Machine Learning A-Z Become Kaggle Master
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       Video Length : 42h50m0s
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       Tasks Number : 283
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       Students Enrolled : 1545
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       Medium Level
  • Curriculum
  • 1. Python Fundamentals
    • videocam
      Introduction to the course
      10m0s
    • videocam
      Introduction to Kaggle
      10m0s
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      Installation of Python and Anaconda
      10m0s
    • videocam
      Python Introduction
      10m0s
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      Variables in Python
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      Numeric Operations in Python
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      Logical Operations
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      If else Loop
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      for while Loop
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    • videocam
      Functions
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    • videocam
      String Part1
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      String Part2
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    • videocam
      List Part1
      10m0s
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      List Part2
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      List Part3
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      List Part4
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    • videocam
      Tuples
      10m0s
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      Sets
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      Dictionaries
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      Comprehentions
      10m0s
  • 2. Numpy
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      Introduction
      10m0s
    • videocam
      Numpy Operations Part1
      10m0s
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      Numpy Operations Part2
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  • 3. Pandas
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      Introduction
      10m0s
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      Series
      10m0s
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      DataFrame
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    • videocam
      Operations Part1
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      Operations Part2
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    • videocam
      Indexes
      10m0s
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      loc and iloc
      10m0s
    • videocam
      Reading CSV
      10m0s
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      Merging Part1
      10m0s
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      groupby
      10m0s
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      Merging Part2
      10m0s
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      Pivot Table
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  • 4. Some Fun With Maths
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      Linear Algebra : Vectors
      10m0s
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      Linear Algebra : Matrix Part1
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      Linear Algebra : Matrix Part2
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      Linear Algebra : Going From 2D to nD Part1
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      Linear Algebra : 2D to nD Part2
      10m0s
  • 5. Inferential Statistics
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      Inferential Statistics
      10m0s
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      Probability Theory
      10m0s
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      Probability Distribution
      10m0s
    • videocam
      Expected Values Part1
      10m0s
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      Expected Values Part2
      10m0s
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      Without Experiment
      10m0s
    • videocam
      Binomial Distribution
      10m0s
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      Commulative Distribution
      10m0s
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      PDF
      10m0s
    • videocam
      Normal Distribution
      10m0s
    • videocam
      z Score
      10m0s
    • videocam
      Sampling
      10m0s
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      Sampling Distribution
      10m0s
    • videocam
      Central Limit Theorem
      10m0s
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      Confidence Interval Part1
      10m0s
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      Confidence Interval Part2
      10m0s
  • 6. Hypothesis Testing
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      Introduction
      10m0s
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      NULL And Alternate Hypothesis
      10m0s
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      Examples
      10m0s
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      One/Two Tailed Tests
      10m0s
    • videocam
      Critical Value Method
      10m0s
    • videocam
      z Table
      10m0s
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      Examples
      10m0s
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      More Examples
      10m0s
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      p Value
      10m0s
    • videocam
      Types of Error
      10m0s
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      t- distribution Part1
      10m0s
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      t- distribution Part2
      10m0s
  • 7. Data Visualisation
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      Matplotlib
      10m0s
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      Seaborn
      10m0s
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      Case Study
      10m0s
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      Seaborn On Time Series Data
      10m0s
  • 8. Exploratory Data Analysis
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      Introduction
      10m0s
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      Data Sourcing and Cleaning part1
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      Data Sourcing and Cleaning part2
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      Data Sourcing and Cleaning part3
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      Data Sourcing and Cleaning part4
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      Data Sourcing and Cleaning part5
      10m0s
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      Data Sourcing and Cleaning part6
      10m0s
    • videocam
      Data Cleaning part1
      10m0s
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      Data Cleaning part2
      10m0s
    • videocam
      Univariate Analysis Part1
      10m0s
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      Univariate Analysis Part2
      10m0s
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      Segmented Analysis
      10m0s
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      Bivariate Analysis
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      Derived Columns
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  • 9. Simple Linear Regression
    • videocam
      Introduction to Machine Learning
      10m0s
    • videocam
      Types of Machine Learning
      10m0s
    • videocam
      Introduction to Linear Regression (LR)
      10m0s
    • videocam
      How LR Works?
      10m0s
    • videocam
      Some Fun With Maths Behind LR
      10m0s
    • videocam
      R Square
      10m0s
    • videocam
      LR Case Study Part1
      10m0s
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      LR Case Study Part2
      10m0s
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      LR Case Study Part3
      10m0s
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      Residual Square Error (RSE)
      10m0s
  • 10. Multiple Linear Regression
    • videocam
      Introduction
      10m0s
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      Case Study part1
      10m0s
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      Case Study part2
      10m0s
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      Case Study part3
      10m0s
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      Adjusted R Square
      10m0s
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      Case Study Part1
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      Case Study Part2
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      Case Study Part3
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      Case Study Part4
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      Case Study Part5
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      Case Study Part6 (RFE)
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  • 11. Hotstar/Netflix: Real world Case Study for Multiple Linear Regression
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      Introduction to the Problem Statement
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      Playing With Data
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    • videocam
      Building Model Part1
      10m0s
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      Building Model Part2
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      Building Model Part3
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      Verification of Model
      10m0s
  • 12. Gradient Descent
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      Pre-Req For Gradient Descent Part1
      10m0s
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      Pre-Req For Gradient Descent Part2
      10m0s
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      Cost Functions
      10m0s
    • videocam
      Defining Cost Functions More Formally
      10m0s
    • videocam
      Gradient Descent
      10m0s
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      Optimisation
      10m0s
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      Closed Form Vs Gradient Descent
      10m0s
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      Gradient Descent case study
      10m0s
  • 13. KNN
    • videocam
      Introduction to Classification
      10m0s
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      Defining Classification Mathematically
      10m0s
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      Introduction to KNN
      10m0s
    • videocam
      Accuracy of KNN
      10m0s
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      Effectiveness of KNN
      10m0s
    • videocam
      Distance Metrics
      10m0s
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      Distance Metrics Part2
      10m0s
    • videocam
      Finding k
      10m0s
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      KNN on Regression
      10m0s
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      Case Study
      10m0s
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      Classification Case1
      10m0s
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      Classification Case2
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      Classification Case3
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      Classification Case4
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  • 14. Model Performance Metrics
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      Performance Metrics Part1
      10m0s
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      Performance Metrics Part2
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      Performance Metrics Part3
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  • 15. Model Selection Part1
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      Model Creation Case1
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      Model Creation Case2
      10m0s
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      Gridsearch Case study Part1
      10m0s
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      Gridsearch Case study Part2
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  • 16. Naive Bayes
    • videocam
      Introduction to Naive Bayes
      10m0s
    • videocam
      Bayes Theorem
      10m0s
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      Practical Example from NB with One Column
      10m0s
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      Practical Example from NB with Multiple Columns
      10m0s
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      Naive Bayes On Text Data Part1
      10m0s
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      Naive Bayes On Text Data Part2
      10m0s
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      Laplace Smoothing
      10m0s
    • videocam
      Bernoulli Naive Bayes
      10m0s
    • videocam
      Case Study 1
      10m0s
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      Case Study 2 Part1
      10m0s
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      Case Study 2 Part2
      10m0s
  • 17. Logistic Regression
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      Introduction
      10m0s
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      Sigmoid Function
      10m0s
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      Log Odds
      10m0s
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      Case Study
      10m0s
  • 18. Support Vector Machine (SVM)
    • videocam
      Introduction
      10m0s
    • videocam
      Hyperplane Part1
      10m0s
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      Hyperplane Part2
      10m0s
    • videocam
      Maths Behind SVM
      10m0s
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      Support Vectors
      10m0s
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      Slack Variable
      10m0s
    • videocam
      SVM Case Study Part1
      10m0s
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      SVM Case Study Part2
      10m0s
    • videocam
      Kernel Part1
      10m0s
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      Kernel Part2
      10m0s
    • videocam
      Case Study : 2
      10m0s
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      Case Study : 3 Part1
      10m0s
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      Case Study : 3 Part2
      10m0s
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      Case Study 4
      10m0s
  • 19. Decision Tree
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      Introduction
      10m0s
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      Example of DT
      10m0s
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      Homogenity
      10m0s
    • videocam
      Gini Index
      10m0s
    • videocam
      Information Gain Part1
      10m0s
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      Information Gain Part2
      10m0s
    • videocam
      Advantages and Disadvantages of DT
      10m0s
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      Preventing Overfitting Issues in DT
      10m0s
    • videocam
      DT Case Study Part1
      10m0s
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      DT Case Study Part2
      10m0s
  • 20. Ensembling
    • videocam
      Introduction to Ensembles
      10m0s
    • videocam
      Bagging
      10m0s
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      Advantages
      10m0s
    • videocam
      Runtime
      10m0s
    • videocam
      Case study
      10m0s
    • videocam
      Introduction to Boosting
      10m0s
    • videocam
      Weak Learners
      10m0s
    • videocam
      Shallow Decision Tree
      10m0s
    • videocam
      Adaboost Part1
      10m0s
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      Adaboost Part2
      10m0s
    • videocam
      Adaboost Case Study
      10m0s
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      XGBoost
      10m0s
    • videocam
      Boosting Part1
      10m0s
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      Boosting Part2
      10m0s
    • videocam
      XGboost Algorithm
      10m0s
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      Case Study Part1
      10m0s
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      Case Study Part2
      10m0s
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      Case Study Part3
      10m0s
  • 21. Model Selection Part2
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      Model Selection Part1
      10m0s
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      Model Selection Part2
      10m0s
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      Model Selection Part3
      10m0s
  • 22. Unsupervised Learning
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      Introduction to Clustering
      10m0s
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      Segmentation
      10m0s
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      Kmeans
      10m0s
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      Maths Behind Kmeans
      10m0s
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      More Maths
      10m0s
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      Kmeans plus
      10m0s
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      Value of K
      10m0s
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      Hopkins test
      10m0s
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      Case Study Part1
      10m0s
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      Case Study Part2
      10m0s
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      More on Segmentation
      10m0s
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      Hierarchial Clustering
      10m0s
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      Case Study
      10m0s
  • 23. Dimension Reduction
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      Introduction
      10m0s
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      PCA
      10m0s
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      Maths Behind PCA
      10m0s
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      Case Study Part1
      10m0s
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      Case Study Part2
      10m0s
  • 24. Advanced Machine Learning Algorithms
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      Introduction
      10m0s
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      Example Part1
      10m0s
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      Example Part2
      10m0s
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      Optimal Solution
      10m0s
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      Case study
      10m0s
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      Regularization
      10m0s
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      Ridge and Lasso
      10m0s
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      Case Study
      10m0s
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      Model Selection
      10m0s
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      Adjusted R Square
      10m0s
  • 25. Deep Learning
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      Expectations
      10m0s
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      Introduction
      10m0s
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      History
      10m0s
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      Perceptron
      10m0s
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      Multi Layered Perceptron
      10m0s
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      Neural Network Playground
      10m0s
  • 26. Project : Kaggle
    • videocam
      Introduction to the Problem Statement
      10m0s
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      Playing With The Data
      10m0s
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      Translating the Problem In Machine Learning World
      10m0s
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      Dealing with Text Data
      10m0s
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      Train, Test And Cross Validation Split
      10m0s
    • videocam
      Understanding Evaluation Matrix: Log Loss
      10m0s
    • videocam
      Building A Worst Model
      10m0s
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      Evaluating Worst ML Model
      10m0s
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      First Categorical column analysis
      10m0s
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      Response encoding and one hot encoder
      10m0s
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      Laplace Smoothing and Calibrated classifier
      10m0s
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      Significance of first categorical column
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      Second Categorical column
      10m0s
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      Third Categorical column
      10m0s
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      Data pre-processing before building machine learning model
      10m0s
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      Building Machine Learning model :part1
      10m0s
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      Building Machine Learning model :part2
      10m0s
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      Building Machine Learning model :part3
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      Building Machine Learning model :part4
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      Building Machine Learning model :part5
      10m0s
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      Building Machine Learning model :part6
      10m0s
Authors

Kevin Gautama is a systems design and programming engineer with 16 years of expertise in the fields of electrical and electronics and information technology.

He teaches at the Hanoi University of Industry in the period 2003-2011 and he has a certificate of vocational training by the Ministry of Industry and Commerce and the Hanoi University of Industry.

From extensive design experience through numerous engineering projects, the author founded the Enziin Academy.

The Enziin Academy is a startup in the field of educational, it's core goal is to training design engineers in the fields technology related.

The Enziin Academy is headquartered in Stockholm-Sweden with an orientation operating multi-lingual and global.

The author's skills in IT:

  • Implementing the application infrastructure on Amazon's cloud computing platform.
  • Linux server system administration (Sysadmin).
  • Design load balancing and content distribution system.
  • MySQL database administration.
  • C/C++/C# Programming
  • Ruby and Ruby on Rails Programming
  • Python and Django Programming
  • The WPF/C# on the .NET Framework Programming
  • The PHP/JAVA Programming
  • Machine Learning and Expert System.
  • Internet of Things.

The author's skills in the fields of electric and electronic:

  • The design of popular CPU / MCU systems.
  • Design FPGA / CPLD system (Xilinx - Altera).
  • Design and programming of DSP systems (Texas Instruments).
  • Embedded ARM system design.
  • The RTOS Programming
  • Design and programming electronic power systems.
  • PLC - inverter - sensor - electric control cabinet industrial.
  • Control systems distributed connection with Server.

Read more...

Machine Learning A-Z Become Kaggle Master


Machine Learning A-Z Become Kaggle Master

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