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Machine learning using python is a very good combination. Python, which is a general purpose programming language is known for …

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Machine learning using python is a very good combination. Python, which is a general purpose programming language is known for its scientific computing and its machine learning abilities. This course gives a good knowledge about machine learning with Python.
  • Contents
    • Correlation
    • Simple Regression
    • Logistic regression
    • What is segmentation
    • What is a Decision tree
    • Tree validation
    • Over fitting and Under fitting
    • Cross validation
    • Boot strapping
    • Neural networks
    • SVM
    • Random Forest algorithm
    • Boosting
  • After Completion of This Course any one who enrolled, will have good knowledge about linear regression, logistic regression, SVM, Cross Validation, Decision Tree, Boot strapping , Neural Networks, etc. using python. Python is a very good language for Data Analysis.

Course Currilcum

    • Handout – Linear Regression 00:00:00
    • 1.1 Introduction and Correlation FREE 00:00:00
    • 1.2 LAB_ Correlation FREE 00:00:00
    • 1.3 Beyond Pearson Correlation 00:00:00
    • 1.4 From Correlation to Regression 00:00:00
    • 1.5 Regression _ LAB 00:00:00
    • 1.6 How Good Is My Line 00:00:00
    • 1.7 Multiple Regression Model 00:00:00
    • 1.8 Adjusted R Squared 00:00:00
    • 1.9 Multiple Regression Issue 00:00:00
    • 1.10 Multicolinearity LAB 00:00:00
    • 1.11 Conclusion 00:00:00
    • Regression Quiz Unlimited
    • Handout – Logistic Regression in Python 00:00:00
    • 2.1 Introduction and Need of Logistic Regression FREE 00:00:00
    • 2.2 A Logistic Function FREE 00:00:00
    • 2.3 Multiple Logistic Regression Model 00:00:00
    • 2.4 Goodness of fit LogRegression 00:00:00
    • 2.5 Multicollinearity in Logistic Regression in Python 00:00:00
    • 2.6 individual Impact of Variables in Python 00:00:00
    • 2.7 Model Selection in Python 00:00:00
    • 2.8 Conclusion 00:00:00
    • Logistic Regression Quiz Unlimited
    • Handout – Decision Trees in python 00:00:00
    • 3.1 Introduction to Decision Tree & Segmentation FREE 00:00:00
    • 3.2 The Decision Tree Philosophy & The Decision Tree Approach FREE 00:00:00
    • 3.3 The Splitting criterion &Entropy Calculation 00:00:00
    • 3.4 Information Gain & Calculation 00:00:00
    • 3.5 The Decision tree Algorithm 00:00:00
    • 3.6 Many splits for a Variable 00:00:00
    • 3.7 Decision Tree Fitting and Interpretation 00:00:00
    • 3.8 Decision Tree Validation 00:00:00
    • 3.9 Decision Tree Overfitting 00:00:00
    • 3.10 Pruning and Pruning Parameters 00:00:00
    • 3.11 Tree Building & Model Selection-Lab1 00:00:00
    • 3.12 Tree Building & Model Selection-Lab2 00:00:00
    • 3.13 Conclusion 00:00:00
    • Decision Trees Quiz Unlimited
    • Handout – Model Selection and Cross Validation in Python 00:00:00
    • 4.1 Introduction to Model Selection FREE 00:00:00
    • 4.2 Sensitivity Specificity FREE 00:00:00
    • 4.3 LAB – Sensitivity and Specificity in Python 00:00:00
    • 4.4 Sensitivity Specificity Contd p.1 00:00:00
    • 4.5 Sensitivity Specificit Contd p.2 00:00:00
    • 4.6. ROC AUC and LAB- ROC AUC 00:00:00
    • 4.7 The Best Model 00:00:00
    • 4.8 The Best Model Lab 00:00:00
    • 4.9 Errors 00:00:00
    • 4.10 Overfitting Underfitting 00:00:00
    • 4.11 Bias_Variance Treadoff 00:00:00
    • 4.12 Holdout Data Validation 00:00:00
    • 4.13 Ten fold CV 00:00:00
    • 4.14 Boot Strap Cross Validation 00:00:00
    • 4.15 MSCV Conclusion 00:00:00
    • 00:00:00
    • Handout – Neural Networks in python 00:00:00
    • 5.1 Neural Networks Introduction FREE 00:00:00
    • 5.2 Logistic Regression Recap LAB FREE 00:00:00
    • 5.3 Decision Boundry – Logistic Regression 00:00:00
    • 5.4 Decision Boundry – LAB 00:00:00
    • 5.5 New Representation for Logistic Regression 00:00:00
    • 5.6 Non Linear Decision Boundry – Problem 00:00:00
    • 5.7 Non Linear Decision Boundry – Solution 00:00:00
    • 5.8 Intermediate Output LAB 00:00:00
    • 5.9 Neural Network Intution 00:00:00
    • 5.10 Neural Network Algorithm 00:00:00
    • 5.11 Demo Neural Network Algorithm 00:00:00
    • 5.12 Neural Network LAB 00:00:00
    • 5.13 Local Minima and Number of Hidden Layers 00:00:00
    • 5.14 Digit Recogniser Lab 00:00:00
    • 5.15 Conclusion 00:00:00
    • Neural Networks Quiz Unlimited
    • Handout – SVM in Python 00:00:00
    • 6.1 Introduction To SVM FREE 00:00:00
    • 6.2 The Classifier and Decision Boundary P.1 FREE 00:00:00
    • 6.3 SVM-The Large Margin Classifier 00:00:00
    • 6.4 The SVM Algrothm and Results 00:00:00
    • 6.5 SVM on Python 00:00:00
    • 6.6 Non Linear Boundary 00:00:00
    • 6.7 Kernal Trick 00:00:00
    • 6.8 Kernal Trick in Python 00:00:00
    • 6.9 Soft margin and validataion 00:00:00
    • 6.10 SVM Advantages Disadvantages and Applications 00:00:00
    • 6.11 Lab Digit Recognizer 00:00:00
    • 6.12 SVM Conclusion Session 00:00:00
    • SVM Quiz Unlimited
    • Handout – Random Forest and Boosting in python 00:00:00
    • 7.1 Introduction to Bagging RF Boosting FREE 00:00:00
    • 7.2 Wisdom of Crowd FREE 00:00:00
    • 7.3 Ensemble Learning 00:00:00
    • 7.4 Ensemble Models 00:00:00
    • 7.5 Bagging 00:00:00
    • 7.6 Random Forest 00:00:00
    • 7.7 LAB Random Forests in Python 00:00:00
    • 7.8 Boosting 00:00:00
    • 7.9 Boosting Illustration 00:00:00
    • 7.10 LAB Boosting in Python 00:00:00
    • 7.11 Conclusion 00:00:00
    • RF and Boosting Quiz Unlimited
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