Course Content
Subtitle Guide – Hướng dẫn thêm phụ đề
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01 – Introduction
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001 Course Outline
05:59
002 Join Our Online Classroom!
04:01
005 Your First Day
03:48
02 – Machine Learning 101
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001 What Is Machine Learning
06:52
002 AIMachine LearningData Science
04:51
003 ZTM Resources
04:23
004 Exercise Machine Learning Playground
06:16
005 How Did We Get Here
06:03
006 Exercise YouTube Recommendation Engine
04:24
007 Types of Machine Learning
04:41
009 What Is Machine Learning Round 2
04:45
010 Section Review
01:48
03 – Machine Learning and Data Science Framework
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001 Section Overview
03:08
002 Introducing Our Framework
02:38
003 6 Step Machine Learning Framework
04:58
004 Types of Machine Learning Problems
10:32
005 Types of Data
04:51
006 Types of Evaluation
03:31
007 Features In Data
05:22
008 Modelling – Splitting Data
05:58
009 Modelling – Picking the Model
04:35
010 Modelling – Tuning
03:17
011 Modelling – Comparison
09:32
013 Experimentation
03:35
014 Tools We Will Use
04:00
04 – The 2 Paths
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001 The 2 Paths
03:27
05 – Data Science Environment Setup
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001 Section Overview
01:09
002 Introducing Our Tools
03:28
003 What is Conda
02:35
004 Conda Environments
04:30
005 Mac Environment Setup_part1
08:43
005 Mac Environment Setup_part2
08:43
006 Mac Environment Setup 2_part1
07:05
006 Mac Environment Setup 2_part2
07:05
007 Windows Environment Setup
05:17
008 Windows Environment Setup 2_part1
07:45
008 Windows Environment Setup 2_part2
07:45
008 Windows Environment Setup 2_part3
07:45
011 Jupyter Notebook Walkthrough
10:20
012 Jupyter Notebook Walkthrough 2_part1
08:09
012 Jupyter Notebook Walkthrough 2_part2
08:08
013 Jupyter Notebook Walkthrough 3
08:10
06 – Pandas Data Analysis
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001 Section Overview
02:27
003 Pandas Introduction
04:29
004 Series, Data Frames and CSVs
13:21
007 Describing Data with Pandas
09:48
008 Selecting and Viewing Data with Pandas
11:08
010 Selecting and Viewing Data with Pandas Part 2_part1
06:33
010 Selecting and Viewing Data with Pandas Part 2_part2
06:33
011 Manipulating Data_part1
06:58
011 Manipulating Data_part2
06:58
012 Manipulating Data 2
09:57
013 Manipulating Data 3
10:12
015 How To Download The Course Assignments
07:43
07 – NumPy
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001 Section Overview
02:40
002 NumPy Introduction
05:17
004 NumPy DataTypes and Attributes
14:05
005 Creating NumPy Arrays
09:22
006 NumPy Random Seed
07:17
007 Viewing Arrays and Matrices
09:35
008 Manipulating Arrays
11:32
009 Manipulating Arrays 2
09:44
010 Standard Deviation and Variance
07:10
011 Reshape and Transpose
07:26
012 Dot Product vs Element Wise
11:45
013 Exercise Nut Butter Store Sales
13:04
014 Comparison Operators
03:33
015 Sorting Arrays
06:19
016 Turn Images Into NumPy Arrays
07:37
017 Exercise Imposter Syndrome
02:56
08 – Matplotlib Plotting and Data Visualization
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001 Section Overview
01:50
002 Matplotlib Introduction
05:16
003 Importing And Using Matplotlib
11:36
004 Anatomy Of A Matplotlib Figure
09:19
005 Scatter Plot And Bar Plot
10:09
006 Histograms And Subplots
08:40
007 Subplots Option 2
04:15
008 Quick Tip Data Visualizations
01:48
009 Plotting From Pandas DataFrames
05:58
011 Plotting From Pandas DataFrames 2_part1
05:16
011 Plotting From Pandas DataFrames 2_part2
05:16
012 Plotting from Pandas DataFrames 3
08:32
013 Plotting from Pandas DataFrames 4
06:36
014 Plotting from Pandas DataFrames 5
08:29
015 Plotting from Pandas DataFrames 6
08:28
016 Plotting from Pandas DataFrames 7_part1
05:40
016 Plotting from Pandas DataFrames 7_part2
05:40
017 Customizing Your Plots
10:09
018 Customizing Your Plots 2_part1
04:50
018 Customizing Your Plots 2_part2
04:50
019 Saving And Sharing Your Plots
04:14
09 – Scikit-learn Creating Machine Learning Models
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001 Section Overview
02:29
002 Scikit-learn Introduction
06:41
004 Refresher What Is Machine Learning
05:40
006 Scikit-learn Cheatsheet
06:12
007 Typical scikit-learn Workflow_part1
11:37
007 Typical scikit-learn Workflow_part2_part1
05:48
007 Typical scikit-learn Workflow_part2_part2
05:48
008 Optional Debugging Warnings In Jupyter_part1
09:28
008 Optional Debugging Warnings In Jupyter_part2
09:28
009 Getting Your Data Ready Splitting Your Data
08:37
010 Quick Tip Clean, Transform, Reduce
05:03
011 Getting Your Data Ready Convert Data To Numbers_part1
08:27
011 Getting Your Data Ready Convert Data To Numbers_part2
08:27
013 Getting Your Data Ready Handling Missing Values With Pandas_part1
06:11
013 Getting Your Data Ready Handling Missing Values With Pandas_part2
06:11
016 Getting Your Data Ready Handling Missing Values With Scikit-learn_part1
08:44
016 Getting Your Data Ready Handling Missing Values With Scikit-learn_part2
08:44
017 NEW Choosing The Right Model For Your Data_part1
06:44
017 NEW Choosing The Right Model For Your Data_part2
06:44
017 NEW Choosing The Right Model For Your Data_part3
06:44
018 NEW Choosing The Right Model For Your Data 2 (Regression)_part1
05:40
018 NEW Choosing The Right Model For Your Data 2 (Regression)_part2
05:40
020 Quick Tip How ML Algorithms Work
01:25
021 Choosing The Right Model For Your Data 3 (Classification)_part1
06:22
021 Choosing The Right Model For Your Data 3 (Classification)_part2
06:22
022 Fitting A Model To The Data
06:45
023 Making Predictions With Our Model
08:24
024 predict() vs predict_proba()
08:33
025 NEW Making Predictions With Our Model (Regression)
08:48
026 NEW Evaluating A Machine Learning Model (Score) Part 1
09:41
027 NEW Evaluating A Machine Learning Model (Score) Part 2
06:47
028 Evaluating A Machine Learning Model 2 (Cross Validation)
13:16
029 Evaluating A Classification Model 1 (Accuracy)
04:46
030 Evaluating A Classification Model 2 (ROC Curve)
09:04
031 Evaluating A Classification Model 3 (ROC Curve)
07:44
033 Evaluating A Classification Model 4 (Confusion Matrix)
11:01
034 NEW Evaluating A Classification Model 5 (Confusion Matrix)_part1
07:11
034 NEW Evaluating A Classification Model 5 (Confusion Matrix)_part2
07:11
035 Evaluating A Classification Model 6 (Classification Report)
10:16
036 NEW Evaluating A Regression Model 1 (R2 Score)_part1
04:59
036 NEW Evaluating A Regression Model 1 (R2 Score)_part2
04:59
037 NEW Evaluating A Regression Model 2 (MAE)
07:22
038 NEW Evaluating A Regression Model 3 (MSE)
09:49
040 NEW Evaluating A Model With Cross Validation and Scoring Parameter_part1
08:26
040 NEW Evaluating A Model With Cross Validation and Scoring Parameter_part2
08:26
040 NEW Evaluating A Model With Cross Validation and Scoring Parameter_part3
08:26
041 NEW Evaluating A Model With Scikit-learn Functions_part1
07:01
041 NEW Evaluating A Model With Scikit-learn Functions_part2
07:00
042 Improving A Machine Learning Model
11:16
043 Tuning Hyperparameters_part1
11:37
043 Tuning Hyperparameters_part2_part1
05:48
043 Tuning Hyperparameters_part2_part2
05:48
044 Tuning Hyperparameters 2
14:23
045 Tuning Hyperparameters 3_part1
07:29
045 Tuning Hyperparameters 3_part2
07:29
047 Quick Tip Correlation Analysis
02:28
048 Saving And Loading A Model
07:29
049 Saving And Loading A Model 2
06:20
050 Putting It All Together_part1
10:09
050 Putting It All Together_part2
10:09
051 Putting It All Together 2_part1
05:47
051 Putting It All Together 2_part2
05:47
10 – Supervised Learning Classification + Regression
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11 – Milestone Project 1 Supervised Learning (Classification)
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001 Section Overview
02:09
002 Project Overview
06:09
003 Project Environment Setup
10:59
004 Optional Windows Project Environment Setup
04:52
005 Step 1~4 Framework Setup_part1
06:03
005 Step 1~4 Framework Setup_part2
06:03
007 Getting Our Tools Ready
09:04
008 Exploring Our Data
08:33
009 Finding Patterns
10:02
010 Finding Patterns 2
16:47
011 Finding Patterns 3_part1
06:48
011 Finding Patterns 3_part2
06:48
012 Preparing Our Data For Machine Learning
08:51
013 Choosing The Right Models
10:15
014 Experimenting With Machine Learning Models
06:31
015 TuningImproving Our Model_part1
06:54
015 TuningImproving Our Model_part2
06:54
016 Tuning Hyperparameters_part1
05:43
016 Tuning Hyperparameters_part2
05:43
017 Tuning Hyperparameters 2_part1
05:54
017 Tuning Hyperparameters 2_part2
05:54
018 Tuning Hyperparameters 3
07:06
020 Evaluating Our Model
10:59
022 Evaluating Our Model 2
05:55
023 Evaluating Our Model 3
08:49
024 Finding The Most Important Features_part1
08:03
024 Finding The Most Important Features_part2
08:03
025 Reviewing The Project
09:13
12 – Milestone Project 2 Supervised Learning (Time Series Data)
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001 Section Overview
01:07
002 Project Overview
04:24
004 Project Environment Setup_part1
05:26
004 Project Environment Setup_part2
05:26
005 Step 1~4 Framework Setup
08:36
006 Exploring Our Data_part1
07:08
006 Exploring Our Data_part2
07:08
007 Exploring Our Data 2
06:16
008 Feature Engineering_part1
07:42
008 Feature Engineering_part2
07:42
009 Turning Data Into Numbers_part1
07:49
009 Turning Data Into Numbers_part2
07:49
010 Filling Missing Numerical Values_part1
06:24
010 Filling Missing Numerical Values_part2
06:24
011 Filling Missing Categorical Values
08:27
012 Fitting A Machine Learning Model
07:16
013 Splitting Data
10:00
015 Custom Evaluation Function_part1
05:36
015 Custom Evaluation Function_part2
05:36
016 Reducing Data
10:36
017 RandomizedSearchCV
09:32
018 Improving Hyperparameters
08:11
019 Preproccessing Our Data_part1
06:37
019 Preproccessing Our Data_part2
06:37
020 Making Predictions
09:17
021 Feature Importance_part1
06:55
021 Feature Importance_part2
06:55
13 – Data Engineering
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001 Data Engineering Introduction
03:24
002 What Is Data
06:42
003 What Is A Data Engineer
04:20
004 What Is A Data Engineer 2
05:35
005 What Is A Data Engineer 3
05:04
006 What Is A Data Engineer 4
03:22
007 Types Of Databases
06:50
009 Optional OLTP Databases
10:54
011 Hadoop, HDFS and MapReduce
04:22
012 Apache Spark and Apache Flink
02:07
013 Kafka and Stream Processing
04:33
14 – Neural Networks Deep Learning, Transfer Learning and TensorFlow 2
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001 Section Overview
02:06
002 Deep Learning and Unstructured Data
13:36
004 Setting Up Google Colab
07:17
005 Google Colab Workspace
04:23
006 Uploading Project Data
06:52
007 Setting Up Our Data
04:40
008 Setting Up Our Data 2
01:32
009 Importing TensorFlow 2_part1
06:21
009 Importing TensorFlow 2_part2
06:21
010 Optional TensorFlow 2.0 Default Issue
03:39
011 Using A GPU
08:59
012 Optional GPU and Google Colab
04:27
013 Optional Reloading Colab Notebook
06:49
014 Loading Our Data Labels_part1
06:02
014 Loading Our Data Labels_part2
06:02
015 Preparing The Images_part1
06:16
015 Preparing The Images_part2
06:16
016 Turning Data Labels Into Numbers_part1
06:05
016 Turning Data Labels Into Numbers_part2
06:05
017 Creating Our Own Validation Set
09:18
018 Preprocess Images
10:25
019 Preprocess Images 2_part1
05:30
019 Preprocess Images 2_part2
05:30
020 Turning Data Into Batches
09:37
021 Turning Data Into Batches 2_part1
08:57
021 Turning Data Into Batches 2_part2
08:57
022 Visualizing Our Data_part1
06:20
022 Visualizing Our Data_part2
06:20
023 Preparing Our Inputs and Outputs
06:38
025 Building A Deep Learning Model_part1
05:51
025 Building A Deep Learning Model_part2
05:51
026 Building A Deep Learning Model 2
10:53
027 Building A Deep Learning Model 3_part1
04:33
027 Building A Deep Learning Model 3_part2
04:32
028 Building A Deep Learning Model 4
09:12
029 Summarizing Our Model
04:52
030 Evaluating Our Model
09:26
031 Preventing Overfitting
04:20
032 Training Your Deep Neural Network_part1
09:34
032 Training Your Deep Neural Network_part2
09:34
033 Evaluating Performance With TensorBoard
07:30
034 Make And Transform Predictions_part1
07:32
034 Make And Transform Predictions_part2
07:32
035 Transform Predictions To Text_part1
07:40
035 Transform Predictions To Text_part2
07:39
036 Visualizing Model Predictions_part1
07:23
036 Visualizing Model Predictions_part2
07:23
037 Visualizing And Evaluate Model Predictions 2_part1
07:56
037 Visualizing And Evaluate Model Predictions 2_part2
07:56
038 Visualizing And Evaluate Model Predictions 3_part1
05:19
038 Visualizing And Evaluate Model Predictions 3_part2
05:19
039 Saving And Loading A Trained Model_part1
06:47
039 Saving And Loading A Trained Model_part2
06:47
040 Training Model On Full Dataset_part1
07:31
040 Training Model On Full Dataset_part2
07:31
041 Making Predictions On Test Images_part1
08:27
041 Making Predictions On Test Images_part2
08:27
042 Submitting Model to Kaggle_part1
07:07
042 Submitting Model to Kaggle_part2
07:07
043 Making Predictions On Our Images_part1
07:37
043 Making Predictions On Our Images_part2
07:37
15 – Storytelling + Communication How To Present Your Work
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001 Section Overview
02:19
002 Communicating Your Work
03:22
003 Communicating With Managers
02:58
004 Communicating With Co-Workers
03:42
005 Weekend Project Principle
06:32
006 Communicating With Outside World
03:29
007 Storytelling
03:06
16 – Career Advice + Extra Bits
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003 What If I Don’t Have Enough Experience_part1
07:31
003 What If I Don’t Have Enough Experience_part2
07:31
006 JTS Learn to Learn
01:59
007 JTS Start With Why
02:43
009 CWD Git + Github_part1_part1
04:25
009 CWD Git + Github_part1_part2
04:24
009 CWD Git + Github_part2
08:50
010 CWD Git + Github 2_part1
08:26
010 CWD Git + Github 2_part2
08:26
011 Contributing To Open Source_part1
07:04
011 Contributing To Open Source_part2
07:04
012 Contributing To Open Source 2_part1
04:50
012 Contributing To Open Source 2_part2
04:50
17 – Learn Python
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001 What Is A Programming Language
06:24
002 Python Interpreter
07:04
003 How To Run Python Code
04:53
004 Latest Version Of Python
01:28
005 Our First Python Program
07:43
006 Python 2 vs Python 3
06:41
007 Exercise How Does Python Work
02:09
008 Learning Python
02:05
009 Python Data Types
04:46
011 Numbers
11:09
012 Math Functions
04:29
013 DEVELOPER FUNDAMENTALS I
04:07
014 Operator Precedence
03:10
016 Optional bin() and complex
04:02
017 Variables
13:12
018 Expressions vs Statements
01:36
019 Augmented Assignment Operator
02:49
020 Strings
05:29
021 String Concatenation
01:16
022 Type Conversion
03:03
023 Escape Sequences
04:23
024 Formatted Strings
08:24
025 String Indexes
08:57
026 Immutability
03:13
027 Built-In Functions + Methods
10:03
028 Booleans
03:21
029 Exercise Type Conversion
08:22
030 DEVELOPER FUNDAMENTALS II
04:42
031 Exercise Password Checker
07:21
032 Lists
05:01
033 List Slicing
07:48
034 Matrix
04:11
035 List Methods
10:28
036 List Methods 2
04:24
037 List Methods 3
04:52
038 Common List Patterns
05:57
039 List Unpacking
02:41
040 None
01:51
041 Dictionaries
06:21
042 DEVELOPER FUNDAMENTALS III
02:40
043 Dictionary Keys
03:37
044 Dictionary Methods
04:37
045 Dictionary Methods 2
07:04
046 Tuples
04:46
047 Tuples 2
03:14
048 Sets
07:24
049 Sets 2
08:45
18 – Learn Python Part 2
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001 Breaking The Flow
02:34
002 Conditional Logic
13:18
003 Indentation In Python
04:38
004 Truthy vs Falsey
05:18
005 Ternary Operator
04:14
006 Short Circuiting
04:02
007 Logical Operators
06:56
008 Exercise Logical Operators
07:47
009 is vs ==
07:36
010 For Loops
07:01
011 Iterables
06:43
012 Exercise Tricky Counter
03:23
013 range()
05:38
014 enumerate()
04:37
015 While Loops
06:28
016 While Loops 2
05:49
017 break, continue, pass
04:15
018 Our First GUI
08:48
019 DEVELOPER FUNDAMENTALS IV
06:34
020 Exercise Find Duplicates
03:54
021 Functions
07:41
022 Parameters and Arguments
04:25
023 Default Parameters and Keyword Arguments
05:40
024 return
13:11
026 Methods vs Functions
04:33
027 Docstrings
03:47
028 Clean Code
04:38
029 args and kwargs
07:56
030 Exercise Functions
04:18
031 Scope
03:38
032 Scope Rules
06:55
033 global Keyword
06:13
034 nonlocal Keyword
03:21
035 Why Do We Need Scope
03:38
036 Pure Functions
09:23
037 map()
06:30
038 filter()
04:23
039 zip()
03:28
040 reduce()
07:32
041 List Comprehensions
08:37
042 Set Comprehensions
06:26
043 Exercise Comprehensions
04:36
045 Modules in Python
10:54
047 Optional PyCharm
08:19
048 Packages in Python
10:45
049 Different Ways To Import
07:03
19 – Extra Learn Advanced Statistics and Mathematics for FREE!
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20 – Where To Go From Here
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002 Thank You
02:44
21 – BONUS SECTION
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Complete A.I. & Machine Learning, Data Science Bootcamp
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