Mastering the A-Z of Data Science & Machine Learning

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Course curriculum

1. Introduction to the Course and Your Instructor
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2. Introduction to Instructor
3. Course Code Notebooks and Materials
1. Introduction to the Course: Focus of the Course-Part 1
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2. Introduction to the Course: Focus of the Course-Part 2
3. Basics of Programming: Understanding the Algorithm
4. Basics of Programming: FlowCharts and Pseudocodes
5. Basics of Programming: Example of Algorithms- Making Tea Problem
6. Basics of Programming: Example of Algorithms-Searching Minimun
7. Basics of Programming: Example of Algorithms-Sorting Problem
8. Basics of Programming: Sorting Problem in Python
9. Why Python and Jupyter Notebook: Why Python
10. Why Python and Jupyter Notebook: Why Jupyter Notebooks
11. Installation of Anaconda and IPython Shell: Installing Python and Jupyter Anacon
12. Installation of Anaconda and IPython Shell: Your First Python Code- Hello World
13. Installation of Anaconda and IPython Shell: Coding in IPython Shell
14. Variable and Operator: Variables
15. Variable and Operator: Operators
16. Variable and Operator: Variable Name Quiz
17. Variable and Operator: Bool Data Type in Python
18. Variable and Operator: Comparison in Python
19. Variable and Operator: Combining Comparisons in Python
20. Variable and Operator: Combining Comparisons Quiz
21. Python Useful function: Python Function- Round
22. Python Useful function: Python Function- Divmod
23. Python Useful function: Python Function- Is instance and PowFunctions
24. Python Useful function: Python Function- Input
25. Control Flow in Python: If Python Condition
26. Control Flow in Python: if Elif Else Python Conditions
27. Control Flow in Python: More on if Elif Else Python Conditions
28. Control Flow in Python: Indentations
29. Control Flow in Python: Comments and Problem Solving Practice With If
30. Control Flow in Python: While Loop
31. Control Flow in Python: While Loop break Continue
32. Control Flow in Python: For Loop
33. Control Flow in Python: Else In For Loop
34. Control Flow in Python: Loops Practice-Sorting Problem
35. Function and Module in Python: Functions in Python
36. Function and Module in Python: DocString
37. Function and Module in Python: Input Arguments
38. Function and Module in Python: Multiple Input Arguments
39. Function and Module in Python: Ordering Multiple Input Arguments
40. Function and Module in Python: Output Arguments and Return Statement
41. Function and Module in Python: Function Practice-Output Arguments and Return Statement
42. Function and Module in Python: Variable Number of Input Arguments
43. Function and Module in Python: Variable Number of Input Arguments as Dictionary
44. Function and Module in Python: Default Values in Python
45. Function and Module in Python: Modules in Python
46. Function and Module in Python: Making Modules in Python
47. Function and Module in Python: Function Practice-Sorting List in Python
48. String in Python: Strings
49. String in Python: Multi Line Strings
50. String in Python: Indexing Strings
51. String in Python: String Methods
52. String in Python: String Escape Sequences
53. Data Structure (List, Tuple, Set, Dictionary): Introduction to Data Structure
54. Data Structure (List, Tuple, Set, Dictionary): Defining and Indexing
55. Data Structure (List, Tuple, Set, Dictionary): Insertion and Deletion
56. Data Structure (List, Tuple, Set, Dictionary): Python Practice-Insertion and Deletion
57. Data Structure (List, Tuple, Set, Dictionary): Deep Copy or Reference Slicing
58. Data Structure (List, Tuple, Set, Dictionary): Exploring Methods Using TAB Completion
59. Data Structure (List, Tuple, Set, Dictionary): Data Structure Abstract Ways
60. Data Structure (List, Tuple, Set, Dictionary): Data Structure Practice
61. NumPy for Numerical Data Processing: Introduction to NumPy
62. NumPy for Numerical Data Processing: NumPy Dimensions
63. NumPy for Numerical Data Processing: NumPy Shape, Size and Bytes
64. NumPy for Numerical Data Processing: Arange, Random and Reshape-Part 1
65. NumPy for Numerical Data Processing: Arange, Random and Reshape-Part 2
66. NumPy for Numerical Data Processing: Slicing-Part 1
67. NumPy for Numerical Data Processing: Slicing-Part 2
68. NumPy for Numerical Data Processing: NumPy Masking
69. NumPy for Numerical Data Processing: NumPy BroadCasting and Concatination
70. NumPy for Numerical Data Processing: NumPy ufuncs Speed Test
71. Pandas for Data Manipulation: Introduction to Pandas
72. Pandas for Data Manipulation: Pandas Series
73. Pandas for Data Manipulation: Pandas Data Frame
74. Pandas for Data Manipulation: Pandas Missing Values
75. Pandas for Data Manipulation: Pandas .loc and .iloc
76. Pandas for Data Manipulation: Pandas Practice-Using COVID19 Data -Part 1
77. Pandas for Data Manipulation: Pandas Practice-Using COVID19 Data -Part 2
78. Matplotlib, Seaborn, and Bokeh for Data Visualization: Introduction to Matplotlib
79. Matplotlib, Seaborn, and Bokeh for Data Visualization: Seaborn Vs. Matplotlib Style
80. Matplotlib, Seaborn, and Bokeh for Data Visualization: Histograms Kdeplot
81. Matplotlib, Seaborn, and Bokeh for Data Visualization: Seaborn Pairplot and Jointplot
82. Matplotlib, Seaborn, and Bokeh for Data Visualization: Seaborn Pairplot using Iris Data
83. Matplotlib, Seaborn, and Bokeh for Data Visualization: Introduction to Bokeh
84. Matplotlib, Seaborn, and Bokeh for Data Visualization: Bokeh Gridplot
85. Scikit-Learn for Machine Learning: Introduction to Scikit-Learn
86. Scikit-Learn for Machine Learning: Scikit-Learn for Linear Regression
87. Scikit-Learn for Machine Learning: Scikit-Learn for SVM and Random Forests
88. Scikit-Learn for Machine Learning: ScikitLearn- Trend Analysis COVID19
1. Introduction to the Course: Focus of the Course
2. Introduction to the Course: Content of the Course
3. NumPy for Numerical Data Processing: Ufuncs Add, Sum and Plus Operators
4. NumPy for Numerical Data Processing: Ufuncs Subtract Power Mod
5. NumPy for Numerical Data Processing: Ufuncs Comparisons Logical Operators
6. NumPy for Numerical Data Processing: Ufuncs Comparisons Logical Operators Quiz
7. NumPy for Numerical Data Processing: Ufuncs Comparisons Logical Operators Solution
8. NumPy for Numerical Data Processing: Ufuncs Output Argument
9. NumPy for Numerical Data Processing: NumPy Playing with Images
10. NumPy for Numerical Data Processing: NumPy Playing with Images Quiz
11. NumPy for Numerical Data Processing: NumPy Playing with Images Solution
12. NumPy for Numerical Data Processing: NumPy KNN Classifier fromScratch
13. NumPy for Numerical Data Processing: NumPy Structured Arrays
14. NumPy for Numerical Data Processing: NumPy Structured Arrays Quiz
15. NumPy for Numerical Data Processing: NumPy Structured Arrays Solution
16. Pandas for Data Manipulation and Understanding: Introduction to Pandas
17. Pandas for Data Manipulation and Understanding: Pandas Series
18. Pandas for Data Manipulation and Understanding: Pandas DataFrame
19. Pandas for Data Manipulation and Understanding: Pandas DataFrame Quiz
20. Pandas for Data Manipulation and Understanding: Pandas Missing Values
21. Pandas for Data Manipulation and Understanding: Pandas in Practice
22. Pandas for Data Manipulation and Understanding: Pandas Loc Iloc
23. Pandas for Data Manipulation and Understanding: Pandas Group by
24. Pandas for Data Manipulation and Understanding: Pandas Group by Quiz
25. Pandas for Data Manipulation and Understanding: Hierarchical Indexing
26. Pandas for Data Manipulation and Understanding: Pandas Group by Solution
27. Pandas for Data Manipulation and Understanding: Pandas Rolling
28. Pandas for Data Manipulation and Understanding: Pandas Rolling Quiz
29. Pandas for Data Manipulation and Understanding: Pandas Rolling Solution
30. Pandas for Data Manipulation and Understanding: Pandas Where
31. Pandas for Data Manipulation and Understanding: Pandas Clip
32. Pandas for Data Manipulation and Understanding: Pandas Clip Quiz
33. Pandas for Data Manipulation and Understanding: Pandas Clip Solution
34. Pandas for Data Manipulation and Understanding: Pandas Merge
35. Pandas for Data Manipulation and Understanding: Pandas Merge Quiz
36. Pandas for Data Manipulation and Understanding: Pandas Pivot Table
37. Pandas for Data Manipulation and Understanding: Pandas Strings
38. Pandas for Data Manipulation and Understanding: Pandas Merge Solution
39. Pandas for Data Manipulation and Understanding: Pandas DateTime
40. Pandas for Data Manipulation and Understanding: Pandas Hands On COVID19 Data
41. Pandas for Data Manipulation and Understanding: Pandas Hands On COVID19 Data Bug
42. Matplotlib for Data Visualization: Introduction to Matplotlib
43. Matplotlib for Data Visualization: Matplotlib Multiple Plots
44. Matplotlib for Data Visualization: Matplotlib Colors and Styles
45. Matplotlib for Data Visualization: Matplotlib Colors and Styles Quiz
46. Matplotlib for Data Visualization: Matplotlib Colors and Styles Solution
47. Matplotlib for Data Visualization: Matplotlib Colors and Styles Shortcuts
48. Matplotlib for Data Visualization: Matplotlib Axis Limits
49. Matplotlib for Data Visualization: Matplotlib Axis Limits Quiz
50. Matplotlib for Data Visualization: Matplotlib Axis Limits Solution
51. Matplotlib for Data Visualization: Matplotlib Legends Labels
52. Matplotlib for Data Visualization: Matplotlib Set Function
53. Matplotlib for Data Visualization: Matplotlib Set Function Quiz
54. Matplotlib for Data Visualization: Matplotlib Set Function Solution
55. Matplotlib for Data Visualization: Matplotlib Markers
56. Matplotlib for Data Visualization: Matplotlib Markers Randomplots
57. Matplotlib for Data Visualization: Matplotlib Scatter Plot
58. Matplotlib for Data Visualization: Matplotlib Contour Plot Quiz
59. Pandas for Data Manipulation and Understanding: Pandas DataFrame Solution
60. Matplotlib for Data Visualization: Matplotlib Contour Plot
61. Matplotlib for Data Visualization: Matplotlib Histograms
62. Matplotlib for Data Visualization: Matplotlib Contour Plot Solution
63. Matplotlib for Data Visualization: Matplotlib Subplots
64. Matplotlib for Data Visualization: Matplotlib Subplots Quiz
65. Matplotlib for Data Visualization: Matplotlib Subplots Solution
66. Matplotlib for Data Visualization: Matplotlib 3D Introduction
67. Matplotlib for Data Visualization: Matplotlib 3D Scatter Plots
68. Matplotlib for Data Visualization: Matplotlib 3D Scatter Plots Quiz
69. Matplotlib for Data Visualization: Matplotlib 3D Scatter Plots Solution
70. Matplotlib for Data Visualization: Matplotlib 3D Surface Plots
71. Seaborn for Data Visualization: Introduction to Seaborn
72. Seaborn for Data Visualization: Seaborn Relplot
73. Seaborn for Data Visualization: Seaborn Relplot Quiz
74. Seaborn for Data Visualization: Seaborn Relplot Kind Line
75. Seaborn for Data Visualization: Seaborn Relplot Solution
76. Seaborn for Data Visualization: Seaborn Relplot Facets
77. Seaborn for Data Visualization: Seaborn Relplot Facets Quiz
78. Seaborn for Data Visualization: Seaborn Relplot Facets Solution
79. Seaborn for Data Visualization: Seaborn Catplot
80. Seaborn for Data Visualization: Seaborn Heatmaps
81. Bokeh for Interactive Plotting: Introduction to Bokeh
82. Bokeh for Interactive Plotting: Bokeh Multiplots Markers
83. Bokeh for Interactive Plotting: Bokeh Multiplots Grid Plot
84. Bokeh for Interactive Plotting: Bokeh Multiplots Grid Plot Quiz
85. Bokeh for Interactive Plotting: Bokeh Multiplots Grid Plot Solution
86. Plotly for 3D Interactive Plotting: Plotly 3D Interactive Scatter Plot
87. Plotly for 3D Interactive Plotting: Plotly 3D Interactive Scatter Plot Quiz
88. Plotly for 3D Interactive Plotting: Plotly 3D Interactive Scatter Plot Solution
89. Plotly for 3D Interactive Plotting: Plotly 3D Interactive Surface Plot
90. Plotly for 3D Interactive Plotting: Plotly 3D Interactive Surface Plot Quiz
91. Plotly for 3D Interactive Plotting: Plotly 3D Interactive Surface Plot Solution
92. Geographic Maps with Folium: Geographic Maps with Folium using COVID-19 Data
93. Geographic Maps with Folium: Geographic Maps with Folium using COVID-19 Data Quiz
94. Geographic Maps with Folium: Geographic Maps with Folium using COVID-19 Data Solution
95. Pandas for Plotting: Pandas for Plotting
1. Introduction to Course: Focus of the Course
2. Probability vs Statistics: Probability vs Statistics
3. Sets: Definition of Set
4. Sets: Definition of Set Exercise 01
5. Sets: Definition of Set Solution 01
6. Sets: Definition of Set Exercise 02
7. Sets: Definition of Set Solution 02
8. Sets: Cardinality of a Set
9. Sets: Subsets PowerSet UniversalSet
10. Sets: Python Practice Subsets
11. Sets: PowerSets Solution
12. Sets: Operations
13. Sets: Operations Exercise 01
14. Sets: Operations Solution 01
15. Sets: Operations Exercise 02
16. Sets: Operations Solution 02
17. Sets: Operations Exercise 03
18. Sets: Operations Solution 03
19. Sets: Python Practice Operations
20. Sets: VennDiagrams Operations
21. Sets: Homework
22. Experiment: Random Experiment
23. Experiment: Outcome and Sample Space
24. Experiment: Outcome and Sample Space Exercise 01
25. Experiment: Outcome and Sample Space Solution 01
26. Experiment: Event
27. Experiment: Event Exercise 01
28. Experiment: Event Solution 01
29. Experiment: Event Exercise 02
30. Experiment: Event Solution 02
31. Experiment: Recap and Homework
32. Probability Model: Probability Model
33. Probability Model: Probability Axioms
34. Probability Model: Probability Axioms Derivations
35. Probability Model: Probability Axioms Derivations Exercise 01
36. Probability Model: Probability Axioms Derivations Solution 01
37. Probability Model: Probablility Models Example
38. Probability Model: Probablility Models More Examples
39. Probability Model: Probablility Models Continous
40. Probability Model: Conditional Probability
41. Probability Model: Conditional Probability Example
42. Probability Model: Conditional Probability Formula
43. Probability Model: Conditional Probability in Machine Learning
44. Probability Model: Conditional Probability Total Probability Theorem
45. Probability Model: Probablility Models Independence
46. Probability Model: Probablility Models Conditional Independence
47. Probability Model: Probablility Models Conditional Independence Exercise 01
48. Probability Model: Probablility Models Conditional Independence Solution 01
49. Probability Model: Probablility Models BayesRule
50. Probability Model: Probablility Models towards Random Variables
51. Probability Model: HomeWork
52. Random Variables: Introduction
53. Random Variables: Random Variables Examples
54. Random Variables: Random Variables Examples Exercise 01
55. Random Variables: Random Variables Examples Solution 01
56. Random Variables: Bernulli Random Variables
57. Random Variables: Bernulli Trail Python Practice
58. Random Variables: Bernulli Trail Python Practice Exercise 01
59. Random Variables: Bernulli Trail Python Practice Solution 01
60. Random Variables: Geometric Random Variable
61. Random Variables: Geometric Random Variable Normalization Proof Optional
62. Random Variables: Geometric Random Variable Python Practice
63. Random Variables: Binomial Random Variables
64. Random Variables: Binomial Python Practice
65. Random Variables: Random Variables in Real DataSets
66. Random Variables: Random Variables in Real DataSets Exercise 01
67. Random Variables: Random Variables in Real DataSets Solution 01
68. Random Variables: Homework
69. Continous Random Variables: Zero Probability to Individual Values
70. Continous Random Variables: Zero Probability to Individual Values Exercise 01
71. Continous Random Variables: Zero Probability to Individual Values Solution 01
72. Continous Random Variables: Probability Density Functions
73. Continous Random Variables: Probability Density Functions Exercise 01
74. Continous Random Variables: Probability Density Functions Solution 01
75. Continous Random Variables: Uniform Distribution
76. Continous Random Variables: Uniform Distribution Exercise 01
77. Continous Random Variables: Uniform Distribution Solution 01
78. Continous Random Variables: Uniform Distribution Python
79. Continous Random Variables: Exponential
80. Continous Random Variables: Exponential Exercise 01
81. Continous Random Variables: Exponential Solution 01
82. Continous Random Variables: Exponential Python
83. Continous Random Variables: Gaussian Random Variables
84. Continous Random Variables: Gaussian Random Variables Exercise 01
85. Continous Random Variables: Gaussian Random Variables Solution 01
86. Continous Random Variables: Gaussian Python
87. Continous Random Variables: Transformation of Random Variables
88. Continous Random Variables: Homework
89. Expectations: Definition
90. Expectations: Sample Mean
91. Expectations: Law of Large Numbers
92. Expectations: Law of Large Numbers Famous Distributions
93. Expectations: Law of Large Numbers Famous Distributions Python
94. Expectations: Variance
95. Expectations: Homework
96. Project Bayes Classifier: Project Bayes Classifier From Scratch
97. Multiple Random Variables: Joint Distributions
98. Multiple Random Variables: Joint Distributions Exercise 01
99. Multiple Random Variables: Joint Distributions Solution 01
100. Multiple Random Variables: Joint Distributions Exercise 02
101. Multiple Random Variables: Joint Distributions Solution 02
102. Multiple Random Variables: Joint Distributions Exercise 03
103. Multiple Random Variables: Joint Distributions Solution 03
104. Multiple Random Variables: Multivariate Gaussian
105. Multiple Random Variables: Conditioning Independence
106. Multiple Random Variables: Classification
107. Multiple Random Variables: Naive Bayes Classification
108. Multiple Random Variables: Regression
109. Multiple Random Variables: Curse of Dimensionality
110. Multiple Random Variables: Homework
111. Optional Estimation: Parametric Distributions
112. Optional Estimation: MLE
113. Optional Estimation: LogLiklihood
114. Optional Estimation: MAP
115. Optional Estimation: Logistic Regression
116. Optional Estimation: Ridge Regression
117. Optional Estimation: DNN
118. Mathematical Derivations for Math Lovers (Optional): Permutations
119. Mathematical Derivations for Math Lovers (Optional): Combinations
120. Mathematical Derivations for Math Lovers (Optional): Binomial Random Variable
121. Mathematical Derivations for Math Lovers (Optional): Logistic Regression Formulation
122. Mathematical Derivations for Math Lovers (Optional): Logistic Regression Derivation
1. Introduction to the Course: Focus of the Course
2. Introduction to the Course: Python Practical of the Course
3. Why Machine Learning: Machine Learning Applications-Part 1
4. Why Machine Learning: Machine Learning Applications-Part 2
5. Why Machine Learning: Why Machine Learning is Trending Now
6. Process of Learning from Data: Supervised Learning
7. Process of Learning from Data: UnSupervised Learning and Reinforcement Learning
8. Machine Learning Methods: Features
9. Machine Learning Methods: Features Practice with Python
10. Machine Learning Methods: Regression
11. Machine Learning Methods: Regression Practice with Python
12. Machine Learning Methods: Classsification
13. Machine Learning Methods: Classification Practice with Python
14. Machine Learning Methods: Clustering
15. Machine Learning Methods: Clustering Practice with Python
16. Data Preparation and Preprocessing: Handling Image Data
17. Data Preparation and Preprocessing: Handling Video and Audio Data
18. Data Preparation and Preprocessing: Handling Text Data
19. Data Preparation and Preprocessing: One Hot Encoding
20. Data Preparation and Preprocessing: Data Standardization
21. Machine Learning Models and Optimization: Machine Learning Model 1
22. Machine Learning Models and Optimization: Machine Learning Model 2
23. Machine Learning Models and Optimization: Machine Learning Model 3
24. Machine Learning Models and Optimization: Training Process, Error, Cost and Loss
25. Machine Learning Models and Optimization: Optimization
26. Building Machine Learning Model from Scratch: Linear Regression from Scratch- Part 1
27. Building Machine Learning Model from Scratch: Linear Regression from Scratch- Part 2
28. Building Machine Learning Model from Scratch: Minimun-to-mean Distance Classifier from Scratch- Part 1
29. Building Machine Learning Model from Scratch: Minimun-to-mean Distance Classifier from Scratch- Part 2
30. Building Machine Learning Model from Scratch: K-means Clustering from Scratch- Part 1
31. Building Machine Learning Model from Scratch: K-means Clustering from Scratch- Part 2
32. Overfitting, Underfitting and Generalization: Overfitting Introduction
33. Overfitting, Underfitting and Generalization: Overfitting example on Python
34. Overfitting, Underfitting and Generalization: Regularization
35. Overfitting, Underfitting and Generalization: Generalization
36. Overfitting, Underfitting and Generalization: Data Snooping and the Test Set
37. Overfitting, Underfitting and Generalization: Cross-validation
38. Machine Learning Model Performance Metrics: The Accuracy
39. Machine Learning Model Performance Metrics: The Confusion Matrix
40. Dimensionality Reduction: The Curse of Dimensionality
41. Dimensionality Reduction: The Principal Component Analysis (PCA)
42. Deep Learning Overview: Introduction to Deep Neural Networks (DNN)
43. Deep Learning Overview: Introduction to Convolutional Neural Networks (CNN)
44. Deep Learning Overview: Introduction to Recurrent Neural Networks (CNN)
45. Hands-on Machine Learning Project Using Scikit-Learn: Principal Component Analysis (PCA) with Python
46. Hands-on Machine Learning Project Using Scikit-Learn: Pipeline in Scikit-Learn for Machine Learning Project
47. Hands-on Machine Learning Project Using Scikit-Learn: Cross-validation with Python
48. Hands-on Machine Learning Project Using Scikit-Learn: Face Recognition Project with Python
49. OPTIONAL Section- Mathematics Wrap-up: Mathematical Wrap-up on Machine Learning
1. Introduction: Focus of the Course
2. Features in Data Science: Introduction to Feature in Data Science
3. Features in Data Science: Marking Facial Features
4. Features in Data Science: Feature Space
5. Features in Data Science: Features Dimensions
6. Features in Data Science: Features Dimensions Activity
7. Features in Data Science: Why Dimensionality Reduction
8. Features in Data Science: Activity-Dimensionality Reduction
9. Features in Data Science: Feature Dimensionality Reduction Methods
10. Feature Selection: Why Feature Selection
11. Feature Selection: Feature Selection Methods
12. Feature Selection: Filter Methods
13. Feature Selection: Wrapper Methods
14. Feature Selection: Embedded Methods
15. Feature Selection: Search Strategy
16. Feature Selection: Search Strategy Activity
17. Feature Selection: Statistical Based Methods
18. Feature Selection: Information Theoratic Methods
19. Feature Selection: Similarity Based Methods Introduction
20. Feature Selection: Similarity Based Methods Criteria
21. Feature Selection: Activity- Feature Selection in Python
22. Feature Selection: Activity- Feature Selection
23. Mathematical Foundation: Introduction to Mathematical Foundation of Feature Selection
24. Mathematical Foundation: Closure Of A Set
25. Mathematical Foundation: Linear Combinations
26. Mathematical Foundation: Linear Independence
27. Mathematical Foundation: Vector Space
28. Mathematical Foundation: Basis and Dimensions
29. Mathematical Foundation: Coordinates vs Dimensions
30. Mathematical Foundation: SubSpace
31. Mathematical Foundation: Orthonormal Basis
32. Mathematical Foundation: Matrix Product
33. Mathematical Foundation: Least Squares
34. Mathematical Foundation: Rank
35. Mathematical Foundation: Eigen Space
36. Mathematical Foundation: Positive Semi Definite Matrix
37. Mathematical Foundation: Singular Value Decomposition SVD
38. Mathematical Foundation: Lagrange Multipliers
39. Mathematical Foundation: Vector Derivatives
40. Mathematical Foundation: Linear Algebra Module Python
41. Mathematical Foundation: Activity-Linear Algebra Module Python
42. Feature Extraction: Feature Extraction Introduction
43. Feature Extraction: PCA Introduction
44. Feature Extraction: PCA Criteria
45. Feature Extraction: PCA Properties
46. Feature Extraction: PCA Max Variance Formulation
47. Feature Extraction: PCA Derivation
48. Feature Extraction: PCA Implementation
49. Feature Extraction: PCA For Small Sample Size Problems(DualPCA)
50. Feature Extraction: PCA vs SVD
51. Feature Extraction: Kernel PCA
52. Feature Extraction: Kernel PCA vs ISOMAP
53. Feature Extraction: Kernel PCA vs The Rest
54. Feature Extraction: Encoder Decoder Networks For Dimensionality Reduction vs kernel PCA
55. Feature Extraction: Supervised PCA and Fishers Linear Discriminant Analysis
56. Feature Extraction: Supervised PCA and Fishers Linear Discriminant Analysis Activity
57. Feature Extraction: Dimensionality Reduction Pipelines Python Project
58. Feature Engineering: Categorical Features
59. Feature Engineering: Categorical Features Python
60. Feature Engineering: Text Features
61. Feature Engineering: Image Features
62. Feature Engineering: Derived Features
63. Feature Engineering: Derived Features Histogram Of Gradients Local Binary Patterns
64. Feature Engineering: Feature Scaling
65. Feature Engineering: Activity-Feature Scaling
1. Introduction to the Course: Why Deep learning Networks (DNN)
2. Introduction to Machine Learning: Introduction To Machine Learning
3. Introduction to Machine Learning: Classification
4. Introduction to Machine Learning: Classification Exercise
5. Introduction to Machine Learning: Classification Solution
6. Introduction to Machine Learning: Classification Training Process And Prediction Probablities
7. Introduction to Machine Learning: Classification Prediction Probablities Exercise
8. Introduction to Machine Learning: Classification Prediction Probablities Exercise Solution
9. Introduction to Machine Learning: Regression
10. Introduction to Machine Learning: Regression Exercise
11. Introduction to Machine Learning: Regression Exercise Solution
12. Introduction to Machine Learning: Supervised Learning
13. Introduction to Machine Learning: UnSupervised Learning
14. Introduction to Machine Learning: Reinforcement Learning
15. Introduction to Machine Learning: Machine Learning Model
16. Introduction to Machine Learning: Machine Learning Model Example
17. Introduction to Machine Learning: Machine Learning Model Exercise
18. Introduction to Machine Learning: Machine Learning Model Exercise Solution
19. Introduction to Machine Learning: Machine Learning Model Types
20. Introduction to Machine Learning: Machine Learning Model Linearity
21. Introduction to Machine Learning: Machine Learning Model Linearity Exercise
22. Introduction to Machine Learning: Machine Learning Model Linearity Exercise Solution
23. Introduction to Machine Learning: Machine Learning Model Multi Target Models
24. Introduction to Machine Learning: Machine Learning Model Multi Target Models Exercise
25. Introduction to Machine Learning: Machine Learning Model Multi Target Models Exercise Solution
26. Introduction to Machine Learning: Machine Learning Model Training Exercise
27. Introduction to Machine Learning: Machine Learning Model Training Exercise Solution
28. Introduction to Machine Learning: Machine Learning Model Training Loss
29. Introduction to Machine Learning: Machine Learning Model Hyperparameters Exercise
30. Introduction to Machine Learning: Machine Learning Model Hyperparameters Exercise Solution
31. Introduction to Machine Learning: Machine Learning Occam's Razor
32. Introduction to Machine Learning: Machine Learning Overfitting
33. Introduction to Machine Learning: Machine Learning Overfitting Exercise
34. Introduction to Machine Learning: Machine Learning Overfitting Exercise Solution Regularization
35. Introduction to Machine Learning: Machine Learning Overfitting Generalization
36. Introduction to Machine Learning: Machine Learning Data Snooping
37. Introduction to Machine Learning: Machine Learning Cross Validation
38. Introduction to Machine Learning: Machine Learning Hypterparameter Tunning Exercise
39. Introduction to Machine Learning: Machine Learning Hypterparameter Tunning Exercise Solution
40. DNN and Deep Learning Basics: Why PyTorch
41. DNN and Deep Learning Basics: PyTorch Installation and Tensors Introduction
42. DNN and Deep Learning Basics: Automatic Diffrenciation Pytorch New
43. DNN and Deep Learning Basics: Why DNNs in Machine Learning
44. DNN and Deep Learning Basics: Representational Power and Data Utilization Capacity of DNN
45. DNN and Deep Learning Basics: Perceptron
46. DNN and Deep Learning Basics: Perceptron Exercise
47. DNN and Deep Learning Basics: Perceptron Exercise Solution
48. DNN and Deep Learning Basics: Perceptron Implementation
49. DNN and Deep Learning Basics: DNN Architecture
50. DNN and Deep Learning Basics: DNN Architecture Exercise
51. DNN and Deep Learning Basics: DNN Architecture Exercise Solution
52. DNN and Deep Learning Basics: DNN ForwardStep Implementation
53. DNN and Deep Learning Basics: DNN Why Activation Function Is Required
54. DNN and Deep Learning Basics: DNN Why Activation Function Is Required Exercise
55. DNN and Deep Learning Basics: DNN Why Activation Function Is Required Exercise Solution
56. DNN and Deep Learning Basics: DNN Properties Of Activation Function
57. DNN and Deep Learning Basics: DNN Activation Functions In Pytorch
58. DNN and Deep Learning Basics: DNN What Is Loss Function
59. DNN and Deep Learning Basics: DNN What Is Loss Function Exercise
60. DNN and Deep Learning Basics: DNN What Is Loss Function Exercise Solution
61. DNN and Deep Learning Basics: DNN What Is Loss Function Exercise 02
62. DNN and Deep Learning Basics: DNN What Is Loss Function Exercise 02 Solution
63. DNN and Deep Learning Basics: DNN Loss Function In Pytorch
64. DNN and Deep Learning Basics: DNN Gradient Descent
65. DNN and Deep Learning Basics: DNN Gradient Descent Exercise
66. DNN and Deep Learning Basics: DNN Gradient Descent Exercise Solution
67. DNN and Deep Learning Basics: DNN Gradient Descent Implementation
68. DNN and Deep Learning Basics: DNN Gradient Descent Stochastic Batch Minibatch
69. DNN and Deep Learning Basics: DNN Gradient Descent Summary
70. DNN and Deep Learning Basics: DNN Implemenation Gradient Step
71. DNN and Deep Learning Basics: DNN Implemenation Stochastic Gradient Descent
72. DNN and Deep Learning Basics: DNN Implemenation Batch Gradient Descent
73. DNN and Deep Learning Basics: DNN Implemenation Minibatch Gradient Descent
74. DNN and Deep Learning Basics: DNN Implemenation In PyTorch
75. DNN and Deep Learning Basics: DNN Weights Initializations
76. DNN and Deep Learning Basics: DNN Learning Rate
77. DNN and Deep Learning Basics: DNN Batch Normalization
78. DNN and Deep Learning Basics: DNN batch Normalization Implementation
79. DNN and Deep Learning Basics: DNN Optimizations
80. DNN and Deep Learning Basics: DNN Dropout
81. DNN and Deep Learning Basics: DNN Dropout In PyTorch
82. DNN and Deep Learning Basics: DNN Early Stopping
83. DNN and Deep Learning Basics: DNN Hyperparameters
84. DNN and Deep Learning Basics: DNN Pytorch CIFAR10 Example
85. Python for Data Science: NumPy Pandas and Matplotlib (Part 2)
86. Python for Data Science: NumPy Pandas and Matplotlib (Part 1)
87. Python for Data Science: NumPy Pandas and Matplotlib (Part 3)
88. Python for Data Science: NumPy Pandas and Matplotlib (Part 4)
89. Python for Data Science: NumPy Pandas and Matplotlib (Part 5)
90. Python for Data Science: NumPy Pandas and Matplotlib (Part 6)
91. Python for Data Science: DataSet Preprocessing
92. Python for Data Science: TensorFlow for classification
93. Implementation of DNN for COVID 19 Analysis: COVID19 Data Analysis
94. Implementation of DNN for COVID 19 Analysis: COVID19 Regression with TensorFlow
1. Introduction: Why CNN
2. Introduction: Focus of the Course
3. Image Processing: Gray Scale Images
4. Image Processing: RGB Images
5. Image Processing: Reading and Showing Images in Python
6. Image Processing: Converting an Image to Grayscale in Python
7. Image Processing: Image Formation
8. Image Processing: Image Blurring 1
9. Image Processing: Image Blurring 2
10. Image Processing: General Image Filtering
11. Image Processing: Convolution
12. Image Processing: Edge Detection
13. Image Processing: Image Sharpening
14. Image Processing: Implementation of Image Blurring Edge Detection Image Sharpening in Python
15. Image Processing: Parameteric Shape Detection
16. Image Processing: Image Processing Activity
17. Object Detection: Introduction to Object Detection
18. Object Detection: Classification PipleLine
19. Object Detection: Sliding Window Implementation
20. Object Detection: Shift Scale Rotation Invariance
21. Object Detection: Person Detection
22. Object Detection: HOG Features
23. Object Detection: Hand Engineering vs CNNs
24. Object Detection: Object Detection Activity
25. Deep Neural Network Architecture: Convolution Revisited
26. Deep Neural Network Architecture: Implementing Convolution in Python Revisited
27. Deep Neural Network Architecture: Why Convolution
28. Deep Neural Network Architecture: Filters Padding Strides
29. Deep Neural Network Architecture: Pooling Tensors
30. Deep Neural Network Architecture: CNN Example
31. Deep Neural Network Architecture: Convolution and Pooling Details
32. Deep Neural Network Architecture: NonVectorized Implementations of Conv2d and Pool2d
33. Deep Neural Network Architecture: Deep Neural Network Architecture Activity
34. Gradient Descent in CNNs: Example Setup
35. Gradient Descent in CNNs: Why Derivaties
36. Gradient Descent in CNNs: What is Chain Rule
37. Gradient Descent in CNNs: Applying Chain Rule
38. Gradient Descent in CNNs: Gradients of Convolutional Layer
39. Gradient Descent in CNNs: Extending To Multiple Filters
40. Gradient Descent in CNNs: Gradients of MaxPooling Layer
41. Gradient Descent in CNNs: Extending to Multiple Layers
42. Gradient Descent in CNNs: Implementation in Numpy ForwardPass.mp4.
43. Gradient Descent in CNNs: Implementation in Numpy BackwardPass 1
44. Gradient Descent in CNNs: Implementation in Numpy BackwardPass 2
45. Gradient Descent in CNNs: Implementation in Numpy BackwardPass 3
46. Gradient Descent in CNNs: Implementation in Numpy BackwardPass 4
47. Gradient Descent in CNNs: Implementation in Numpy BackwardPass 5
48. Gradient Descent in CNNs: Gradient Descent in CNNs Activity
49. Introduction to TensorFlow: Introduction
50. Introduction to TensorFlow: FashionMNIST Example Plan Neural Network
51. Introduction to TensorFlow: FashionMNIST Example CNN
52. Introduction to TensorFlow: Introduction to TensorFlow Activity
53. Classical CNNs: LeNet
54. Classical CNNs: AlexNet
55. Classical CNNs: VGG
56. Classical CNNs: InceptionNet
57. Classical CNNs: GoogLeNet
58. Classical CNNs: Resnet
59. Classical CNNs: Classical CNNs Activity
60. Transfer Learning: What is Transfer learning
61. Transfer Learning: Why Transfer Learning
62. Transfer Learning: ImageNet Challenge
63. Transfer Learning: Practical Tips
64. Transfer Learning: Project in TensorFlow
65. Transfer Learning: Transfer Learning Activity
66. Yolo: Image Classfication Revisited
67. Yolo: Sliding Window Object Localization
68. Yolo: Sliding Window Efficient Implementation
69. Yolo: Yolo Introduction
70. Yolo: Yolo Training Data Generation
71. Yolo: Yolo Anchor Boxes
72. Yolo: Yolo Algorithm
73. Yolo: Yolo Non Maxima Supression
74. Yolo: RCNN
75. Yolo: Yolo Activity
76. Face Verification: Problem Setup
77. Face Verification: Project Implementation
78. Face Verification: Face Verification Activity
79. Neural Style Transfer: Problem Setup
80. Neural Style Transfer: Implementation Tensorflow Hub
1. Introduction to Course: Focus of the Course
2. Applications of RNN (Motivation): Human Activity Recognition
3. Applications of RNN (Motivation): Image Captioning
4. Applications of RNN (Motivation): Machine Translation
5. Applications of RNN (Motivation): Speech Recognition
6. Applications of RNN (Motivation): Stock Price Predictions
7. Applications of RNN (Motivation): When to Model RNN
8. Applications of RNN (Motivation): Activity
9. RNN Architecture: Introduction to Module
10. RNN Architecture: Fixed Length Memory Model
11. RNN Architecture: Fixed Length Memory Model Exercise
12. RNN Architecture: Fixed Length Memory Model Exercise Solution Part 01
13. RNN Architecture: Fixed Length Memory Model Exercise Solution Part 02
14. RNN Architecture: Infinite Memory Architecture
15. RNN Architecture: Infinite Memory Architecture Exercise
16. RNN Architecture: Infinite Memory Architecture Solution
17. RNN Architecture: Weight Sharing
18. RNN Architecture: Notations
19. RNN Architecture: ManyToMany Model
20. RNN Architecture: ManyToMany Model Exercise 01
21. RNN Architecture: ManyToMany Model Solution 01
22. RNN Architecture: ManyToMany Model Exercise 02
23. RNN Architecture: ManyToMany Model Solution 02
24. RNN Architecture: ManyToONE Model
25. RNN Architecture: ManyToOne Model Exercise
26. RNN Architecture: ManyToOne Model Solution
27. RNN Architecture: OneToMany Model
28. RNN Architecture: OneToMany Model Exercise
29. RNN Architecture: OneToMany Model Solution
30. RNN Architecture: Activity Many to One
31. RNN Architecture: Activity Many to One Exercise
32. RNN Architecture: Activity Many to One Solution
33. RNN Architecture: ManyToMany Different Sizes Model
34. RNN Architecture: Activity Many to Many Nmt
35. RNN Architecture: Models Summary
36. RNN Architecture: Deep RNNs
37. RNN Architecture: Deep RNNs Exercise
38. RNN Architecture: Deep RNNs Solution
39. Gradient Decsent in RNN: Introduction to Gradient Descent Module
40. Gradient Decsent in RNN: Example Setup
41. Gradient Decsent in RNN: Equations
42. Gradient Decsent in RNN: Equations Exercise
43. Gradient Decsent in RNN: Equations Solution
44. Gradient Decsent in RNN: Loss Function
45. Gradient Decsent in RNN: Why Gradients
46. Gradient Decsent in RNN: Why Gradients Exercise
47. Gradient Decsent in RNN: Why Gradients Solution
48. Gradient Decsent in RNN: Chain Rule
49. Gradient Decsent in RNN: Chain Rule in Action
50. Gradient Decsent in RNN: BackPropagation Through Time
51. Gradient Decsent in RNN: Activity
52. RNN Implementation: Automatic Diffrenciation
53. RNN Implementation: Automatic Diffrenciation Pytorch
54. RNN Implementation: Language Modeling Next Word Prediction Vocabulary Index
55. RNN Implementation: Language Modeling Next Word Prediction Vocabulary Index Embeddings
56. RNN Implementation: Language Modeling Next Word Prediction RNN Architecture
57. RNN Implementation: Language Modeling Next Word Prediction Python 1
58. RNN Implementation: Language Modeling Next Word Prediction Python 2
59. RNN Implementation: Language Modeling Next Word Prediction Python 3
60. RNN Implementation: Language Modeling Next Word Prediction Python 4
61. RNN Implementation: Language Modeling Next Word Prediction Python 5
62. RNN Implementation: Language Modeling Next Word Prediction Python 6
63. Sentiment Classification using RNN:Vocabulary Implementation
64. Sentiment Classification using RNN:Vocabulary Implementation Helpers
65. Sentiment Classification using RNN:Vocabulary Implementation From File
66. Sentiment Classification using RNN:Vectorizer
67. Sentiment Classification using RNN:RNN Setup 1
68. Sentiment Classification using RNN:RNN Setup 2
69. Sentiment Classification using RNN:WhatNext
70. Vanishing Gradients in RNN: Introduction to Better RNNs Module
71. Vanishing Gradients in RNN: Introduction Vanishing Gradients in RNN
72. Vanishing Gradients in RNN: GRU
73. Vanishing Gradients in RNN: GRU Optional
74. Vanishing Gradients in RNN: LSTM
75. Vanishing Gradients in RNN: LSTM Optional
76. Vanishing Gradients in RNN: Bidirectional RNN
77. Vanishing Gradients in RNN: Attention Model
78. Vanishing Gradients in RNN: Attention Model Optional
79. TensorFlow: Introduction to TensorFlow
80. TensorFlow: TensorFlow Text Classification Example using RNN
81. Project I_ Book Writer: Introduction
82. Project I_ Book Writer: Data Mapping
83. Project I_ Book Writer: Modling RNN Architecture
84. Project I_ Book Writer: Modling RNN Model in TensorFlow
85. Project I_ Book Writer: Modling RNN Model Training
86. Project I_ Book Writer: Modling RNN Model Text Generation
87. Project I_ Book Writer: Activity
88. Project II_ Stock Price Prediction: Problem Statement
89. Project II_ Stock Price Prediction: Data Set
90. Project II_ Stock Price Prediction: Data Prepration
91. Project II_ Stock Price Prediction: RNN Model Training and Evaluation
92. Project II_ Stock Price Prediction: Activity
93. Further Readings and Resourses: Further Readings and Resourses 1
1. Motivation Reinforcement Learning: What is Reinforcement Learning
2. Motivation Reinforcement Learning: What is Reinforcement Learning Hiders and Seekers by OpenAI
3. Motivation Reinforcement Learning: RL vs Other ML Frameworks
4. Motivation Reinforcement Learning: Why Reinforcement Learning
5. Motivation Reinforcement Learning: Examples of Reinforcement Learning
6. Motivation Reinforcement Learning: Limitations of Reinforcement Learning
7. Motivation Reinforcement Learning: Exercises
8. Terminology of Reinforcement Learning: What is Environment
9. Terminology of Reinforcement Learning: What is Environment_2
10. Terminology of Reinforcement Learning: What is Agent
11. Terminology of Reinforcement Learning: What is State
12. Terminology of Reinforcement Learning: State Belongs to Environment and not to Agent
13. Terminology of Reinforcement Learning: What is Action
14. Terminology of Reinforcement Learning: What is Reward
15. Terminology of Reinforcement Learning: Goal
16. Terminology of Reinforcement Learning: Policy
17. Terminology of Reinforcement Learning: Summary
18. GridWorld Example: Setup 1
19. GridWorld Example: Setup 2
20. GridWorld Example: Setup 3
21. GridWorld Example: Policy Comparison
22. GridWorld Example: Deterministic Environment
23. GridWorld Example: Stochastic Environment
24. GridWorld Example: Stochastic Environment 2
25. GridWorld Example: Stochastic Environment 3
26. GridWorld Example: Non Stationary Environment
27. GridWorld Example: GridWorld Summary
28. GridWorld Example: Activity
29. Markov Decision Process Prerequisites: Probability
30. Markov Decision Process Prerequisites: Probability 2
31. Markov Decision Process Prerequisites: Probability 3
32. Markov Decision Process Prerequisites: Conditional Probability
33. Markov Decision Process Prerequisites: Conditional Probability Fun Example
34. Markov Decision Process Prerequisites: Joint Probability
35. Markov Decision Process Prerequisites: Joint probability 2
36. Markov Decision Process Prerequisites: Joint Probability 3
37. Markov Decision Process Prerequisites: Expected Value
38. Markov Decision Process Prerequisites: Conditional Expectation
39. Markov Decision Process Prerequisites: Modeling Uncertainity of Environment
40. Markov Decision Process Prerequisites: Modeling Uncertainity of Environment 2
41. Markov Decision Process Prerequisites: Modeling Uncertainity of Environment 3
42. Markov Decision Process Prerequisites: Modeling Uncertainity of Environment Stochastic Policy
43. Markov Decision Process Prerequisites: Modeling Uncertainity of Environment Stochastic Policy 2
44. Markov Decision Process Prerequisites: Modeling Uncertainity of Environment Value Functions
45. Markov Decision Process Prerequisites: Running Averages
46. Markov Decision Process Prerequisites: Running Averages 2
47. Markov Decision Process Prerequisites: Running Averages as Temporal Difference
48. Markov Decision Process Prerequisites: Activity
49. Elements of Markov Decision Process: Markov Property
50. Elements of Markov Decision Process: State Space
51. Elements of Markov Decision Process: Action Space
52. Elements of Markov Decision Process: Transition Probabilities
53. Elements of Markov Decision Process: Reward Function
54. Elements of Markov Decision Process: Discount Factor
55. Elements of Markov Decision Process: Summary
56. Elements of Markov Decision Process: Activity
57. More on Reword: MOR Quiz 1
58. More on Reword: MOR Quiz Solution 1
59. More on Reword: MOR Quiz 2
60. More on Reword: MOR Quiz Solution 2
61. More on Reword: MOR Reward Scaling
62. More on Reword: MOR Infinite Horizons
63. More on Reword: MOR Quiz 3
64. More on Reword: MOR Quiz Solution 3
65. Solving MDP: MDP Recap
66. Solving MDP: Value Functions
67. Solving MDP: Optimal Value Function
68. Solving MDP: Optimal Policy
69. Solving MDP: Balman Equation
70. Solving MDP: Value Iteration
71. Solving MDP: Value Iteration Quiz
72. Solving MDP: Value Iteration Quiz Gamma Missing
73. Solving MDP: Value Iteration Solution
74. Solving MDP: Problems of Value Iteration
75. Solving MDP: Policy Evaluation
76. Solving MDP: Policy Evaluation 2
77. Solving MDP: Policy Evaluation 3
78. Solving MDP: Policy Evaluation Closed Form Solution
79. Solving MDP: Policy Iteration
80. Solving MDP: State Action Values
81. Solving MDP: V and Q Comparisons
82. Value Approximation: What does it mean that MDP is Unknown
83. Value Approximation: Why Transition Probabilities are Important
84. Value Approximation: Model Based Solutions
85. Value Approximation: Model Free Solutions
86. Value Approximation: Monte-Carlo Learning
87. Value Approximation: Monte-Carlo Learning Example
88. Value Approximation: Monte-Carlo Learning Limitations
89. Temporal Differencing-Q Learning: Running Average
90. Temporal Differencing-Q Learning: Learning Rate
91. Temporal Differencing-Q Learning: Learning Equation
92. Temporal Differencing-Q Learning: TD Algorithm
93. Temporal Differencing-Q Learning: Exploration vs Exploitation
94. Temporal Differencing-Q Learning: Epsilon Greedy Policy
95. Temporal Differencing-Q Learning: SARSA
96. Temporal Differencing-Q Learning: Q-Learning
97. Temporal Differencing-Q Learning: Q-Learning Implementation for MAPROVER Clipped
98. TD Lambda: N Step Look a Head
99. TD Lambda: Formulation
100. TD Lambda: Values
101. TD Lambda: TD Eligibility Trace
102. TD Lambda: TD Q-Learning TD Lambda
103. Project Frozenlake (Open AI Gym): Frozenlake 1
104. Project Frozenlake (Open AI Gym): Frozenlake Implementation

About this course

$199.99
793 lessons
99.5 hours of video content

Mastering the A-Z of Data Science & Machine Learning

Course curriculum

Introduction to the Course

Basics for Data Science: Python for Data Science and Data Analysis

Basics for Data Science: Data Understanding and Data Visualization with Python

Basics for Data Science: Mastering Probability and Statistics in Python

Machine Learning: Machine Learning Crash Course

Machine Learning: Feature Engineering and Dimensionality Reduction with Python

Deep learning: Artificial Neural Networks with Python

Deep learning: Convolutional Neural Networks with Python

Deep learning: Recurrent Neural Networks with Python

Reinforcement Learning

About this course