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      • Granger Causality Test
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      • Partial Correlation
      • Chi-Square test – How to test statistical significance?
      • Gentle Introduction to Markov Chain
      • What is P-Value? – Understanding the meaning, math and methods
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      • Mahalanobis Distance – Understanding the math with examples (python)
      • T Test (Students T Test) – Understanding the math and how it works
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      • One Sample T Test – Clearly Explained with Examples | ML+
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      • TensorFlow vs PyTorch – A Detailed Comparison
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      • Complete Guide to Natural Language Processing (NLP) – with Practical Examples
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      • 101 NLP Exercises (using modern libraries)
      • Gensim Tutorial – A Complete Beginners Guide
      • LDA in Python – How to grid search best topic models?
      • Topic Modeling with Gensim (Python)
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      • Cosine Similarity – Understanding the math and how it works (with python codes)
      • spaCy Tutorial – Complete Writeup
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      • Feature Selection – Ten Effective Techniques with Examples
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    • Python
      • Setup Python environment for ML
      • How to speed up Python using Cython
      • Python to Cython in Jupyter
      • How to deal with Big Data in Python for ML Projects (100+ GB)?
      • Decorators in Python – How to enhance functions without changing the code?
      • Generators in Python – How to lazily return values only when needed and save memory?
      • Iterators in Python – What are Iterators and Iterables?
      • Python Module – What are modules and packages in python?
      • Object Oriented Programming (OOPS) in Python
      • Conda virtual environment
      • List Comprehensions in Python – My Simplified Guide
      • Parallel Processing in Python – A Practical Guide with Examples
      • Python @Property Explained – How to Use and When? (Full Examples)
      • pdb – How to use Python debugger
      • Python Regular Expressions Tutorial and Examples: A Simplified Guide
      • Python Logging – Simplest Guide with Full Code and Examples
      • datetime in Python – Simplified Guide with Clear Examples
      • Requests in Python Tutorial – How to send HTTP requests in Python?
      • Python JSON – Guide
      • Python Collections – An Introductory Guide
      • cProfile – How to profile your python code
      • Python Yield – What does the yield keyword do?
      • Lambda Function in Python – How and When to use?
      • What does Python Global Interpreter Lock – (GIL) do?
    • Time Series
      • Granger Causality Test
      • Augmented Dickey Fuller Test (ADF Test) – Must Read Guide
      • KPSS Test for Stationarity
      • ARIMA Model – Complete Guide to Time Series Forecasting in Python
      • Time Series Analysis in Python – A Comprehensive Guide with Examples
      • Vector Autoregression (VAR) – Comprehensive Guide with Examples in Python
    • Statistics
      • Partial Correlation
      • Chi-Square test – How to test statistical significance?
      • Gentle Introduction to Markov Chain
      • What is P-Value? – Understanding the meaning, math and methods
      • How to implement common statistical significance tests and find the p value?
      • Mahalanobis Distance – Understanding the math with examples (python)
      • T Test (Students T Test) – Understanding the math and how it works
      • Confidence Interval – Fully Explained
      • Understanding Standard Error – A practical guide with examples
      • One Sample T Test – Clearly Explained with Examples | ML+
    • Deep Learning
      • TensorFlow vs PyTorch – A Detailed Comparison
      • How to use tf.function to speed up Python code in Tensorflow
      • How to implement Linear Regression in TensorFlow
    • NLP
      • Complete Guide to Natural Language Processing (NLP) – with Practical Examples
      • Text Summarization Approaches for NLP – Practical Guide with Generative Examples
      • 101 NLP Exercises (using modern libraries)
      • Gensim Tutorial – A Complete Beginners Guide
      • LDA in Python – How to grid search best topic models?
      • Topic Modeling with Gensim (Python)
      • Lemmatization Approaches with Examples in Python
      • Topic modeling visualization – How to present the results of LDA models?
      • Cosine Similarity – Understanding the math and how it works (with python codes)
      • spaCy Tutorial – Complete Writeup
      • Training Custom NER models in SpaCy to auto-detect named entities [Complete Guide]
      • Building chatbot with Rasa and spaCy
      • SpaCy Text Classification – How to Train Text Classification Model in spaCy (Solved Example)?
    • Plots
      • Matplotlib Plotting Tutorial – Complete overview of Matplotlib library
      • Matplotlib Histogram – How to Visualize Distributions in Python
      • Bar Plot in Python – How to compare Groups visually
      • Python Boxplot – How to create and interpret boxplots (also find outliers and summarize distributions)
      • Waterfall Plot in Python
      • Top 50 matplotlib Visualizations – The Master Plots (with full python code)
      • Matplotlib Tutorial – A Complete Guide to Python Plot w/ Examples
      • Matplotlib Pyplot – How to import matplotlib in Python and create different plots
      • Python Scatter Plot – How to visualize relationship between two numeric features
      • Matplotlib Line Plot – How to create a line plot to visualize the trend?
      • Matplotlib Subplots – How to create multiple plots in same figure in Python?
    • Machine Learning
      • Main Pitfalls in Machine Learning Projects
      • Deploy ML model in AWS Ec2 – Complete no-step-missed guide
      • Feature selection using FRUFS and VevestaX
      • Simulated Annealing Algorithm Explained from Scratch (Python)
      • Bias Variance Tradeoff – Clearly Explained
      • Complete Introduction to Linear Regression in R
      • Caret Package – A Practical Guide to Machine Learning in R
      • Logistic Regression – A Complete Tutorial With Examples in R
      • Principal Component Analysis (PCA) – Better Explained
      • K-Means Clustering Algorithm from Scratch
      • How Naive Bayes Algorithm Works? (with example and full code)
      • Feature Selection – Ten Effective Techniques with Examples
      • Evaluation Metrics for Classification Models – How to measure performance of machine learning models?
      • Brier Score – How to measure accuracy of probablistic predictions
      • Portfolio Optimization with Python using Efficient Frontier with Practical Examples
      • Gradient Boosting – A Concise Introduction from Scratch
    • Deployment
      • Population Stability Index (PSI)
      • Deploy ML model in AWS Ec2 – Complete no-step-missed guide
    • Julia
      • Julia – Programming Language
      • Linear Regression in Julia
      • Logistic Regression in Julia – Practical Guide with Examples
      • For-Loop in Julia
      • While-loop in Julia
      • Function in Julia
      • DataFrames in Julia
    • Data Wrangling
      • 101 NumPy Exercises for Data Analysis (Python)
      • 101 Pandas Exercises for Data Analysis
      • SQL Tutorial – A Simple and Intuitive Guide to the Structured Query Language
      • Dask – How to handle large dataframes in python using parallel computing
      • Modin – How to speedup pandas by changing one line of code
      • Python Numpy – Introduction to ndarray [Part 1]
      • data.table in R – The Complete Beginners Guide
      • 101 Python datatable Exercises (pydatatable)
      • 101 R data.table Exercises
      • 101 NLP Exercises (using modern libraries)
    • Recent
      • How to deal with Big Data in Python for ML Projects (100+ GB)?
      • Granger Causality Test
      • Main Pitfalls in Machine Learning Projects
      • Population Stability Index (PSI)
      • Deploy ML model in AWS Ec2 – Complete no-step-missed guide
      • Feature selection using FRUFS and VevestaX
      • Object Oriented Programming (OOPS) in Python
      • Simulated Annealing Algorithm Explained from Scratch (Python)
      • Partial Correlation
      • Chi-Square test – How to test statistical significance for categorical data?
      • Conda virtual environment
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      • Data Science Specializations >
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  • Getting Started
    • #1. How to formulate machine learning problem
    • #2. Setup Python environment for ML
    • #3. Exploratory Data Analysis (EDA)
    • #4. How to reduce the memory size of Pandas Data frame
    • #5. Missing Data Imputation Approaches
    • #6. Interpolation in Python
    • #7. MICE imputation
    • #8. How to detect outliers using IQR and Boxplots?
    • #9. How to detect outliers with z-score
  • Beginners Corner
    • How to formulate machine learning problem
    • Setup Python environment for ML
    • What is a Data Scientist?
    • The story of how Data Scientists came into existence
    • Task Checklist for Almost Any Machine Learning Project
    • Data Science Roadmap (2023)
    • Why learn the math behind Machine Learning and AI?
    • Mistakes programmers make when starting machine learning
    • Machine Learning Use Cases
    • How to deal with Big Data in Python for ML Projects (100+ GB)?
    • Main Pitfalls in Machine Learning Projects
  • Courses
    • 1. Foundations of Machine Learning
    • 2. Python Programming
    • 3. NumPy for Data Science
    • 4. Pandas for Data Science
    • 5. Linux Command
    • 6. SQL for Data Science – Level 1
    • 7. SQL for Data Science – Level 2
    • 8. SQL for Data Science – Level 3
    • 9. SQL for Data Science – Window Functions
    • 10. Data Pre-processing and EDA
    • 11. Linear regression and regularisation
    • 12. Classification: Logistic Regression
    • 13. Imbalanced Classification
    • 14. Supervised ML Algorithms
    • 15. Ensemble Learning
    • 16. ML Deployment in AWS EC2
    • 17. Deploy in AWS Lamda
    • 18. Deploy in AWS Sagemaker
    • 19. PySpark for Data Science – I: Fundamentals
    • 20. PySpark for Data Science – II: Statistics for Big Data
    • 21. Introduction to Time Series Analaysis
    • 22. Time Series Analysis – I (Beginners)
    • 23. Time Series Analysis – II (Intermediate )
    • 24. Time Series Forecasting Part 1: Statistical Models
    • 25. Time Series Forecasting Part 2: ARIMA modeling and Tests
    • 26. Time Series Forecasting Part 3: Vector Auto Regression
    • 27. Time Series Analysis – III: Singular Spectrum Analysis
    • 28. Feature Engineering for Time Series Project: I
    • 29. Feature Engineering for Time Series Projects: II
    • 31. Estimating customer lifetime value for business
    • 32. Microsoft malware detection project
    • 33. Credit card fraud detection
    • 34. Restaurant Visitor Forecasting
    • 35. Optimizing Marketing Budget Spend with Marketing Mix Modeling
    • 36. Predict Rating given Amazon Product review using NLP
    • 37. Foundations of Deep Learning in Python
    • 38. Foundations of Deep Learning: Part 2
    • 39. Applied Deep Learning with PyTorch
    • 40. Detecting defects in Steel sheet with Computer vision
    • 41. Project Text Generation using Language models with LSTM
    • 42. Project Classifying Sentiment of reviews using BERT NLP
    • 43. Spacy for NLP
    • 44. Base R Programming
    • 45. Dplyr for Data Wrangling
    • 46. Wrangling Data with Data Table
    • 47. GGPlot2 Visualization for Data Analysis
    • 48. Statistical foundation for ML in R
    • 49. Regression Model in R
    • 50. Caret Package in R
  • Python
    • Introduction to Python
      • Setup Python environment for ML
      • Decorators in Python
      • Generators in Python
      • Iterators in Python
      • Python Module
      • Object Oriented Programming (OOPS) in Python
      • List Comprehension
      • Requests in Python
      • Python Collections
      • Python Logging
    • Plots
      • Matplotlib Tutorial
      • Matplotlib Histogram
      • Bar Plot in Python
      • Python Boxplot
      • Waterfall Plot in Python
      • Top 50 matplotlib Visualizations
      • Matplotlib Tutorial
      • Matplotlib Pyplot
      • Python Scatter Plot
      • Matplotlib Subplots
    • Data Wrangling
      • 101 NumPy Exercises for Data Analysis (Python)
      • 101 Pandas Exercises for Data Analysis
      • 101 Pandas Exercises for Data Analysis
      • Dask
      • Modin
      • Numpy Tutorial
      • data.table in R
      • 101 Python datatable Exercises (pydatatable)
      • 101 R data.table Exercises
    • Advanced Python
      • Conda create environment and everything you need to know to manage conda virtual environment
      • Python @Property Explained
      • pdb – How to use Python debugger
      • Python JSON – Guide
      • cProfile – How to profile your python code
      • Python Yield
      • Lambda Function in Python
      • What does Python Global Interpreter Lock
      • Install opencv python
      • Install pip mac
      • Scrapy vs. Beautiful Soup
      • Add Python to PATH
    • PySpark
      • Introduction to Pyspark
      • Power of Pyspark
      • Install PySpark on Windows
      • Install PySpark on MAC
      • Install PySpark on Linux
      • What is Sparksession
      • Read and Write files using PySpark
      • Pyspark Show
      • Run SQL Queries with PySpark
      • PySpark Pandas API
      • Select columns in PySpark dataframe
      • PySpark withColumn()
      • Pyspark Drop Columns
      • PySpark Rename Columns
      • PySpark Filter vs Where
      • PySpark orderBy() and sort()
      • PySpark GroupBy()
      • PySpark Pivot
      • PySpark Joins
      • PySpark Union
      • PySpark Connect to MySQL
      • PySpark Connect to PostgreSQL
      • PySpark Connect to SQL Serve
      • PySpark Connect to Redshift
      • PySpark Connect to Snowflake
      • PySpark Linear Regression
      • PySpark Logistic Regression
      • PySpark Decision Tree
      • PySpark Ridge Regression
      • PySpark Lasso Regression
      • PySpark Random Forest
      • PySpark Gradient Boosting model
      • PySpark Mllib K-Means Clustering
      • PySpark Statistics Mean
      • PySpark Statistics Median
      • PySpark Statistics Mode
      • PySpark Statistics Standard Deviation
      • PySpark Statistics Variance
      • PySpark Statistics Deciles and Quartiles
      • PySpark Correlation
      • PySpark Chi-Square Test
      • PySpark Variable type Identification
      • PySpark Outlier Detection and Treatment
      • PySpark Missing Data Imputation
      • PySpark Variance Inflation Factor (VIF)
      • PySpark StringIndexer
      • PySpark OneHot Encoding
      • PySpark Exercises – 101 PySpark Exercises for Data Analysis
      • Others
        • Deployment
          • Population Stability Index (PSI)
          • Deploy ML model in AWS Ec2
        • Julia
          • Julia – Programming Language
          • Linear Regression in Julia
          • Logistic Regression in Julia
          • For-Loop in Julia
          • While-loop in Julia
          • Function in Julia
          • DataFrames in Julia
        • Linux
          • ls command in Linux – Mastering the “ls” command in Linux
          • mkdir command in Linux – A comprehensive guide for mkdir command
          • cd command in linux – Mastering the ‘cd’ command in Linux
          • cat command in Linux – Mastering the ‘cat’ command in Linux
          • Linux Commands List with Examples
  • Machine Learning
    • Deep Learning
      • TensorFlow vs PyTorch
      • How to use tf.function to speed up Python code in Tensorflow
      • How to implement Linear Regression in TensorFlow
    • NLP
      • Complete Guide to Natural Language Processing (NLP)
      • Text Summarization Approaches for NLP
      • 101 NLP Exercises (using modern libraries)
      • Gensim Tutorial
      • LDA in Python
      • Topic Modeling with Gensim (Python)
      • Lemmatization Approaches with Examples in Python
      • Topic modeling visualization
      • Cosine Similarity
      • spaCy Tutorial
      • Training Custom NER models in SpaCy to auto-detect named entities
      • Building chatbot with Rasa and spaCy
      • SpaCy Text Classification
    • Algorithms
      • K-Means Clustering Algorithm from Scratch
      • Simulated Annealing Algorithm Explained from Scratch
      • How Naive Bayes Algorithm Works?
      • Feature selection using FRUFS and VevestaX
      • Principal Component Analysis
      • Gradient Boosting
      • Feature Selection – Ten Effective Techniques with Examples
    • Projects
      • Evaluation Metrics for Classification Models
      • Deploy ML model in AWS Ec2
      • Portfolio Optimization with Python using Efficient Frontier
      • Bias Variance Tradeoff
    • Specific Topics
      • Logistic Regression
      • Complete Introduction to Linear Regression in R
      • Caret Package
      • Brier Score
  • Time Series
    • Granger Causality Test
    • Augmented Dickey Fuller Test (ADF Test)
    • KPSS Test for Stationarity
    • ARIMA Model
    • Time Series Analysis in Python
    • Vector Autoregression (VAR)
  • Prob and Stats
    • Probability
      • Introduction to Probability
      • Odds and Odds Ratios
      • Independent and Dependent Events
      • Mutually Exclusive Events
      • Joint Probability
      • Conditional Probability
      • Bayes’ Theorem
      • Expected Value
      • Probability frequency distribution
      • Discrete Frequency Distributions
      • Continuous Frequency Distributions
    • Partial Correlation
    • Chi-Square Test – Theory & Math
    • Gentle Introduction to Markov Chain
    • What is P-Value?
    • How to implement common statistical significance tests and find the p value?
    • Mahalanobis Distance
    • T Test (Students T Test)
    • Confidence Interval in Statistics
    • Standard Error in Statistics
    • One Sample T Test
    • Descriptive and inferential statistics
    • Types of data in statistics
    • Measures of central tendency
    • Quantiles and Percentiles
    • Measures of dispersion
    • Skewness and kurtosis
    • Central Limit Theroem
    • Law of large numbers
    • Standard Error
    • Sampling and sampling distributions
    • Correlation
  • SQL
    • SQL Tutorial – The Introduction
    • SQL Subquery (advanced)
    • SQL Window Functions (advanced)
    • SQL Window Functions Exercises – Set 1
    • SQL Window Functions Exercises – Set 2
    • Intro to SQL
    • SQL Select
    • SQL Select Distinct
    • SQL Where
    • SQL Order by
    • SQL Insert Into
    • SQL AND, OR, and NOT
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    • SQL LIKE
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    • How to transpose columns to rows in SQL?
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    • SQL Select Into
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    • SQL Case
    • SQL Null Functions
    • SQL Comments
    • SQL Operators
    • SQL Create Table
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    • SQL Primary Key
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    • Sort multiple columns in SQL and in different directions?
    • Count the number of work days between two dates?
    • Compute maximum of multiple columns, aks row wise max?
    • GROUP BY clause on multiple columns in SQL?
  • Linear Algebra
    • 01. Introduction to Linear Algebra
    • 02. Types of Tensors
    • 03. Scalars
    • 04. Vectors
    • 05. Vectors Linear Algebra
    • 06. Matrix Types
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    • 08. Orthogonal and Ortrhonormal Matrix
    • 09. Eigenvectors and Eigenvalues
    • 10. Affine Transformation
    • 11. Singular Value Decomposition (SVD)
    • 12. System of Equations
    • 13. Linear Regression Algorithm
    • 14. Principal Component Analysis
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  • Getting Started
    • #1. How to formulate machine learning problem
    • #2. Setup Python environment for ML
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    • #6. Interpolation in Python
    • #7. MICE imputation
    • #8. How to detect outliers using IQR and Boxplots?
    • #9. How to detect outliers with z-score
  • Beginners Corner
    • How to formulate machine learning problem
    • Setup Python environment for ML
    • What is a Data Scientist?
    • The story of how Data Scientists came into existence
    • Task Checklist for Almost Any Machine Learning Project
    • Data Science Roadmap (2023)
    • Why learn the math behind Machine Learning and AI?
    • Mistakes programmers make when starting machine learning
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    • How to deal with Big Data in Python for ML Projects (100+ GB)?
    • Main Pitfalls in Machine Learning Projects
  • Courses
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    • 22. Time Series Analysis – I (Beginners)
    • 23. Time Series Analysis – II (Intermediate )
    • 24. Time Series Forecasting Part 1: Statistical Models
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    • 26. Time Series Forecasting Part 3: Vector Auto Regression
    • 27. Time Series Analysis – III: Singular Spectrum Analysis
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    • 31. Estimating customer lifetime value for business
    • 32. Microsoft malware detection project
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    • 35. Optimizing Marketing Budget Spend with Marketing Mix Modeling
    • 36. Predict Rating given Amazon Product review using NLP
    • 37. Foundations of Deep Learning in Python
    • 38. Foundations of Deep Learning: Part 2
    • 39. Applied Deep Learning with PyTorch
    • 40. Detecting defects in Steel sheet with Computer vision
    • 41. Project Text Generation using Language models with LSTM
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    • 46. Wrangling Data with Data Table
    • 47. GGPlot2 Visualization for Data Analysis
    • 48. Statistical foundation for ML in R
    • 49. Regression Model in R
    • 50. Caret Package in R
  • Python
    • Introduction to Python
      • Setup Python environment for ML
      • Decorators in Python
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          • mkdir command in Linux – A comprehensive guide for mkdir command
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          • Linux Commands List with Examples
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      • Complete Guide to Natural Language Processing (NLP)
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      • 101 NLP Exercises (using modern libraries)
      • Gensim Tutorial
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    • Projects
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  • Time Series
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      • Discrete Frequency Distributions
      • Continuous Frequency Distributions
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    • SQL Left Join
    • SQL Right Join
    • SQL Full Join
    • SQL Self Join
    • SQL UNION
    • SQL GROUP BY
    • SQL HAVING
    • SQL EXISTS
    • SQL ANY, ALL Operators
    • How to transpose columns to rows in SQL?
    • How to select only rows with max value on a column?
    • SQL Select Into
    • SQL Insert Into Select
    • SQL Case
    • SQL Null Functions
    • SQL Comments
    • SQL Operators
    • SQL Create Table
    • SQL Drop Table
    • SQL Primary Key
    • SQL Foreign Key
    • Sort multiple columns in SQL and in different directions?
    • Count the number of work days between two dates?
    • Compute maximum of multiple columns, aks row wise max?
    • GROUP BY clause on multiple columns in SQL?
  • Linear Algebra
    • 01. Introduction to Linear Algebra
    • 02. Types of Tensors
    • 03. Scalars
    • 04. Vectors
    • 05. Vectors Linear Algebra
    • 06. Matrix Types
    • 07. Matrix Operations
    • 08. Orthogonal and Ortrhonormal Matrix
    • 09. Eigenvectors and Eigenvalues
    • 10. Affine Transformation
    • 11. Singular Value Decomposition (SVD)
    • 12. System of Equations
    • 13. Linear Regression Algorithm
    • 14. Principal Component Analysis

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