[Last updated 5/2024] Complete A.I. & Machine Learning, Data Science Bootcamp (Udemy – Vietsub and Engsub)

About Course

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Learn
Data Science
, Data Analysis,
Machine Learning
(Artificial Intelligence) and
Python
with Tensorflow, Pandas and more!
What you’ll learn:
Become a Data Scientist and get hired
Master Machine Learning and use it on the job
Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0
Use modern tools that big tech companies like Google, Apple, Amazon and Meta use
Present Data Science projects to management and stakeholders
Learn which Machine Learning model to choose for each type of problem
Real life case studies and projects to understand how things are done in the real world
Learn best practices when it comes to Data Science Workflow
Implement Machine Learning algorithms
Learn how to program in Python using the latest Python 3
v.v…
Description:
Dive into the transformative world of
Artificial Intelligence (A.I.)
,
Machine Learning
, and
Data Science
with our comprehensive “
Complete A.I. & Machine Learning, Data Science Bootcamp
“. This bootcamp is meticulously designed to provide you with a robust foundation and in-depth knowledge in these cutting-edge fields, ranging from fundamental concepts to advanced techniques.

Link gốc:

https://www.udemy.com/course/complete-machine-learning-and-data-science-zero-to-mastery/

Time Course:
43.5 hours (384 Lectures + Documents)

Instructor
: Andrei Neagoie
Total Weight:
17.43 GB
** Note
:

Chú ý:

Course Content

11 – Milestone Project 1 Supervised Learning (Classification)

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

18 – Learn Python Part 2

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

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

15 – Storytelling + Communication How To Present Your Work

14 – Neural Networks Deep Learning, Transfer Learning and TensorFlow 2

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

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)

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

09 – Scikit-learn Creating Machine Learning Models

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

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

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

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

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

03 – Machine Learning and Data Science Framework

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

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

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About Course

Course Content

11 – Milestone Project 1 Supervised Learning (Classification)

013 Choosing The Right Models.mp4

025 Reviewing The Project.mp4

024 Finding The Most Important Features.mp4

023 Evaluating Our Model 3.mp4

022 Evaluating Our Model 2.mp4

020 Evaluating Our Model.mp4

018 Tuning Hyperparameters 3.mp4

017 Tuning Hyperparameters 2.mp4

016 Tuning Hyperparameters.mp4

015 TuningImproving Our Model.mp4

014 Experimenting With Machine Learning Models.mp4

001 Section Overview.mp4

012 Preparing Our Data For Machine Learning.mp4

011 Finding Patterns 3.mp4

010 Finding Patterns 2.mp4

009 Finding Patterns.mp4

008 Exploring Our Data.mp4

007 Getting Our Tools Ready.mp4

005 Step 1~4 Framework Setup.mp4

004 Optional Windows Project Environment Setup.mp4

003 Project Environment Setup.mp4

002 Project Overview.mp4

20 – Where To Go From Here

002 Thank You.mp4

18 – Learn Python Part 2

037 map().mp4

026 Methods vs Functions.mp4

027 Docstrings.mp4

028 Clean Code.mp4

029 args and kwargs.mp4

030 Exercise Functions.mp4

031 Scope.mp4

032 Scope Rules.mp4

033 global Keyword.mp4

034 nonlocal Keyword.mp4

035 Why Do We Need Scope.mp4

036 Pure Functions.mp4

024 return.mp4

038 filter().mp4

039 zip().mp4

040 reduce().mp4

041 List Comprehensions.mp4

042 Set Comprehensions.mp4

043 Exercise Comprehensions.mp4

045 Modules in Python.mp4

047 Optional PyCharm.mp4

048 Packages in Python.mp4

049 Different Ways To Import.mp4

013 range().mp4

002 Conditional Logic.mp4

003 Indentation In Python.mp4

004 Truthy vs Falsey.mp4

005 Ternary Operator.mp4

006 Short Circuiting.mp4

007 Logical Operators.mp4

008 Exercise Logical Operators.mp4

009 is vs ==.mp4

010 For Loops.mp4

011 Iterables.mp4

012 Exercise Tricky Counter.mp4

001 Breaking The Flow.mp4

014 enumerate().mp4

015 While Loops.mp4

016 While Loops 2.mp4

017 break, continue, pass.mp4

018 Our First GUI.mp4

019 DEVELOPER FUNDAMENTALS IV.mp4

020 Exercise Find Duplicates.mp4

021 Functions.mp4

022 Parameters and Arguments.mp4

023 Default Parameters and Keyword Arguments.mp4

17 – Learn Python

038 Common List Patterns.mp4

027 Built-In Functions + Methods.mp4

028 Booleans.mp4

029 Exercise Type Conversion.mp4

030 DEVELOPER FUNDAMENTALS II.mp4