed Identification Competition the basis of our upcoming project.txt [58.0 B]
250 - Endtoend Dog Vision Notebook from the videos.txt [130.0 B]
222 - Preparing The Images.srt [16.4 KB]
241 - Make And Transform Predictions.srt [21.4 KB]
214 - Setting Up Our Data.srt [6.9 KB]
213 - Google Colab IO example how to get data in and out of your Colab notebook.txt [52.0 B]
248 - Dog Vision Prediction Probabilities Array.txt [109.0 B]
249 - Dog Vision Predictions with MobileNetV2 Ready for Kaggle Submission.txt [119.0 B]
212 - Google Colab FAQ things you should know about Google Colab.txt [49.0 B]
218 - Using A GPU.srt [13.6 KB]
250 - Making Predictions On Our Images.srt [22.1 KB]
211 - Google Colab IO example how to get data in and out of your Colab notebook.txt [52.0 B]
220 - Optional Reloading Colab Notebook.srt [9.2 KB]
232 - TensorFlow Hub resource for pretrained deep learning models and more.txt [18.0 B]
219 - Introduction to Google Colab example notebook.txt [55.0 B]
225 - Preprocess Images.srt [14.2 KB]
245 - Visualizing And Evaluate Model Predictions 3.srt [15.9 KB]
234 - Building A Deep Learning Model 3.srt [12.2 KB]
242 - TensorFlow documentation for the unbatch function.txt [66.0 B]
211 - Google Colab our workspace for the upcoming project.txt [34.0 B]
216 - Importing TensorFlow 2.srt [19.2 KB]
211 - Setting Up Google Colab.srt [10.5 KB]
215 - Setting Up Our Data 2.srt [2.2 KB]
239 - Training Your Deep Neural Network.srt [25.8 KB]
210 - Setting Up With Google.html [568.0 B]
228 - Yann LeCuns OG of deep learning Tweet on Batch Sizes.txt [57.0 B]
211 - Introduction to Google Colab example notebook.txt [55.0 B]
208 - Section Overview.srt [2.7 KB]
232 - Andrei Karpathys talk on AI at Tesla.txt [34.0 B]
246 - Saving And Loading A Trained Model.srt [19.5 KB]
221 - Loading Our Data Labels.srt [17.7 KB]
224 - Blog post by Rachel Thomas of fastai on how and why you should create a validation set.txt [47.0 B]
📁 16 - Career Advice Extra Bits
266 - JTS Start With Why.srt [3.0 KB]
265 - JTS Learn to Learn.srt [2.3 KB]
262 - What If I Dont Have Enough Experience.srt [19.2 KB]
261 - Quick Note Upcoming Video.html [587.0 B]
260 - Endorsements On LinkedIn.html [1.4 KB]
269 - CWD Git Github 2.srt [21.5 KB]
273 - Coding Challenges.html [948.0 B]
268 - CWD Git Github.srt [21.7 KB]
271 - Contributing To Open Source 2.srt [11.5 KB]
263 - Learning Guideline.html [336.0 B]
267 - Quick Note Upcoming Videos.html [352.0 B]
272 - Exercise Contribute To Open Source.html [1.9 KB]
270 - Contributing To Open Source.srt [17.6 KB]
264 - Quick Note Upcoming Videos.html [565.0 B]
📁 18 - Learn Python Part 2
365 - Solution Repl.txt [41.0 B]
337 - While Loops.srt [8.4 KB]
332 - For Loops.srt [8.9 KB]
345 - Default Parameters and Keyword Arguments.srt [6.3 KB]
338 - While Loops 2.srt [7.3 KB]
343 - Functions.srt [10.0 KB]
370 - Packages in Python.srt [13.9 KB]
358 - Pure Functions.srt [11.0 KB]
371 - Different Ways To Import.srt [8.3 KB]
334 - Exercise Tricky Counter.srt [3.9 KB]
334 - Solution Repl.txt [31.0 B]
330 - Exercise Logical Operators.srt [9.6 KB]
356 - Solution Repl.txt [34.0 B]
363 - List Comprehensions.srt [10.6 KB]
342 - Exercise Find Duplicates.srt [4.8 KB]
350 - Clean Code.srt [6.0 KB]
336 - enumerate.srt [5.0 KB]
351 - args and kwargs.srt [9.0 KB]
354 - Scope Rules.srt [9.0 KB]
367 - Modules in Python.srt [13.7 KB]
349 - Docstrings.srt [4.4 KB]
329 - Logical Operators.srt [10.1 KB]
340 - Our First GUI.srt [11.5 KB]
373 - Bonus Resource Python Cheatsheet.html [489.0 B]
324 - Conditional Logic.srt [18.5 KB]
325 - Indentation In Python.srt [5.5 KB]
366 - Python Exam Testing Your Understanding.html [1.1 KB]
340 - Exercise Repl.txt [38.0 B]
369 - Optional PyCharm.srt [11.4 KB]
355 - global Keyword.srt [7.3 KB]
353 - Scope.srt [4.7 KB]
323 - Breaking The Flow.srt [2.8 KB]
364 - Set Comprehensions.srt [7.6 KB]
346 - return.srt [16.2 KB]
359 - map.srt [7.2 KB]
327 - Ternary Operator.srt [5.4 KB]
365 - Exercise Comprehensions.srt [5.7 KB]
328 - Short Circuiting.srt [4.8 KB]
361 - zip.srt [4.2 KB]
344 - Parameters and Arguments.srt [5.5 KB]
339 - break continue pass.srt [5.6 KB]
348 - Methods vs Functions.srt [6.0 KB]
326 - Truthy vs Falsey Stackoverflow.txt [109.0 B]
326 - Truthy vs Falsey.srt [6.3 KB]
372 - Next Steps.html [959.0 B]
360 - filter.srt [5.9 KB]
356 - nonlocal Keyword.srt [4.0 KB]
333 - Iterables.srt [8.1 KB]
347 - Exercise Tesla.html [402.0 B]
340 - Solution Repl.txt [38.0 B]
368 - Quick Note Upcoming Videos.html [448.0 B]
335 - range.srt [6.8 KB]
352 - Exercise Functions.srt [5.4 KB]
341 - DEVELOPER FUNDAMENTALS IV.srt [7.9 KB]
331 - is vs.srt [10.0 KB]
362 - reduce.srt [8.5 KB]
📁 10 - Supervised Learning Classification Regression
150 - Milestone Projects.html [738.0 B]
📁 1 - Introduction
1 - Course Outline.srt [8.3 KB]
📁 20 - Where To Go From Here
376 - Thank You.srt [3.9 KB]
377 - Thank You Part 2.html [730.0 B]
375 - Become An Alumni.html [944.0 B]
📁 4 - The 2 Paths
30 - The 2 Paths.srt [4.4 KB]
31 - Python Machine Learning Monthly.html [917.0 B]
32 - Endorsements On LinkedIN.html [1.4 KB]

适合人群

• 数据科学初学者
• 机器学习爱好者
• 希望提升模型技能的从业者

🎯 学习收获

• 掌握Scikit-Learn库的使用
• 学会创建与评估机器学习模型
• 提升数据科学实战能力

🎉 祝您学习愉快！