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Премиум
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    Introduction to the course
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    Introduction to Kaggle
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    Installation of Python and Anaconda
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    Python Introduction
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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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    Functions
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    String Part1
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    String Part2
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    List Part1
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    List Part2
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    List Part3
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    List Part4
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    Tuples
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    Sets
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    Dictionaries
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    Comprehentions
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    Introduction
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    Numpy Operations Part1
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    Numpy Operations Part2
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    Introduction
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    Series
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    DataFrame
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    Operations Part1
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    Operations Part2
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    Indexes
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    loc and iloc
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    Reading CSV
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    Merging Part1
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    groupby
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    Merging Part2
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    Pivot Table
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    Linear Algebra : Vectors
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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
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    Inferential Statistics
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    Probability Theory
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    Probability Distribution
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    Expected Values Part1
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    Expected Values Part2
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    Without Experiment
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    Binomial Distribution
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    Commulative Distribution
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    PDF
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    Normal Distribution
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    z Score
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    Sampling
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    Sampling Distribution
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    Central Limit Theorem
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    Confidence Interval Part1
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    Confidence Interval Part2
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    Introduction
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    NULL And Alternate Hypothesis
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    Examples
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    One/Two Tailed Tests
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    Critical Value Method
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    z Table
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    Examples
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    More Examples
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    p Value
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    Types of Error
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    t- distribution Part1
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    t- distribution Part2
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    Matplotlib
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    Seaborn
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    Case Study
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    Seaborn On Time Series Data
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    Introduction
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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
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    Data Sourcing and Cleaning part6
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    Data Cleaning part1
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    Data Cleaning part2
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    Univariate Analysis Part1
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    Univariate Analysis Part2
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    Segmented Analysis
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    Bivariate Analysis
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    Derived Columns
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    Introduction to Machine Learning
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    Types of Machine Learning
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    Introduction to Linear Regression (LR)
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    How LR Works?
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    Some Fun With Maths Behind LR
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    R Square
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    LR Case Study Part1
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    LR Case Study Part2
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    LR Case Study Part3
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    Residual Square Error (RSE)
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    Introduction
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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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    Adjusted R Square
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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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    Introduction to the Problem Statement
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    Playing With Data
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    Building Model Part1
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    Building Model Part2
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    Building Model Part3
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    Verification of Model
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    Pre-Req For Gradient Descent Part1
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    Pre-Req For Gradient Descent Part2
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    Cost Functions
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    Defining Cost Functions More Formally
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    Gradient Descent
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    Optimisation
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    Closed Form Vs Gradient Descent
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    Gradient Descent case study
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    Introduction to Classification
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    Defining Classification Mathematically
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    Introduction to KNN
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    Accuracy of KNN
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    Effectiveness of KNN
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    Distance Metrics
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    Distance Metrics Part2
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    Finding k
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    KNN on Regression
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    Case Study
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    Classification Case1
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    Classification Case2
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    Classification Case3
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    Classification Case4
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    Performance Metrics Part1
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    Performance Metrics Part2
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    Performance Metrics Part3
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    Model Creation Case1
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    Model Creation Case2
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    Gridsearch Case study Part1
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    Gridsearch Case study Part2
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    Introduction to Naive Bayes
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    Bayes Theorem
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    Practical Example from NB with One Column
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    Practical Example from NB with Multiple Columns
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    Naive Bayes On Text Data Part1
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    Naive Bayes On Text Data Part2
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    Laplace Smoothing
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    Bernoulli Naive Bayes
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    Case Study 1
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    Case Study 2 Part1
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    Case Study 2 Part2
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    Introduction
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    Sigmoid Function
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    Log Odds
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    Case Study
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    Introduction
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    Hyperplane Part1
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    Hyperplane Part2
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    Maths Behind SVM
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    Support Vectors
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    Slack Variable
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    SVM Case Study Part1
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    SVM Case Study Part2
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    Kernel Part1
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    Kernel Part2
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    Case Study : 2
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    Case Study : 3 Part1
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    Case Study : 3 Part2
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    Case Study 4
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    Introduction
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    Example of DT
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    Homogenity
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    Gini Index
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    Information Gain Part1
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    Information Gain Part2
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    Advantages and Disadvantages of DT
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    Preventing Overfitting Issues in DT
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    DT Case Study Part1
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    DT Case Study Part2
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    Introduction to Ensembles
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    Bagging
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    Advantages
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    Runtime
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    Case study
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    Introduction to Boosting
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    Weak Learners
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    Shallow Decision Tree
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    Adaboost Part1
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    Adaboost Part2
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    Adaboost Case Study
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    XGBoost
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    Boosting Part1
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    Boosting Part2
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    XGboost Algorithm
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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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    Model Selection Part1
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    Model Selection Part2
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    Model Selection Part3
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    Introduction to Clustering
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    Segmentation
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    Kmeans
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    Maths Behind Kmeans
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    More Maths
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    Kmeans plus
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    Value of K
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    Hopkins test
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    Case Study Part1
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    Case Study Part2
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    More on Segmentation
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    Hierarchial Clustering
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    Case Study
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    Introduction
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    PCA
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    Maths Behind PCA
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    Case Study Part1
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    Case Study Part2
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    Introduction
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    Example Part1
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    Example Part2
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    Optimal Solution
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    Case study
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    Regularization
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    Ridge and Lasso
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    Case Study
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    Model Selection
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    Adjusted R Square
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    Expectations
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    Introduction
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    History
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    Perceptron
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    Multi Layered Perceptron
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    Neural Network Playground
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    Introduction to the Problem Statement
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    Playing With The Data
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    Translating the Problem In Machine Learning World
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    Dealing with Text Data
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    Train, Test And Cross Validation Split
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    Understanding Evaluation Matrix: Log Loss
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    Building A Worst Model
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    Evaluating Worst ML Model
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    First Categorical column analysis
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    Response encoding and one hot encoder
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    Laplace Smoothing and Calibrated classifier
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    Significance of first categorical column
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    Second Categorical column
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    Third Categorical column
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    Data pre-processing before building machine learning model
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    Building Machine Learning model :part1
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    Building Machine Learning model :part2
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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
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    Building Machine Learning model :part6