Certificate in Data Science and Machine Learning

How It Works

  1. 1. Select Certification & Register
  2. 2. Receive Online e-Learning Access (LMS)
  3. 3. Take exam online anywhere, anytime
  4. 4. Get certified & Increase Employability

Test Details

  • Duration: 60 minutes
  • No. of questions: 50
  • Maximum marks: 50, Passing marks: 25 (50%).
  • There is NO negative marking in this module.
  • Online exam.

Benefits of Certification


$69.00 /-
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Why should I take this certification?

The primary objective of this certification course is to provide a comprehensive understanding of data science and machine learning principles using Python.

At first glance, the various sections of this course may appear to stand alone, but in reality, they are interconnected and follow a nearly sequential order. At the end of every subsection, you are assigned homework to further strengthen your learning. 

Each of these assessments builds upon the concepts and methodologies you've previously acquired. Many of these assessment tasks will involve coding, as our primary goal is to advance your proficiency in practical implementations.

Upon completing this course, you will possess the capability to confidently address real-world challenges, paving the way for continuous career advancement. You will also have a firm grasp of all the fundamental concepts required for success in the field of data science.

Note: This course is exclusively available online (videos), and no hard copies are provided.

What you will learn from this certification course:

  • Data understanding and data visualization with Python
  • Explore probability and statistics in Python
  • Learn feature engineering and dimensionality reduction with Python
  • Cover artificial neural networks with Python
  • Cover CNN and RNN with Python
  • Build your own AI applications

How will I benefit from this certification?

Acquiring skills in data science and machine learning can open up numerous career opportunities. Many industries, including finance, healthcare, technology, and marketing, are actively seeking professionals with expertise in data science and machine learning.

Data science and machine learning professionals are often well-compensated due to the high demand for their skills. Completing a course can lead to a significant increase in your earning potential. 

Corporate training is also available, click here to know more.

Data Science and Machine Learning Table of Contents

https://www.vskills.in/certification/data-science-and-machine-learning-table-of-contents

Data Science and Machine Learning Practice Questions

https://www.vskills.in/practice/data-science-and-machine-learning-practice-questions

Data Science and Machine Learning Interview Questions

https://www.vskills.in/interview-questions/data-science-and-machine-learning-certificate-interview-questions

Companies that hire Data Science and Machine Learning Professionals

Vskills Certified Candidate will find employment in Top MNC's like Tech Mahindra, Akamai, Mphasis, Cognizant, HCL, Accenture, TCS.

Jobs in Data Science and Machine Learning

Checkout the various job openings in Data Science and Machine learning, click here..

Vskills Internship in Data Science and Machine Learning

Vskills runs its flagship internship program where bright interns work with academic council, click to know more details..

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TABLE OF CONTENT

Introduction to the Course

  • Introduction to Courses and Instructor

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

  • Introduction - Part 1
  • Introduction - Part 2
  • Basics of Programming: Understanding the Algorithm
  • Basics of Programming: FlowCharts and Pseudocodes
  • Basics of Programming: Example of Algorithms - Making Tea Problem
  • Basics of Programming: Example of Algorithms-Searching Minimum
  • Basics of Programming: Example of Algorithms-Sorting Problem
  • Basics of Programming: Sorting Problem in Python
  • Why Python and Jupyter Notebook: Why Python
  • Why Python and Jupyter Notebook: Why Jupyter Notebooks
  • Installation of Anaconda and IPython Shell: Installing Python and Jupyter Anaconda
  • Installation of Anaconda and IPython Shell: Your First Python Code - Hello World
  • Installation of Anaconda and IPython Shell: Coding in IPython Shell
  • Variable and Operator: Variables
  • Variable and Operator: Operators
  • Variable and Operator: Variable Name Quiz
  • Variable and Operator: Bool Data Type in Python
  • Variable and Operator: Comparison in Python
  • Variable and Operator: Combining Comparisons in Python
  • Variable and Operator: Combining Comparisons Quiz
  • Python Useful function: Python Function- Round
  • Python Useful function: Python Function- Divmod
  • Python Useful function: Python Function- Is instance and PowFunctions
  • Python Useful function: Python Function- Input
  • Control Flow in Python: If Python Condition
  • Control Flow in Python: if Elif Else Python Conditions
  • Control Flow in Python: More on if Elif Else Python Conditions
  • Control Flow in Python: Indentations
  • Control Flow in Python: Comments and Problem-Solving Practice with If
  • Control Flow in Python: While Loop
  • Control Flow in Python: While Loop Break Continue
  • Control Flow in Python: For Loop
  • Control Flow in Python: Else in For Loop
  • Control Flow in Python: Loops Practice-Sorting Problem
  • Function and Module in Python: Functions in Python
  • Function and Module in Python: DocString
  • Function and Module in Python: Input Arguments
  • Function and Module in Python: Multiple Input Arguments
  • Function and Module in Python: Ordering Multiple Input Arguments
  • Function and Module in Python: Output Arguments and Return Statement
  • Function and Module in Python: Function Practice-Output Arguments and Return Statement
  • Function and Module in Python: Variable Number of Input Arguments
  • Function and Module in Python: Variable Number of Input Arguments as Dictionary
  • Function and Module in Python: Default Values in Python
  • Function and Module in Python: Modules in Python
  • Function and Module in Python: Making Modules in Python
  • Function and Module in Python: Function Practice-Sorting List in Python
  • String in Python: Strings
  • String in Python: Multi-Line Strings
  • String in Python: Indexing Strings
  • String in Python: String Methods
  • String in Python: String Escape Sequences
  • Data Structure (List, Tuple, Set, Dictionary): Introduction to Data Structure
  • Data Structure (List, Tuple, Set, Dictionary): Defining and Indexing
  • Data Structure (List, Tuple, Set, Dictionary): Insertion and Deletion
  • Data Structure (List, Tuple, Set, Dictionary): Python Practice-Insertion and Deletion
  • Data Structure (List, Tuple, Set, Dictionary): Deep Copy or Reference Slicing
  • Data Structure (List, Tuple, Set, Dictionary): Exploring Methods Using TAB Completion
  • Data Structure (List, Tuple, Set, Dictionary): Data Structure Abstract Ways
  • Data Structure (List, Tuple, Set, Dictionary): Data Structure Practice
  • NumPy for Numerical Data Processing: Introduction to NumPy
  • NumPy for Numerical Data Processing: NumPy Dimensions
  • NumPy for Numerical Data Processing: NumPy Shape, Size, and Bytes
  • NumPy for Numerical Data Processing: Arrange, Random, and Reshape-Part 1
  • NumPy for Numerical Data Processing: Arrange, Random, and Reshape-Part 2
  • NumPy for Numerical Data Processing: Slicing-Part 1
  • NumPy for Numerical Data Processing: Slicing-Part 2
  • NumPy for Numerical Data Processing: NumPy Masking
  • NumPy for Numerical Data Processing: NumPy BroadCasting and Concatenation
  • NumPy for Numerical Data Processing: NumPy ufuncs Speed Test
  • Pandas for Data Manipulation: Introduction to Pandas
  • Pandas for Data Manipulation: Pandas Series
  • Pandas for Data Manipulation: Pandas Data Frame
  • Pandas for Data Manipulation: Pandas Missing Values
  • Pandas for Data Manipulation: Pandas .loc and .iloc
  • Pandas for Data Manipulation: Pandas Practice-Using COVID19 Data - Part 1
  • Pandas for Data Manipulation: Pandas Practice-Using COVID19 Data - Part 2
  • Matplotlib, Seaborn, and Bokeh for Data Visualization: Introduction to Matplotlib
  • Matplotlib, Seaborn, and Bokeh for Data Visualization: Seaborn Versus Matplotlib Style
  • Matplotlib, Seaborn, and Bokeh for Data Visualization: Histograms Kdeplot
  • Matplotlib, Seaborn, and Bokeh for Data Visualization: Seaborn Pairplot and Jointplot
  • Matplotlib, Seaborn, and Bokeh for Data Visualization: Seaborn Pairplot using Iris Data
  • Matplotlib, Seaborn, and Bokeh for Data Visualization: Introduction to Bokeh
  • Matplotlib, Seaborn, and Bokeh for Data Visualization: Bokeh Gridplot
  • Scikit-Learn for Machine Learning: Introduction to Scikit-Learn
  • Scikit-Learn for Machine Learning: Scikit-Learn for Linear Regression
  • Scikit-Learn for Machine Learning: Scikit-Learn for SVM and Random Forests
  • Scikit-Learn for Machine Learning: Scikit-Learn - Trend Analysis COVID19

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

  • Introduction
  • What We will Learn
  • NumPy for Numerical Data Processing: Ufuncs Add, Sum, and Plus Operators
  • NumPy for Numerical Data Processing: Ufuncs Subtract Power Mod
  • NumPy for Numerical Data Processing: Ufuncs Comparisons Logical Operators
  • NumPy for Numerical Data Processing: Ufuncs Comparisons Logical Operators Quiz
  • NumPy for Numerical Data Processing: Ufuncs Comparisons Logical Operators Solution
  • NumPy for Numerical Data Processing: Ufuncs Output Argument
  • NumPy for Numerical Data Processing: NumPy Playing with Images
  • NumPy for Numerical Data Processing: NumPy Playing with Images Quiz
  • NumPy for Numerical Data Processing: NumPy Playing with Images Solution
  • NumPy for Numerical Data Processing: NumPy KNN Classifier from Scratch
  • NumPy for Numerical Data Processing: NumPy Structured Arrays
  • NumPy for Numerical Data Processing: NumPy Structured Arrays Quiz
  • NumPy for Numerical Data Processing: NumPy Structured Arrays Solution
  • Pandas for Data Manipulation and Understanding: Introduction to Pandas
  • Pandas for Data Manipulation and Understanding: Pandas Series
  • Pandas for Data Manipulation and Understanding: Pandas DataFrame
  • Pandas for Data Manipulation and Understanding: Pandas DataFrame Quiz
  • Pandas for Data Manipulation and Understanding: Pandas DataFrame Solution
  • Pandas for Data Manipulation and Understanding: Pandas Missing Values
  • Pandas for Data Manipulation and Understanding: Pandas Loc Iloc
  • Pandas for Data Manipulation and Understanding: Pandas in Practice
  • Pandas for Data Manipulation and Understanding: Pandas Group By
  • Pandas for Data Manipulation and Understanding: Pandas Group By Quiz
  • Pandas for Data Manipulation and Understanding: Pandas Group by Solution
  • Pandas for Data Manipulation and Understanding: Hierarchical Indexing
  • Pandas for Data Manipulation and Understanding: Pandas Rolling
  • Pandas for Data Manipulation and Understanding: Pandas Rolling Quiz
  • Pandas for Data Manipulation and Understanding: Pandas Rolling Solution
  • Pandas for Data Manipulation and Understanding: Pandas Where
  • Pandas for Data Manipulation and Understanding: Pandas Clip
  • Pandas for Data Manipulation and Understanding: Pandas Clip Quiz
  • Pandas for Data Manipulation and Understanding: Pandas Clip Solution
  • Pandas for Data Manipulation and Understanding: Pandas Merge
  • Pandas for Data Manipulation and Understanding: Pandas Merge Quiz
  • Pandas for Data Manipulation and Understanding: Pandas Merge Solution
  • Pandas for Data Manipulation and Understanding: Pandas Pivot Table
  • Pandas for Data Manipulation and Understanding: Pandas Strings
  • Pandas for Data Manipulation and Understanding: Pandas DateTime
  • Pandas for Data Manipulation and Understanding: Pandas Hands on COVID19 Data
  • Pandas for Data Manipulation and Understanding: Pandas Hands on COVID19 Data Bug
  • Matplotlib for Data Visualization: Introduction to Matplotlib
  • Matplotlib for Data Visualization: Matplotlib Multiple Plots
  • Matplotlib for Data Visualization: Matplotlib Colors and Styles
  • Matplotlib for Data Visualization: Matplotlib Colors and Styles Quiz
  • Matplotlib for Data Visualization: Matplotlib Colors and Styles Solution
  • Matplotlib for Data Visualization: Matplotlib Colors and Styles Shortcuts
  • Matplotlib for Data Visualization: Matplotlib Axis Limits
  • Matplotlib for Data Visualization: Matplotlib Axis Limits Quiz
  • Matplotlib for Data Visualization: Matplotlib Axis Limits Solution
  • Matplotlib for Data Visualization: Matplotlib Legends Labels
  • Matplotlib for Data Visualization: Matplotlib Set Function
  • Matplotlib for Data Visualization: Matplotlib Set Function Quiz
  • Matplotlib for Data Visualization: Matplotlib Set Function Solution
  • Matplotlib for Data Visualization: Matplotlib Markers
  • Matplotlib for Data Visualization: Matplotlib Markers Randomplots
  • Matplotlib for Data Visualization: Matplotlib Scatter Plot
  • Matplotlib for Data Visualization: Matplotlib Contour Plot
  • Matplotlib for Data Visualization: Matplotlib Contour Plot Quiz
  • Matplotlib for Data Visualization: Matplotlib Contour Plot Solution
  • Matplotlib for Data Visualization: Matplotlib Histograms
  • Matplotlib for Data Visualization: Matplotlib Subplots
  • Matplotlib for Data Visualization: Matplotlib Subplots Quiz
  • Matplotlib for Data Visualization: Matplotlib Subplots Solution
  • Matplotlib for Data Visualization: Matplotlib 3D Introduction
  • Matplotlib for Data Visualization: Matplotlib 3D Scatter Plots
  • Matplotlib for Data Visualization: Matplotlib 3D Scatter Plots Quiz
  • Matplotlib for Data Visualization: Matplotlib 3D Scatter Plots Solution
  • Matplotlib for Data Visualization: Matplotlib 3D Surface Plots
  • Seaborn for Data Visualization: Introduction to Seaborn
  • Seaborn for Data Visualization: Seaborn Relplot
  • Seaborn for Data Visualization: Seaborn Relplot Quiz
  • Seaborn for Data Visualization: Seaborn Relplot Solution
  • Seaborn for Data Visualization: Seaborn Relplot Kind Line
  • Seaborn for Data Visualization: Seaborn Relplot Facets
  • Seaborn for Data Visualization: Seaborn Relplot Facets Quiz
  • Seaborn for Data Visualization: Seaborn Relplot Facets Solution
  • Seaborn for Data Visualization: Seaborn Catplot
  • Seaborn for Data Visualization: Seaborn Heatmaps
  • Bokeh for Interactive Plotting: Introduction to Bokeh
  • Bokeh for Interactive Plotting: Bokeh Multiplots Markers
  • Bokeh for Interactive Plotting: Bokeh Multiplots Grid Plot
  • Bokeh for Interactive Plotting: Bokeh Multiplots Grid Plot Quiz
  • Bokeh for Interactive Plotting: Bokeh Multiplots Grid Plot Solution
  • Plotly for 3D Interactive Plotting: Plotly 3D Interactive Scatter Plot
  • Plotly for 3D Interactive Plotting: Plotly 3D Interactive Scatter Plot Quiz
  • Plotly for 3D Interactive Plotting: Plotly 3D Interactive Scatter Plot Solution
  • Plotly for 3D Interactive Plotting: Plotly 3D Interactive Surface Plot
  • Plotly for 3D Interactive Plotting: Plotly 3D Interactive Surface Plot Quiz
  • Plotly for 3D Interactive Plotting: Plotly 3D Interactive Surface Plot Solution
  • Geographic Maps with Folium: Geographic Maps with Folium using COVID-19 Data
  • Geographic Maps with Folium: Geographic Maps with Folium using COVID-19 Data Quiz
  • Geographic Maps with Folium: Geographic Maps with Folium using COVID-19 Data Solution
  • Pandas for Plotting: Pandas for Plotting

Basics for Data Science: Mastering Probability and Statistics in Python

  • Introduction
  • Probability Versus Statistics
  • Sets: Definition of Set
  • Sets: Definition of Set Exercise 01
  • Sets: Definition of Set Solution 01
  • Sets: Definition of Set Exercise 02
  • Sets: Definition of Set Solution 02
  • Sets: Cardinality of a Set
  • Sets: Subsets PowerSet UniversalSet
  • Sets: Python Practice Subsets
  • Sets: PowerSets Solution
  • Sets: Operations
  • Sets: Operations Exercise 01
  • Sets: Operations Solution 01
  • Sets: Operations Exercise 02
  • Sets: Operations Solution 02
  • Sets: Operations Exercise 03
  • Sets: Operations Solution 03
  • Sets: Python Practice Operations
  • Sets: Venn Diagrams Operations
  • Sets: Homework
  • Experiment: Random Experiment
  • Experiment: Outcome and Sample Space
  • Experiment: Outcome and Sample Space Exercise 01
  • Experiment: Outcome and Sample Space Solution 01
  • Experiment: Event
  • Experiment: Event Exercise 01
  • Experiment: Event Solution 01
  • Experiment: Event Exercise 02
  • Experiment: Event Solution 02
  • Experiment: Recap and Homework
  • Probability Model: Probability Model
  • Probability Model: Probability Axioms
  • Probability Model: Probability Axioms Derivations
  • Probability Model: Probability Axioms Derivations Exercise 01
  • Probability Model: Probability Axioms Derivations Solution 01
  • Probability Model: Probability Models Example
  • Probability Model: Probability Models More Examples
  • Probability Model: Probability Models Continuous
  • Probability Model: Conditional Probability
  • Probability Model: Conditional Probability Example
  • Probability Model: Conditional Probability Formula
  • Probability Model: Conditional Probability in Machine Learning
  • Probability Model: Conditional Probability Total Probability Theorem
  • Probability Model: Probability Models Independence
  • Probability Model: Probability Models Conditional Independence
  • Probability Model: Probability Models Conditional Independence Exercise 01
  • Probability Model: Probability Models Conditional Independence Solution 01
  • Probability Model: Probability Models BayesRule
  • Probability Model: Probability Models towards Random Variables
  • Probability Model: Homework
  • Random Variables: Introduction
  • Random Variables: Random Variables Examples
  • Random Variables: Random Variables Examples Exercise 01
  • Random Variables: Random Variables Examples Solution 01
  • Random Variables: Bernulli Random Variables
  • Random Variables: Bernulli Trail Python Practice
  • Random Variables: Bernulli Trail Python Practice Exercise 01
  • Random Variables: Bernulli Trail Python Practice Solution 01
  • Random Variables: Geometric Random Variable
  • Random Variables: Geometric Random Variable Normalization Proof Optional
  • Random Variables: Geometric Random Variable Python Practice
  • Random Variables: Binomial Random Variables
  • Random Variables: Binomial Python Practice
  • Random Variables: Random Variables in Real Datasets
  • Random Variables: Random Variables in Real Datasets Exercise 01
  • Random Variables: Random Variables in Real Datasets Solution 01
  • Random Variables: Homework
  • Continuous Random Variables: Zero Probability to Individual Values
  • Continuous Random Variables: Zero Probability to Individual Values Exercise 01
  • Continuous Random Variables: Zero Probability to Individual Values Solution 01
  • Continuous Random Variables: Probability Density Functions
  • Continuous Random Variables: Probability Density Functions Exercise 01
  • Continuous Random Variables: Probability Density Functions Solution 01
  • Continuous Random Variables: Uniform Distribution
  • Continuous Random Variables: Uniform Distribution Exercise 01
  • Continuous Random Variables: Uniform Distribution Solution 01
  • Continuous Random Variables: Uniform Distribution Python
  • Continuous Random Variables: Exponential
  • Continuous Random Variables: Exponential Exercise 01
  • Continuous Random Variables: Exponential Solution 01
  • Continuous Random Variables: Exponential Python
  • Continuous Random Variables: Gaussian Random Variables
  • Continuous Random Variables: Gaussian Random Variables Exercise 01
  • Continuous Random Variables: Gaussian Random Variables Solution 01
  • Continuous Random Variables: Gaussian Python
  • Continuous Random Variables: Transformation of Random Variables
  • Continuous Random Variables: Homework
  • Expectations: Definition
  • Expectations: Sample Mean
  • Expectations: Law of Large Numbers
  • Expectations: Law of Large Numbers Famous Distributions
  • Expectations: Law of Large Numbers Famous Distributions Python
  • Expectations: Variance
  • Expectations: Homework
  • Project Bayes Classifier: Project Bayes Classifier from Scratch
  • Multiple Random Variables: Joint Distributions
  • Multiple Random Variables: Joint Distributions Exercise 01
  • Multiple Random Variables: Joint Distributions Solution 01
  • Multiple Random Variables: Joint Distributions Exercise 02
  • Multiple Random Variables: Joint Distributions Solution 02
  • Multiple Random Variables: Joint Distributions Exercise 03
  • Multiple Random Variables: Joint Distributions Solution 03
  • Multiple Random Variables: Multivariate Gaussian
  • Multiple Random Variables: Conditioning Independence
  • Multiple Random Variables: Classification
  • Multiple Random Variables: Naive Bayes Classification
  • Multiple Random Variables: Regression
  • Multiple Random Variables: Curse of Dimensionality
  • Multiple Random Variables: Homework
  • Optional Estimation: Parametric Distributions
  • Optional Estimation: MLE
  • Optional Estimation: Loglikelihood
  • Optional Estimation: MAP
  • Optional Estimation: Logistic Regression
  • Optional Estimation: Ridge Regression
  • Optional Estimation: DNN
  • Mathematical Derivations for Math Lovers (Optional): Permutations
  • Mathematical Derivations for Math Lovers (Optional): Combinations
  • Mathematical Derivations for Math Lovers (Optional): Binomial Random Variable
  • Mathematical Derivations for Math Lovers (Optional): Logistic Regression Formulation
  • Mathematical Derivations for Math Lovers (Optional): Logistic Regression Derivation

Machine Learning: Machine Learning Crash Course

  • Introduction
  • Introduction: Python Practical of the Course
  • Why Machine Learning: Machine Learning Applications-Part 1
  • Why Machine Learning: Machine Learning Applications-Part 2
  • Why Machine Learning: Why Machine Learning is Trending Now
  • Process of Learning from Data: Supervised Learning
  • Process of Learning from Data: Unsupervised Learning and Reinforcement Learning
  • Machine Learning Methods: Features
  • Machine Learning Methods: Features Practice with Python
  • Machine Learning Methods: Regression
  • Machine Learning Methods: Regression Practice with Python
  • Machine Learning Methods: Classification
  • Machine Learning Methods: Classification Practice with Python
  • Machine Learning Methods: Clustering
  • Machine Learning Methods: Clustering Practice with Python
  • Data Preparation and Pre-processing: Handling Image Data
  • Data Preparation and Preprocessing: Handling Video and Audio Data
  • Data Preparation and Preprocessing: Handling Text Data
  • Data Preparation and Preprocessing: One Hot Encoding
  • Data Preparation and Preprocessing: Data Standardization
  • Machine Learning Models and Optimization: Machine Learning Model 1
  • Machine Learning Models and Optimization: Machine Learning Model 2
  • Machine Learning Models and Optimization: Machine Learning Model 3
  • Machine Learning Models and Optimization: Training Process, Error, Cost and Loss
  • Machine Learning Models and Optimization: Optimization
  • Building Machine Learning Model from Scratch: Linear Regression from Scratch- Part 1
  • Building Machine Learning Model from Scratch: Linear Regression from Scratch- Part 2
  • Building Machine Learning Model from Scratch: Minimum-to-mean Distance Classifier from Scratch- Part 1
  • Building Machine Learning Model from Scratch: Minimum-to-mean Distance Classifier from Scratch- Part 2
  • Building Machine Learning Model from Scratch: K-Means Clustering from Scratch- Part 1
  • Building Machine Learning Model from Scratch: K-Means Clustering from Scratch- Part 2
  • Overfitting, Underfitting, and Generalization: Overfitting Introduction
  • Overfitting, Underfitting, and Generalization: Overfitting Example in Python
  • Overfitting, Underfitting, and Generalization: Regularization
  • Overfitting, Underfitting, and Generalization: Generalization
  • Overfitting, Underfitting, and Generalization: Data Snooping and the Test Set
  • Overfitting, Underfitting and Generalization: Cross-validation
  • Machine Learning Model Performance Metrics: The Accuracy
  • Machine Learning Model Performance Metrics: The Confusion Matrix
  • Dimensionality Reduction: The Curse of Dimensionality
  • Dimensionality Reduction: The Principal Component Analysis (PCA)
  • Deep Learning Overview: Introduction to Deep Neural Networks (DNN)
  • Deep Learning Overview: Introduction to Convolutional Neural Networks (CNN)
  • Deep Learning Overview: Introduction to Recurrent Neural Networks (CNN)
  • Hands-on Machine Learning Project Using Scikit-Learn: Principal Component Analysis (PCA) with Python
  • Hands-on Machine Learning Project Using Scikit-Learn: Pipeline in Scikit-Learn for Machine Learning Project
  • Hands-on Machine Learning Project Using Scikit-Learn: Cross-validation with Python
  • Hands-on Machine Learning Project Using Scikit-Learn: Face Recognition Project with Python
  • OPTIONAL Section- Mathematics Wrap-Up: Mathematical Wrap-Up on Machine Learning

Machine Learning: Feature Engineering and Dimensionality Reduction with Python

  • Introduction
  • Features in Data Science: Introduction to Feature in Data Science
  • Features in Data Science: Marking Facial Features
  • Features in Data Science: Feature Space
  • Features in Data Science: Features Dimensions
  • Features in Data Science: Features Dimensions Activity
  • Features in Data Science: Why Dimensionality Reduction
  • Features in Data Science: Activity-Dimensionality Reduction
  • Features in Data Science: Feature Dimensionality Reduction Methods
  • Feature Selection: Why Feature Selection
  • Feature Selection: Feature Selection Methods
  • Feature Selection: Filter Methods
  • Feature Selection: Wrapper Methods
  • Feature Selection: Embedded Methods
  • Feature Selection: Search Strategy
  • Feature Selection: Search Strategy Activity
  • Feature Selection: Statistical Based Methods
  • Feature Selection: Information Theoretic Methods
  • Feature Selection: Similarity Based Methods Introduction
  • Feature Selection: Similarity Based Methods Criteria
  • Feature Selection: Activity- Feature Selection in Python
  • Feature Selection: Activity- Feature Selection
  • Mathematical Foundation: Introduction to Mathematical Foundation of Feature Selection
  • Mathematical Foundation: Closure of a Set
  • Mathematical Foundation: Linear Combinations
  • Mathematical Foundation: Linear Independence
  • Mathematical Foundation: Vector Space
  • Mathematical Foundation: Basis and Dimensions
  • Mathematical Foundation: Coordinates Versus Dimensions
  • Mathematical Foundation: SubSpace
  • Mathematical Foundation: Orthonormal Basis
  • Mathematical Foundation: Matrix Product
  • Mathematical Foundation: Least Squares
  • Mathematical Foundation: Rank
  • Mathematical Foundation: Eigen Space
  • Mathematical Foundation: Positive Semi Definite Matrix
  • Mathematical Foundation: Singular Value Decomposition (SVD)
  • Mathematical Foundation: Lagrange Multipliers
  • Mathematical Foundation: Vector Derivatives
  • Mathematical Foundation: Linear Algebra Module Python
  • Mathematical Foundation: Activity-Linear Algebra Module Python
  • Feature Extraction: Feature Extraction Introduction
  • Feature Extraction: PCA Introduction
  • Feature Extraction: PCA Criteria
  • Feature Extraction: PCA Properties
  • Feature Extraction: PCA Max Variance Formulation
  • Feature Extraction: PCA Derivation
  • Feature Extraction: PCA Implementation
  • Feature Extraction: PCA For Small Sample Size Problems(DualPCA)
  • Feature Extraction: PCA Versus SVD
  • Feature Extraction: Kernel PCA
  • Feature Extraction: Kernel PCA Versus ISOMAP
  • Feature Extraction: Kernel PCA Versus the Rest
  • Feature Extraction: Encoder Decoder Networks for Dimensionality Reduction Versus Kernel PCA
  • Feature Extraction: Supervised PCA and Fishers Linear Discriminant Analysis
  • Feature Extraction: Supervised PCA and Fishers Linear Discriminant Analysis Activity
  • Feature Extraction: Dimensionality Reduction Pipelines Python Project
  • Feature Engineering: Categorical Features
  • Feature Engineering: Categorical Features Python
  • Feature Engineering: Text Features
  • Feature Engineering: Image Features
  • Feature Engineering: Derived Features
  • Feature Engineering: Derived Features Histogram of Gradients Local Binary Patterns
  • Feature Engineering: Feature Scaling
  • Feature Engineering: Activity-Feature Scaling

Deep learning: Artificial Neural Networks with Python

  • Introduction
  • Introduction to Machine Learning: Introduction to Machine Learning
  • Introduction to Machine Learning: Classification
  • Introduction to Machine Learning: Classification Exercise
  • Introduction to Machine Learning: Classification Solution
  • Introduction to Machine Learning: Classification Training Process and Prediction Probabilities
  • Introduction to Machine Learning: Classification Prediction Probabilities Exercise
  • Introduction to Machine Learning: Classification Prediction Probabilities Exercise Solution
  • Introduction to Machine Learning: Regression
  • Introduction to Machine Learning: Regression Exercise
  • Introduction to Machine Learning: Regression Exercise Solution
  • Introduction to Machine Learning: Supervised Learning
  • Introduction to Machine Learning: Unsupervised Learning
  • Introduction to Machine Learning: Reinforcement Learning
  • Introduction to Machine Learning: Machine Learning Model
  • Introduction to Machine Learning: Machine Learning Model Example
  • Introduction to Machine Learning: Machine Learning Model Exercise
  • Introduction to Machine Learning: Machine Learning Model Exercise Solution
  • Introduction to Machine Learning: Machine Learning Model Types
  • Introduction to Machine Learning: Machine Learning Model Linearity
  • Introduction to Machine Learning: Machine Learning Model Linearity Exercise
  • Introduction to Machine Learning: Machine Learning Model Linearity Exercise Solution
  • Introduction to Machine Learning: Machine Learning Model Multi Target Models
  • Introduction to Machine Learning: Machine Learning Model Multi Target Models Exercise
  • Introduction to Machine Learning: Machine Learning Model Multi Target Models Exercise Solution
  • Introduction to Machine Learning: Machine Learning Model Training Exercise
  • Introduction to Machine Learning: Machine Learning Model Training Exercise Solution
  • Introduction to Machine Learning: Machine Learning Model Training Loss
  • Introduction to Machine Learning: Machine Learning Model Hyperparameters Exercise
  • Introduction to Machine Learning: Machine Learning Model Hyperparameters Exercise Solution
  • Introduction to Machine Learning: Machine Learning Occam's Razor
  • Introduction to Machine Learning: Machine Learning Overfitting
  • Introduction to Machine Learning: Machine Learning Overfitting Exercise
  • Introduction to Machine Learning: Machine Learning Overfitting Exercise Solution Regularization
  • Introduction to Machine Learning: Machine Learning Overfitting Generalization
  • Introduction to Machine Learning: Machine Learning Data Snooping
  • Introduction to Machine Learning: Machine Learning Cross Validation
  • Introduction to Machine Learning: Machine Learning Hyperparameter Tunning Exercise
  • Introduction to Machine Learning: Machine Learning Hyperparameter Tunning Exercise Solution
  • DNN and Deep Learning Basics: Why PyTorch
  • DNN and Deep Learning Basics: PyTorch Installation and Tensors Introduction
  • DNN and Deep Learning Basics: Automatic Differentiation PyTorch New
  • DNN and Deep Learning Basics: Why DNNs in Machine Learning
  • DNN and Deep Learning Basics: Representational Power and Data Utilization Capacity of DNN
  • DNN and Deep Learning Basics: Perceptron
  • DNN and Deep Learning Basics: Perceptron Exercise
  • DNN and Deep Learning Basics: Perceptron Exercise Solution
  • DNN and Deep Learning Basics: Perceptron Implementation
  • DNN and Deep Learning Basics: DNN Architecture
  • DNN and Deep Learning Basics: DNN Architecture Exercise
  • DNN and Deep Learning Basics: DNN Architecture Exercise Solution
  • DNN and Deep Learning Basics: DNN ForwardStep Implementation
  • DNN and Deep Learning Basics: DNN Why Activation Function Is Required
  • DNN and Deep Learning Basics: DNN Why Activation Function Is Required Exercise
  • DNN and Deep Learning Basics: DNN Why Activation Function Is Required Exercise Solution
  • DNN and Deep Learning Basics: DNN Properties of Activation Function
  • DNN and Deep Learning Basics: DNN Activation Functions in PyTorch
  • DNN and Deep Learning Basics: DNN What is Loss Function
  • DNN and Deep Learning Basics: DNN What is Loss Function Exercise
  • DNN and Deep Learning Basics: DNN What is Loss Function Exercise Solution
  • DNN and Deep Learning Basics: DNN What is Loss Function Exercise 02
  • DNN and Deep Learning Basics: DNN What is Loss Function Exercise 02 Solution
  • DNN and Deep Learning Basics: DNN Loss Function in PyTorch
  • DNN and Deep Learning Basics: DNN Gradient Descent
  • DNN and Deep Learning Basics: DNN Gradient Descent Exercise
  • DNN and Deep Learning Basics: DNN Gradient Descent Exercise Solution
  • DNN and Deep Learning Basics: DNN Gradient Descent Implementation
  • DNN and Deep Learning Basics: DNN Gradient Descent Stochastic Batch Minibatch
  • DNN and Deep Learning Basics: DNN Gradient Descent Summary
  • DNN and Deep Learning Basics: DNN Implementation Gradient Step
  • DNN and Deep Learning Basics: DNN Implementation Stochastic Gradient Descent
  • DNN and Deep Learning Basics: DNN Implementation Batch Gradient Descent
  • DNN and Deep Learning Basics: DNN Implementation Minibatch Gradient Descent
  • DNN and Deep Learning Basics: DNN Implementation in PyTorch
  • DNN and Deep Learning Basics: DNN Weights Initializations
  • DNN and Deep Learning Basics: DNN Learning Rate
  • DNN and Deep Learning Basics: DNN Batch Normalization
  • DNN and Deep Learning Basics: DNN Batch Normalization Implementation
  • DNN and Deep Learning Basics: DNN Optimizations
  • DNN and Deep Learning Basics: DNN Dropout
  • DNN and Deep Learning Basics: DNN Dropout in PyTorch
  • DNN and Deep Learning Basics: DNN Early Stopping
  • DNN and Deep Learning Basics: DNN Hyperparameters
  • DNN and Deep Learning Basics: DNN PyTorch CIFAR10 Example
  • Deep Neural Networks and Deep Learning Basics: Introduction to Artificial Neural Networks
  • Deep Neural Networks and Deep Learning Basics: Neuron and Perceptron
  • Deep Neural Networks and Deep Learning Basics: Deep Neural Network Architecture
  • Deep Neural Networks and Deep Learning Basics: Feedforward Fully Connected MLP
  • Deep Neural Networks and Deep Learning Basics: Calculating Number of Weights of DNN
  • Deep Neural Networks and Deep Learning Basics: Number of Neurons Versus Number of Layers
  • Deep Neural Networks and Deep Learning Basics: Discriminative Versus Generative Learning
  • Deep Neural Networks and Deep Learning Basics: Universal Approximation Theorem
  • Deep Neural Networks and Deep Learning Basics: Why Depth
  • Deep Neural Networks and Deep Learning Basics: Decision Boundary in DNN
  • Deep Neural Networks and Deep Learning Basics: Bias Term
  • Deep Neural Networks and Deep Learning Basics: The Activation Function
  • Deep Neural Networks and Deep Learning Basics: DNN Training Parameters
  • Deep Neural Networks and Deep Learning Basics: Gradient Descent
  • Deep Neural Networks and Deep Learning Basics: Backpropagation
  • Deep Neural Networks and Deep Learning Basics: Training DNN Animation
  • Deep Neural Networks and Deep Learning Basics: Weight Initialization
  • Deep Neural Networks and Deep Learning Basics: Batch Minibatch Stochastic
  • Deep Neural Networks and Deep Learning Basics: Batch Normalization
  • Deep Neural Networks and Deep Learning Basics: Rprop Momentum
  • Deep Neural Networks and Deep Learning Basics: convergence Animation
  • Deep Neural Networks and Deep Learning Basics: Drop Out Early Stopping Hyperparameters
  • Python for Data Science: Python Packages for Data Science
  • Python for Data Science: NumPy Pandas and Matplotlib (Part 1)
  • Python for Data Science: NumPy Pandas and Matplotlib (Part 2)
  • Python for Data Science: NumPy Pandas and Matplotlib (Part 3)
  • Python for Data Science: NumPy Pandas and Matplotlib (Part 4)
  • Python for Data Science: NumPy Pandas and Matplotlib (Part 5)
  • Python for Data Science: NumPy Pandas and Matplotlib (Part 6)
  • Python for Data Science: Dataset Preprocessing
  • Python for Data Science: TensorFlow for classification
  • Implementation of DNN for COVID 19 Analysis: COVID19 Data Analysis
  • Implementation of DNN for COVID 19 Analysis: COVID19 Regression with TensorFlow

Deep learning: Convolutional Neural Networks with Python

  • Introduction: Why CNN
  • Introduction: Focus of the Course
  • Image Processing: Grayscale Images
  • Image Processing: RGB Images
  • Image Processing: Reading and Showing Images in Python
  • Image Processing: Converting an Image to Grayscale in Python
  • Image Processing: Image Formation
  • Image Processing: Image Blurring 1
  • Image Processing: Image Blurring 2
  • Image Processing: General Image Filtering
  • Image Processing: Convolution
  • Image Processing: Edge Detection
  • Image Processing: Image Sharpening
  • Image Processing: Implementation of Image Blurring Edge Detection Image Sharpening in Python
  • Image Processing: Parametric Shape Detection
  • Image Processing: Image Processing Activity
  • Object Detection: Introduction to Object Detection
  • Object Detection: Classification Pipeline
  • Object Detection: Sliding Window Implementation
  • Object Detection: Shift Scale Rotation Invariance
  • Object Detection: Person Detection
  • Object Detection: HOG Features
  • Object Detection: Hand Engineering Versus CNNs
  • Object Detection: Object Detection Activity
  • Deep Neural Network Architecture: Convolution Revisited
  • Deep Neural Network Architecture: Implementing Convolution in Python Revisited
  • Deep Neural Network Architecture: Why Convolution
  • Deep Neural Network Architecture: Filters Padding Strides
  • Deep Neural Network Architecture: Pooling Tensors
  • Deep Neural Network Architecture: CNN Example
  • Deep Neural Network Architecture: Convolution and Pooling Details
  • Deep Neural Network Architecture: Nonvectorized Implementations of Conv2d and Pool2d
  • Deep Neural Network Architecture Activity
  • Gradient Descent in CNNs: Example Setup
  • Gradient Descent in CNNs: Why Derivatives
  • Gradient Descent in CNNs: What is Chain Rule
  • Gradient Descent in CNNs: Applying Chain Rule
  • Gradient Descent in CNNs: Gradients of Convolutional Layer
  • Gradient Descent in CNNs: Extending to Multiple Filters
  • Gradient Descent in CNNs: Gradients of MaxPooling Layer
  • Gradient Descent in CNNs: Extending to Multiple Layers
  • Gradient Descent in CNNs: Implementation in NumPy ForwardPass.mp4.
  • Gradient Descent in CNNs: Implementation in NumPy BackwardPass 1
  • Gradient Descent in CNNs: Implementation in NumPy BackwardPass 2
  • Gradient Descent in CNNs: Implementation in NumPy BackwardPass 3
  • Gradient Descent in CNNs: Implementation in NumPy BackwardPass 4
  • Gradient Descent in CNNs: Implementation in NumPy BackwardPass 5
  • Gradient Descent in CNNs: Gradient Descent in CNNs Activity
  • Introduction to TensorFlow: Introduction
  • Introduction to TensorFlow: FashionMNIST Example Plan Neural Network
  • Introduction to TensorFlow: FashionMNIST Example CNN
  • Introduction to TensorFlow: Introduction to TensorFlow Activity
  • Classical CNNs: LeNet
  • Classical CNNs: AlexNet
  • Classical CNNs: VGG
  • Classical CNNs: InceptionNet
  • Classical CNNs: Google Net
  • Classical CNNs: Resnet
  • Classical CNNs: Classical CNNs Activity
  • Transfer Learning: What is Transfer learning
  • Transfer Learning: Why Transfer Learning
  • Transfer Learning: ImageNet Challenge
  • Transfer Learning: Practical Tips
  • Transfer Learning: Project in TensorFlow
  • Transfer Learning: Transfer Learning Activity
  • Yolo: Image Classification Revisited
  • Yolo: Sliding Window Object Localization
  • Yolo: Sliding Window Efficient Implementation
  • Yolo: Yolo Introduction
  • Yolo: Yolo Training Data Generation
  • Yolo: Yolo Anchor Boxes
  • Yolo: Yolo Algorithm
  • Yolo: Yolo Non-Maxima Suppression
  • Yolo: RCNN
  • Yolo: Yolo Activity
  • Face Verification: Problem Setup
  • Face Verification: Project Implementation
  • Face Verification: Face Verification Activity
  • Neural Style Transfer: Problem Setup
  • Neural Style Transfer: Implementation TensorFlow Hub

Deep learning: Recurrent Neural Networks with Python

  • Introduction
  • Applications of RNN (Motivation): Human Activity Recognition
  • Applications of RNN (Motivation): Image Captioning
  • Applications of RNN (Motivation): Machine Translation
  • Applications of RNN (Motivation): Speech Recognition
  • Applications of RNN (Motivation): Stock Price Predictions
  • Applications of RNN (Motivation): When to Model RNN
  • Applications of RNN (Motivation): Activity
  • RNN Architecture: Introduction to Module
  • RNN Architecture: Fixed Length Memory Model
  • RNN Architecture: Fixed Length Memory Model Exercise
  • RNN Architecture: Fixed Length Memory Model Exercise Solution Part 01
  • RNN Architecture: Fixed Length Memory Model Exercise Solution Part 02
  • RNN Architecture: Infinite Memory Architecture
  • RNN Architecture: Infinite Memory Architecture Exercise
  • RNN Architecture: Infinite Memory Architecture Solution
  • RNN Architecture: Weight Sharing
  • RNN Architecture: Notations
  • RNN Architecture: ManyToMany Model
  • RNN Architecture: ManyToMany Model Exercise 01
  • RNN Architecture: ManyToMany Model Solution 01
  • RNN Architecture: ManyToMany Model Exercise 02
  • RNN Architecture: ManyToMany Model Solution 02
  • RNN Architecture: ManyToOne Model
  • RNN Architecture: ManyToOne Model Exercise
  • RNN Architecture: ManyToOne Model Solution
  • RNN Architecture: OneToMany Model
  • RNN Architecture: OneToMany Model Exercise
  • RNN Architecture: OneToMany Model Solution
  • RNN Architecture: Activity Many to One
  • RNN Architecture: Activity Many to One Exercise
  • RNN Architecture: Activity Many to One Solution
  • RNN Architecture: ManyToMany Different Sizes Model
  • RNN Architecture: Activity Many to Many Nmt
  • RNN Architecture: Models Summary
  • RNN Architecture: Deep RNNs
  • RNN Architecture: Deep RNNs Exercise
  • RNN Architecture: Deep RNNs Solution
  • Gradient Descent in RNN: Introduction to Gradient Descent Module
  • Gradient Descent in RNN: Example Setup
  • Gradient Descent in RNN: Equations
  • Gradient Descent in RNN: Equations Exercise
  • Gradient Descent in RNN: Equations Solution
  • Gradient Descent in RNN: Loss Function
  • Gradient Descent in RNN: Why Gradients
  • Gradient Descent in RNN: Why Gradients Exercise
  • Gradient Descent in RNN: Why Gradients Solution
  • Gradient Descent in RNN: Chain Rule
  • Gradient Descent in RNN: Chain Rule in Action
  • Gradient Descent in RNN: Backpropagation Through Time
  • Gradient Descent in RNN: Activity
  • RNN Implementation: Automatic Differentiation
  • RNN Implementation: Automatic Differentiation PyTorch
  • RNN Implementation: Language Modelling Next Word Prediction Vocabulary Index
  • RNN Implementation: Language Modelling Next Word Prediction Vocabulary Index Embeddings
  • RNN Implementation: Language Modelling Next Word Prediction RNN Architecture
  • RNN Implementation: Language Modelling Next Word Prediction Python 1
  • RNN Implementation: Language Modelling Next Word Prediction Python 2
  • RNN Implementation: Language Modelling Next Word Prediction Python 3
  • RNN Implementation: Language Modelling Next Word Prediction Python 4
  • RNN Implementation: Language Modelling Next Word Prediction Python 5
  • RNN Implementation: Language Modelling Next Word Prediction Python 6
  • Sentiment Classification using RNN: Vocabulary Implementation
  • Sentiment Classification using RNN: Vocabulary Implementation Helpers
  • Sentiment Classification using RNN: Vocabulary Implementation from File
  • Sentiment Classification using RNN: Vectorizer
  • Sentiment Classification using RNN: RNN Setup 1
  • Sentiment Classification using RNN: RNN Setup 2
  • Sentiment Classification using RNN: What Next
  • Vanishing Gradients in RNN: Introduction to Better RNNs Module
  • Vanishing Gradients in RNN: Introduction Vanishing Gradients in RNN
  • Vanishing Gradients in RNN: GRU
  • Vanishing Gradients in RNN: GRU Optional
  • Vanishing Gradients in RNN: LSTM
  • Vanishing Gradients in RNN: LSTM Optional
  • Vanishing Gradients in RNN: Bidirectional RNN
  • Vanishing Gradients in RNN: Attention Model
  • Vanishing Gradients in RNN: Attention Model Optional
  • TensorFlow: Introduction to TensorFlow
  • TensorFlow: TensorFlow Text Classification Example using RNN
  • Project I_ Book Writer: Introduction
  • Project I_ Book Writer: Data Mapping
  • Project I_ Book Writer: Modelling RNN Architecture
  • Project I_ Book Writer: Modelling RNN Model in TensorFlow
  • Project I_ Book Writer: Modelling RNN Model Training
  • Project I_ Book Writer: Modelling RNN Model Text Generation
  • Project I_ Book Writer: Activity
  • Project II_ Stock Price Prediction: Problem Statement
  • Project II_ Stock Price Prediction: Dataset
  • Project II_ Stock Price Prediction: Data Preparation
  • Project II_ Stock Price Prediction: RNN Model Training and Evaluation
  • Project II_ Stock Price Prediction: Activity
  • Further Readings and Resources: Further Readings and Resources

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