This course has been created, designed and assembled by professional Data Scientists who have worked in this field for nearly a decade. We can help you understand the complex machine learning algorithms while keeping you grounded to the implementation on real business and data science problems.
We will let you feel the water and coach you to become a full swimmer in the realm of data science and Machine Learning. Every tutorial will increase your skill level by challenging your ability to foresee, yet letting you improve upon self.
We are sure that you will have fun while learning from our tried and tested structure of course to keep you interested in what’s coming next.
Here is how the course is going to work:
- Part 1 – Introduction to Python Programming.
- This is the part where you will learn basic of python programming and familiarize yourself with Python environment.
- Be able to import, export, explore, clean and prepare the data for advance modeling.
- Understand the underlying statistics of data and how to report/document the insights.
- Part 2 – Machine Learning using Python
- Learn, upgrade and become expert on classic machine learning algorithms like Linear Regression, Logistic Regression and Decision Trees.
- Learn which algorithm to choose for specific problem, build multiple models, learn how to choose the best model and be able to improve upon it.
- Move on to advance machine learning algorithms like SVM, Artificial Neural Networks, Random Forests and Boosting.
- Part 3 – Machine Learning Projects using Python
- Apply all your learning into projects on problems from various domains.
- Full comprehensive Data Science pipeline from raw data to model building and making final predictions.
Course Curriculum
| Part 1: Python Programming for Data Science | |||
| Session 1 - Introduction to Python | |||
| Handout – Introduction to Python | 00:00:00 | ||
| 1.1 Python Intoduction and IDE | FREE | 00:00:00 | |
| 1.2 Basic Commands in Python | FREE | 00:00:00 | |
| 1.3 Objects, Number and Strings | 00:00:00 | ||
| 1.4 Objects, List, Tuples and Dictionaries | 00:00:00 | ||
| 1.5 If_else and For_loop | 00:00:00 | ||
| 1.6 Functions and Packages | 00:00:00 | ||
| 1.7 Important Packages | 00:00:00 | ||
| 1.8 End Note | 00:00:00 | ||
| Python Quiz Unit 1 | Unlimited | ||
| Session 2 - Data Handling in Python | |||
| Handout – Data Handling in Python | 00:00:00 | ||
| 2.1 Introduction to DataHandling | FREE | 00:00:00 | |
| 2.2 Basic Commands and Checklist | FREE | 00:00:00 | |
| 2.3 Subsetting the Dataset | 00:00:00 | ||
| 2.4 Calculated Field Sort Duplicates | 00:00:00 | ||
| 2.5 Merge and Exporting | 00:00:00 | ||
| Python Quiz Unit 2 | Unlimited | ||
| Session 3 - Basic Statistics, Graphs and Reports in Python | |||
| Handout – Basic Statistics, Graphs and Reports in Python | 00:00:00 | ||
| 3.1 Basic Statistics and Sampling | FREE | 00:00:00 | |
| 3.2 Discriptive Statistics | 00:00:00 | ||
| 3.3 Percentile and Boxplot | 00:00:00 | ||
| 3.4 Graphs Plots and Conclusion | 00:00:00 | ||
| Python Quiz Unit 3 | Unlimited | ||
| Session 4 - Data Cleaning and Treatement | |||
| Handout – Data Cleaning and Treatement | 00:00:00 | ||
| 4.1 Data Cleaning Intro and Model Building Cycle | FREE | 00:00:00 | |
| 4.2 Model Building Cycle | FREE | 00:00:00 | |
| 4.3 Data Cleaning Case Study | 00:00:00 | ||
| 4.4 Step1 Basic Content of Dataset | 00:00:00 | ||
| 4.5 Variable level Exploration Catagorical | 00:00:00 | ||
| 4.6 Reading Data Dictionary | 00:00:00 | ||
| 4.7 LAB – Step2 Catagorical Variable Exploration | 00:00:00 | ||
| 4.8 Step3 Variable Level Exploration – Continuous | 00:00:00 | ||
| 4.8.1 LAB – Step3 Variable Level Exploration | 00:00:00 | ||
| 4.9 Data Cleaning and Treatments | 00:00:00 | ||
| 4.10 Step4 Treatment – Scenario1 | 00:00:00 | ||
| 4.10.1 LAB – step4 Treatment – scenario 1 | 00:00:00 | ||
| 4.11 Step4 Treatment – Scenario2 | 00:00:00 | ||
| 4.11.1 LAB – step4 Treatment – scenario 2 | 00:00:00 | ||
| 4.12 Data Cleaning – Scenario 3 | 00:00:00 | ||
| 4.12.1 LAB – Data Cleaning – Scenario 3 | 00:00:00 | ||
| 4.13 Some Other Variables | 00:00:00 | ||
| 4.14 Conclusion | 00:00:00 | ||
| Part 2: Machine Learning using Python | |||
| 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 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 | ||
| Session 3 - Decision Trees | |||
| 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 | ||
| Session 4 - Model Selection and Cross Validation | |||
| 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 | ||
| Session 5 - Neural Networks | |||
| 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 | ||
| Session 6 - SVM | |||
| 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 | ||
| Session 7 - Random Forest and Boosting | |||
| 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 | ||
| Part 3 – Machine Learning Projects using Python | |||
| Face Recognition | 00:00:00 | ||
| Human Activity Recognition Using Smartphones | 00:00:00 | ||
| Object Recognition in Images | 00:00:00 | ||
| Ecommerce Product Classification | 00:00:00 | ||
| Direct Mail Marketing Project in Python | 00:00:00 | ||
| Bank Tele Marketing Project in Python | 00:00:00 | ||
| Consumer Loan Default Prediction | 00:00:00 | ||
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