Complete Machine Learning

Course content

Sections: 18

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Activities: 0

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Resources: 305

1. Course Outline
4. Your First Day

1. What Is Machine Learning
2. AIMachine LearningData Science
3. Exercise Machine Learning Playground
4. How Did We Get Here
5. Exercise YouTube Recommendation Engine
8. What Is Machine Learning Round 2
9. Section Review

1. Section Overview
2. Introducing Our Framework
3. 6 Step Machine Learning Framework
4. Types of Machine Learning Problems
5. Types of Data
6. Types of Evaluation
7. Features In Data
8. Modelling - Splitting Data
9. Modelling - Picking the Mode
10. Modelling - Tuning
11. Modelling - Comparison
13. Experimentation
14. Tools We Will Use

1. The 2 Paths

1. Section Overview
2. Introducing Our Tools
3. What is Conda
4. Conda Environments
5. Mac Environment Setup
6. Mac Environment Setup 2
7. Windows Environment Setup
8. Windows Environment Setup 2
11. Jupyter Notebook Walkthrough
12. Jupyter Notebook Walkthrough 2
13. Jupyter Notebook Walkthrough 3

1. Section Overview
4. Series, Data Frames and CSVs
3. Pandas Introduction
6. Describing Data with Pandas
7. Selecting and Viewing Data with Pandas
8. Selecting and Viewing Data with Pandas Part 2
9. Manipulating Data
10. Manipulating Data 2
11. Manipulating Data 3
13. How To Download The Course Assignments

1. Section Overview
2. NumPy Introduction
4. NumPy DataTypes and Attributes
5. Creating NumPy Arrays
6. NumPy Random Seed
7. Viewing Arrays and Matrices
8. Manipulating Arrays
9. Manipulating Arrays 2
10. Standard Deviation and Variance
11. Reshape and Transpose
12. Dot Product vs Element Wise
13. Exercise Nut Butter Store Sales
14. Comparison Operators
15. Sorting Arrays
16. Turn Images Into NumPy Arrays

1. Section Overview.
2. Matplotlib Introduction
3. Importing And Using Matplotlib
4. Anatomy Of A Matplotlib Figure
5. Scatter Plot And Bar Plot
6. Histograms And Subplots
7. Subplots Option 2
8. Quick Tip Data Visualizations
9. Plotting From Pandas DataFrames.
11. Plotting From Pandas DataFrames 2.
12. Plotting from Pandas DataFrames 3
13. Plotting from Pandas DataFrames 4
14. Plotting from Pandas DataFrames 5
15. Plotting from Pandas DataFrames 6
16. Plotting from Pandas DataFrames 7
17. Customizing Your Plots.
18. Customizing Your Plots 2
19. Saving And Sharing Your Plots.

1. Section Overview
2. Scikit-learn Introduction
4. Refresher What Is Machine Learning
6. Scikit-learn Cheatsheet
7. Typical scikit-learn Workflow
8. Optional Debugging Warnings In Jupyter
9. Getting Your Data Ready Splitting Your Data
10. Quick Tip Clean, Transform, Reduce
11. Getting Your Data Ready Convert Data To Numbers
12. Getting Your Data Ready Handling Missing Values With Pandas
15. Getting Your Data Ready Handling Missing Values With Scikit-learn
16. Choosing The Right Model For Your Data
17. Choosing The Right Model For Your Data 2 (Regression)
19. Quick Tip How ML Algorithms Work
20. Choosing The Right Model For Your Data 3 (Classification)
21. Fitting A Model To The Data
22. Making Predictions With Our Model
23. predict() vs predict_proba()
24. Making Predictions With Our Model (Regression)
25. Evaluating A Machine Learning Model (Score)
26. Evaluating A Machine Learning Model 2 (Cross Validation)
27. Evaluating A Classification Model 1 (Accuracy)
28. Evaluating A Classification Model 2 (ROC Curve)
29. Evaluating A Classification Model 3 (ROC Curve)
31. Evaluating A Classification Model 4 (Confusion Matrix)
32. Evaluating A Classification Model 5 (Confusion Matrix)
33. Evaluating A Classification Model 6 (Classification Report)
34. Evaluating A Regression Model 1 (R2 Score)
35. Evaluating A Regression Model 2 (MAE)
36. Evaluating A Regression Model 3 (MSE)
38. Evaluating A Model With Cross Validation and Scoring Parameter
39. Evaluating A Model With Scikit-learn Functions
40. Improving A Machine Learning Model
41. Tuning Hyperparameters
42. Tuning Hyperparameters 2
43. Tuning Hyperparameters 3
45. Quick Tip Correlation Analysis.
46. Saving And Loading A Model
47. Saving And Loading A Model 2
48. Putting It All Together
49. Putting It All Together 2

1. Section Overview
2. Project Overview
3. Project Environment Setup
4. Optional Windows Project Environment Setup
5. Step 1~4 Framework Setup
6. Getting Our Tools Ready
7. Exploring Our Data
8. Finding Patterns
9. Finding Patterns 2
10. Finding Patterns 3
11. Preparing Our Data For Machine Learning
12. Choosing The Right Models
13. Experimenting With Machine Learning Models
14. TuningImproving Our Model
15. Tuning Hyperparameters
16. Tuning Hyperparameters 2
17. Tuning Hyperparameters 3
18. Evaluating Our Model
19. Evaluating Our Model 2
20. Evaluating Our Model 3
21. Finding The Most Important Features
22. Reviewing The Project

1. Section Overview
10. Filling Missing Numerical Values
11. Filling Missing Categorical Values
12. Fitting A Machine Learning Model
13. Splitting Data
15. Custom Evaluation Function

1. Data Engineering Introduction
2. What Is Data
3. What Is A Data Engineer
4. What Is A Data Engineer 2
5. What Is A Data Engineer 3
6. What Is A Data Engineer 4
7. Types Of Databases
9. Optional OLTP Databases
11. Hadoop, HDFS and MapReduce
12. Apache Spark and Apache Flink
13. Kafka and Stream Processing

1. Section Overview
2. Deep Learning and Unstructured Data
4. Setting Up Google Colab
5. Google Colab Workspace
6. Uploading Project Data
7. Setting Up Our Data
8. Setting Up Our Data 2
9. Importing TensorFlow 2
10. Optional TensorFlow 2.0 Default Issue
11. Using A GPU
12. Optional GPU and Google Colab
13. Optional Reloading Colab Notebook
14. Loading Our Data Labels
15. Preparing The Images
16. Turning Data Labels Into Numbers
17. Creating Our Own Validation Set
18. Preprocess Images
19. Preprocess Images 2
20. Turning Data Into Batches
21. Turning Data Into Batches 2
22. Visualizing Our Data
23. Preparing Our Inputs and Outputs
25. Building A Deep Learning Model
26. Building A Deep Learning Model 2
27. Building A Deep Learning Model 3
28. Building A Deep Learning Model 4
29. Summarizing Our Model
30. Evaluating Our Model
31. Preventing Overfitting
32. Training Your Deep Neural Network
33. Evaluating Performance With TensorBoard
34. Make And Transform Predictions
35. Transform Predictions To Text
36. Visualizing Model Predictions
37. Visualizing And Evaluate Model Predictions 2
38. Visualizing And Evaluate Model Predictions 3
39. Saving And Loading A Trained Model
40. Training Model On Full Dataset
41. Making Predictions On Test Images
42. Submitting Model to Kaggle
43. Making Predictions On Our Images

1. Section Overview
2. Communicating Your Work
3. Communicating With Managers.
4. Communicating With Co-Workers
5. Weekend Project Principle
6. Communicating With Outside World
7. Storytelling

3. What If I Don't Have Enough Experience
6. JTS Learn to Learn
9. CWD Git + Github
7. JTS Start With Why
10. CWD Git + Github 2
11. Contributing To Open Source
12. Contributing To Open Source 2

1. What Is A Programming Language.
2. Python Interpreter
3. How To Run Python Code
4. Our First Python Program
5. Python 2 vs Python 3
6. Exercise How Does Python Work
7. Learning Python
8. Python Data Types
10. Numbers
11. Math Functions
12. DEVELOPER FUNDAMENTALS I
13. Operator Precedence
15. Optional bin() and complex
16. Variables
17. Expressions vs Statements
18. Augmented Assignment Operator
19. Strings
20. String Concatenation
21. Type Conversion
22. Escape Sequences
23. Formatted Strings
24. String Indexes
25. Immutability
26. Built-In Functions + Methods
27. Booleans
28. Exercise Type Conversion
29. DEVELOPER FUNDAMENTALS II
30. Exercise Password Checker
31. Lists
32. List Slicing
33. Matrix
34. List Methods
35. List Methods 2
36. List Methods 3
37. Common List Patterns
38. List Unpacking
39. None
40. Dictionaries
41. DEVELOPER FUNDAMENTALS III
42. Dictionary Keys
43. Dictionary Methods
44. Dictionary Methods 2
45. Tuples
46. Tuples 2
47. Sets
48. Sets 2

1. Breaking The Flow
2. Conditional Logic
3. Indentation In Python
4. Truthy vs Falsey
5. Ternary Operator
6. Short Circuiting
7. Logical Operators
8. Exercise Logical Operators
9. is vs ==
10. For Loops
11. Iterables
12. Exercise Tricky Counter
13. range()
14. enumerate()
15. While Loops
16. While Loops 2
17. break, continue, pass
18. Our First GUI
19. DEVELOPER FUNDAMENTALS IV
20. Exercise Find Duplicates
21. Functions
22. Parameters and Arguments
23. Default Parameters and Keyword Arguments
24. return
26. Methods vs Functions
27. Docstrings
28. Clean Code
29. args and kwargs
30. Exercise Functions
31. Scope
32. Scope Rules
33. global Keyword
34. nonlocal Keyword
35. Why Do We Need Scope
36. Pure Functions
37. map()
38. filter()
39. zip()
40. reduce()
41. List Comprehensions
42. Set Comprehensions
43. Exercise Comprehensions
45. Modules in Python
47. Optional PyCharm
48. Packages in Python
49. Different Ways To Import

2. Thank You

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Udemy - Complete Machine Learning and Data Science Zero to Mastery 2020-9

Course modified date: 9 Feb 2024

Enrolled students: 9

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Udemy - Complete Machine Learning and Data Science Zero to Mastery 2020-9

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Udemy - Complete Machine Learning and Data Science Zero to Mastery 2020-9

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