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 Curriculum
| Session 1 - Linear Regression | |||
| 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 | ||
| Session 2 - Logistic Regression | |||
| Handout – Logistic Regression | 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 | 00:00:00 | ||
| 2.6 individual Impact of Variables | 00:00:00 | ||
| 2.7 Model Selection | 00:00:00 | ||
| 2.8 Conclusion | 00:00:00 | ||
| Logistic Regression Quiz | Unlimited | ||
| Session 3 - Decision Trees | |||
| Handout – Decision Trees | 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 | ||
| Session 4 - Model Selection and Cross Validation | |||
| Handout – Model Selection and Cross Validation | 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 | ||
| Model selection and cross validation Quiz | 00:10:00 | ||
| Session 5 - Neural Networks | |||
| Handout – Neural Networks | 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 | ||
| Session 6 - SVM | |||
| Handout – SVM | 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 | ||
| Session 7 - Random Forest and Boosting | |||
| Handout – Random Forest and Boosting | 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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