Course Content
11 – Milestone Project 1 Supervised Learning (Classification)
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013 Choosing The Right Models.mp4
10:15
025 Reviewing The Project.mp4
09:13
024 Finding The Most Important Features.mp4
16:07
023 Evaluating Our Model 3.mp4
08:49
022 Evaluating Our Model 2.mp4
05:54
020 Evaluating Our Model.mp4
10:59
018 Tuning Hyperparameters 3.mp4
07:06
017 Tuning Hyperparameters 2.mp4
11:49
016 Tuning Hyperparameters.mp4
11:27
015 TuningImproving Our Model.mp4
13:49
014 Experimenting With Machine Learning Models.mp4
06:31
001 Section Overview.mp4
02:09
012 Preparing Our Data For Machine Learning.mp4
08:51
011 Finding Patterns 3.mp4
13:36
010 Finding Patterns 2.mp4
16:47
009 Finding Patterns.mp4
10:02
008 Exploring Our Data.mp4
08:33
007 Getting Our Tools Ready.mp4
09:04
005 Step 1~4 Framework Setup.mp4
12:06
004 Optional Windows Project Environment Setup.mp4
04:52
003 Project Environment Setup.mp4
10:59
002 Project Overview.mp4
06:09
20 – Where To Go From Here
0/1
002 Thank You.mp4
02:44
18 – Learn Python Part 2
0/46
037 map().mp4
06:30
026 Methods vs Functions.mp4
04:33
027 Docstrings.mp4
03:47
028 Clean Code.mp4
04:38
029 args and kwargs.mp4
07:56
030 Exercise Functions.mp4
04:18
031 Scope.mp4
03:37
032 Scope Rules.mp4
06:55
033 global Keyword.mp4
06:13
034 nonlocal Keyword.mp4
03:20
035 Why Do We Need Scope.mp4
03:38
036 Pure Functions.mp4
09:23
024 return.mp4
13:11
038 filter().mp4
04:23
039 zip().mp4
03:28
040 reduce().mp4
07:31
041 List Comprehensions.mp4
08:37
042 Set Comprehensions.mp4
06:26
043 Exercise Comprehensions.mp4
04:36
045 Modules in Python.mp4
10:54
047 Optional PyCharm.mp4
08:19
048 Packages in Python.mp4
10:45
049 Different Ways To Import.mp4
07:03
013 range().mp4
05:38
002 Conditional Logic.mp4
13:17
003 Indentation In Python.mp4
04:38
004 Truthy vs Falsey.mp4
05:17
005 Ternary Operator.mp4
04:14
006 Short Circuiting.mp4
04:02
007 Logical Operators.mp4
06:56
008 Exercise Logical Operators.mp4
07:47
009 is vs ==.mp4
07:36
010 For Loops.mp4
07:01
011 Iterables.mp4
06:43
012 Exercise Tricky Counter.mp4
03:23
001 Breaking The Flow.mp4
02:34
014 enumerate().mp4
04:37
015 While Loops.mp4
06:27
016 While Loops 2.mp4
05:49
017 break, continue, pass.mp4
04:15
018 Our First GUI.mp4
08:48
019 DEVELOPER FUNDAMENTALS IV.mp4
06:34
020 Exercise Find Duplicates.mp4
03:54
021 Functions.mp4
07:41
022 Parameters and Arguments.mp4
04:24
023 Default Parameters and Keyword Arguments.mp4
05:40
17 – Learn Python
0/47
038 Common List Patterns.mp4
05:57
027 Built-In Functions + Methods.mp4
10:03
028 Booleans.mp4
03:21
029 Exercise Type Conversion.mp4
08:22
030 DEVELOPER FUNDAMENTALS II.mp4
04:42
031 Exercise Password Checker.mp4
07:20
032 Lists.mp4
05:01
033 List Slicing.mp4
07:48
034 Matrix.mp4
04:11
035 List Methods.mp4
10:28
036 List Methods 2.mp4
04:24
037 List Methods 3.mp4
04:52
026 Immutability.mp4
03:13
039 List Unpacking.mp4
02:40
040 None.mp4
01:50
041 Dictionaries.mp4
06:20
042 DEVELOPER FUNDAMENTALS III.mp4
02:40
043 Dictionary Keys.mp4
03:36
044 Dictionary Methods.mp4
04:37
045 Dictionary Methods 2.mp4
07:04
046 Tuples.mp4
04:46
047 Tuples 2.mp4
03:14
048 Sets.mp4
07:24
049 Sets 2.mp4
08:45
014 Operator Precedence.mp4
03:10
002 Python Interpreter.mp4
07:04
003 How To Run Python Code.mp4
04:53
004 Latest Version Of Python.mp4
01:28
005 Our First Python Program.mp4
07:43
006 Python 2 vs Python 3.mp4
06:40
007 Exercise How Does Python Work.mp4
02:09
008 Learning Python.mp4
02:05
009 Python Data Types.mp4
04:45
011 Numbers.mp4
11:09
012 Math Functions.mp4
04:29
013 DEVELOPER FUNDAMENTALS I.mp4
04:07
001 What Is A Programming Language.mp4
06:24
016 Optional bin() and complex.mp4
04:02
017 Variables.mp4
13:12
018 Expressions vs Statements.mp4
01:36
019 Augmented Assignment Operator.mp4
02:49
020 Strings.mp4
05:29
021 String Concatenation.mp4
01:15
022 Type Conversion.mp4
03:03
023 Escape Sequences.mp4
04:23
024 Formatted Strings.mp4
08:23
025 String Indexes.mp4
08:56
16 – Career Advice + Extra Bits
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003 What If I Don’t Have Enough Experience.mp4
15:02
006 JTS Learn to Learn.mp4
01:59
007 JTS Start With Why.mp4
02:43
009 CWD Git + Github.mp4
17:39
010 CWD Git + Github 2.mp4
16:52
011 Contributing To Open Source.mp4
14:08
012 Contributing To Open Source 2.mp4
09:40
15 – Storytelling + Communication How To Present Your Work
0/7
001 Section Overview.mp4
02:19
002 Communicating Your Work.mp4
03:21
003 Communicating With Managers.mp4
02:58
004 Communicating With Co-Workers.mp4
03:42
005 Weekend Project Principle.mp4
06:32
006 Communicating With Outside World.mp4
03:28
007 Storytelling.mp4
03:05
14 – Neural Networks Deep Learning, Transfer Learning and TensorFlow 2
0/41
034 Make And Transform Predictions.mp4
15:04
023 Preparing Our Inputs and Outputs.mp4
06:37
025 Building A Deep Learning Model.mp4
11:42
026 Building A Deep Learning Model 2.mp4
10:53
027 Building A Deep Learning Model 3.mp4
09:05
028 Building A Deep Learning Model 4.mp4
09:12
029 Summarizing Our Model.mp4
04:51
030 Evaluating Our Model.mp4
09:26
031 Preventing Overfitting.mp4
04:19
032 Training Your Deep Neural Network.mp4
19:09
033 Evaluating Performance With TensorBoard.mp4
07:30
022 Visualizing Our Data.mp4
12:41
035 Transform Predictions To Text.mp4
15:19
036 Visualizing Model Predictions.mp4
14:45
037 Visualizing And Evaluate Model Predictions 2.mp4
15:52
038 Visualizing And Evaluate Model Predictions 3.mp4
10:39
039 Saving And Loading A Trained Model.mp4
13:33
040 Training Model On Full Dataset.mp4
15:01
041 Making Predictions On Test Images.mp4
16:54
042 Submitting Model to Kaggle.mp4
14:14
043 Making Predictions On Our Images.mp4
15:15
012 Optional GPU and Google Colab.mp4
04:27
002 Deep Learning and Unstructured Data.mp4
13:36
004 Setting Up Google Colab.mp4
07:17
005 Google Colab Workspace.mp4
04:23
006 Uploading Project Data.mp4
06:52
007 Setting Up Our Data.mp4
04:40
008 Setting Up Our Data 2.mp4
01:32
009 Importing TensorFlow 2.mp4
12:43
010 Optional TensorFlow 2.0 Default Issue.mp4
03:38
011 Using A GPU.mp4
08:59
001 Section Overview.mp4
02:06
013 Optional Reloading Colab Notebook.mp4
06:49
014 Loading Our Data Labels.mp4
12:04
015 Preparing The Images.mp4
12:32
016 Turning Data Labels Into Numbers.mp4
12:11
017 Creating Our Own Validation Set.mp4
09:18
018 Preprocess Images.mp4
10:25
019 Preprocess Images 2.mp4
10:59
020 Turning Data Into Batches.mp4
09:37
021 Turning Data Into Batches 2.mp4
17:54
13 – Data Engineering
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001 Data Engineering Introduction.mp4
03:23
002 What Is Data.mp4
06:42
003 What Is A Data Engineer.mp4
04:20
004 What Is A Data Engineer 2.mp4
05:35
005 What Is A Data Engineer 3.mp4
05:03
006 What Is A Data Engineer 4.mp4
03:22
007 Types Of Databases.mp4
06:50
009 Optional OLTP Databases.mp4
10:54
011 Hadoop, HDFS and MapReduce.mp4
04:22
012 Apache Spark and Apache Flink.mp4
02:07
013 Kafka and Stream Processing.mp4
04:33
12 – Milestone Project 2 Supervised Learning (Time Series Data)
0/19
011 Filling Missing Categorical Values.mp4
08:27
021 Feature Importance.mp4
13:50
020 Making Predictions.mp4
09:17
019 Preproccessing Our Data.mp4
13:15
018 Improving Hyperparameters.mp4
08:11
017 RandomizedSearchCV.mp4
09:32
016 Reducing Data.mp4
10:36
015 Custom Evaluation Function.mp4
11:13
013 Splitting Data.mp4
10:00
012 Fitting A Machine Learning Model.mp4
07:15
001 Section Overview.mp4
01:07
010 Filling Missing Numerical Values.mp4
12:49
009 Turning Data Into Numbers.mp4
15:38
008 Feature Engineering.mp4
15:24
007 Exploring Our Data 2.mp4
06:16
006 Exploring Our Data.mp4
14:16
005 Step 1~4 Framework Setup.mp4
08:36
004 Project Environment Setup.mp4
10:52
002 Project Overview.mp4
04:24
01 – Introduction
0/3
001 Course Outline.mp4
05:59
002 Join Our Online Classroom!.mp4
04:01
005 Your First Day.mp4
03:48
09 – Scikit-learn Creating Machine Learning Models
0/42
041 NEW Evaluating A Model With Scikit-learn Functions.mp4
14:01
029 Evaluating A Classification Model 1 (Accuracy).mp4
04:46
030 Evaluating A Classification Model 2 (ROC Curve).mp4
09:04
031 Evaluating A Classification Model 3 (ROC Curve).mp4
07:44
033 Evaluating A Classification Model 4 (Confusion Matrix).mp4
11:01
034 NEW Evaluating A Classification Model 5 (Confusion Matrix).mp4
14:22
035 Evaluating A Classification Model 6 (Classification Report).mp4
10:16
036 NEW Evaluating A Regression Model 1 (R2 Score).mp4
09:59
037 NEW Evaluating A Regression Model 2 (MAE).mp4
07:22
038 NEW Evaluating A Regression Model 3 (MSE).mp4
09:48
040 NEW Evaluating A Model With Cross Validation and Scoring Parameter.mp4
25:18
028 Evaluating A Machine Learning Model 2 (Cross Validation).mp4
13:15
042 Improving A Machine Learning Model.mp4
11:16
043 Tuning Hyperparameters.mp4
23:15
044 Tuning Hyperparameters 2.mp4
14:23
045 Tuning Hyperparameters 3.mp4
14:59
047 Quick Tip Correlation Analysis.mp4
02:28
048 Saving And Loading A Model.mp4
07:28
049 Saving And Loading A Model 2.mp4
06:20
050 Putting It All Together.mp4
20:19
051 Putting It All Together 2.mp4
11:34
017 NEW Choosing The Right Model For Your Data.mp4
20:14
002 Scikit-learn Introduction.mp4
06:40
004 Refresher What Is Machine Learning.mp4
05:40
006 Scikit-learn Cheatsheet.mp4
06:12
007 Typical scikit-learn Workflow.mp4
23:14
008 Optional Debugging Warnings In Jupyter.mp4
18:57
009 Getting Your Data Ready Splitting Your Data.mp4
08:36
010 Quick Tip Clean, Transform, Reduce.mp4
05:03
011 Getting Your Data Ready Convert Data To Numbers.mp4
16:54
013 Getting Your Data Ready Handling Missing Values With Pandas.mp4
12:22
016 Getting Your Data Ready Handling Missing Values With Scikit-learn.mp4
17:29
001 Section Overview.mp4
02:29
018 NEW Choosing The Right Model For Your Data 2 (Regression).mp4
11:21
020 Quick Tip How ML Algorithms Work.mp4
01:25
021 Choosing The Right Model For Your Data 3 (Classification).mp4
12:45
022 Fitting A Model To The Data.mp4
06:45
023 Making Predictions With Our Model.mp4
08:24
024 predict() vs predict_proba().mp4
08:32
025 NEW Making Predictions With Our Model (Regression).mp4
08:48
026 NEW Evaluating A Machine Learning Model (Score) Part 1.mp4
09:41
027 NEW Evaluating A Machine Learning Model (Score) Part 2.mp4
06:47
08 – Matplotlib Plotting and Data Visualization
0/18
011 Plotting From Pandas DataFrames 2.mp4
10:33
019 Saving And Sharing Your Plots.mp4
04:14
018 Customizing Your Plots 2.mp4
09:41
017 Customizing Your Plots.mp4
10:09
016 Plotting from Pandas DataFrames 7.mp4
11:20
015 Plotting from Pandas DataFrames 6.mp4
08:27
014 Plotting from Pandas DataFrames 5.mp4
08:28
013 Plotting from Pandas DataFrames 4.mp4
06:36
012 Plotting from Pandas DataFrames 3.mp4
08:32
001 Section Overview.mp4
01:50
009 Plotting From Pandas DataFrames.mp4
05:58
008 Quick Tip Data Visualizations.mp4
01:48
007 Subplots Option 2.mp4
04:15
006 Histograms And Subplots.mp4
08:40
005 Scatter Plot And Bar Plot.mp4
10:09
004 Anatomy Of A Matplotlib Figure.mp4
09:19
003 Importing And Using Matplotlib.mp4
11:36
002 Matplotlib Introduction.mp4
05:16
07 – NumPy
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001 Section Overview.mp4
02:40
002 NumPy Introduction.mp4
05:17
004 NumPy DataTypes and Attributes.mp4
14:05
005 Creating NumPy Arrays.mp4
09:22
006 NumPy Random Seed.mp4
07:17
007 Viewing Arrays and Matrices.mp4
09:35
008 Manipulating Arrays.mp4
11:31
009 Manipulating Arrays 2.mp4
09:44
010 Standard Deviation and Variance.mp4
07:10
011 Reshape and Transpose.mp4
07:26
012 Dot Product vs Element Wise.mp4
11:45
013 Exercise Nut Butter Store Sales.mp4
13:04
014 Comparison Operators.mp4
03:33
015 Sorting Arrays.mp4
06:19
016 Turn Images Into NumPy Arrays.mp4
07:37
017 Exercise Imposter Syndrome.mp4
02:55
06 – Pandas Data Analysis
0/10
001 Section Overview.mp4
02:27
003 Pandas Introduction.mp4
04:29
004 Series, Data Frames and CSVs.mp4
13:21
007 Describing Data with Pandas.mp4
09:48
008 Selecting and Viewing Data with Pandas.mp4
11:08
010 Selecting and Viewing Data with Pandas Part 2.mp4
13:06
011 Manipulating Data.mp4
13:56
012 Manipulating Data 2.mp4
09:56
013 Manipulating Data 3.mp4
10:12
015 How To Download The Course Assignments.mp4
07:43
05 – Data Science Environment Setup
0/11
001 Section Overview.mp4
01:09
002 Introducing Our Tools.mp4
03:28
003 What is Conda.mp4
02:34
004 Conda Environments.mp4
04:30
005 Mac Environment Setup.mp4
17:26
006 Mac Environment Setup 2.mp4
14:11
007 Windows Environment Setup.mp4
05:17
008 Windows Environment Setup 2.mp4
23:17
011 Jupyter Notebook Walkthrough.mp4
10:20
012 Jupyter Notebook Walkthrough 2.mp4
16:17
013 Jupyter Notebook Walkthrough 3.mp4
08:10
04 – The 2 Paths
0/1
001 The 2 Paths.mp4
03:27
03 – Machine Learning and Data Science Framework
0/13
001 Section Overview.mp4
03:08
002 Introducing Our Framework.mp4
02:38
003 6 Step Machine Learning Framework.mp4
04:58
004 Types of Machine Learning Problems.mp4
10:31
005 Types of Data.mp4
04:50
006 Types of Evaluation.mp4
03:31
007 Features In Data.mp4
05:22
008 Modelling – Splitting Data.mp4
05:58
009 Modelling – Picking the Model.mp4
04:34
010 Modelling – Tuning.mp4
03:17
011 Modelling – Comparison.mp4
09:32
013 Experimentation.mp4
03:35
014 Tools We Will Use.mp4
03:59
02 – Machine Learning 101
0/9
001 What Is Machine Learning.mp4
06:52
002 AIMachine LearningData Science.mp4
04:51
003 ZTM Resources.mp4
04:23
004 Exercise Machine Learning Playground.mp4
06:15
005 How Did We Get Here.mp4
06:03
006 Exercise YouTube Recommendation Engine.mp4
04:24
007 Types of Machine Learning.mp4
04:41
009 What Is Machine Learning Round 2.mp4
04:44
010 Section Review.mp4
01:48
[Last updated 5/2024] Complete A.I. & Machine Learning, Data Science Bootcamp (Udemy – Vietsub and Engsub)
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