0
  • LOGIN

  • No products in the cart.

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 model, 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, Reinforced Learning, Random Forests and Boosting
  • Part 3   – Machine Learning Projects using Python
    • Apply all your learning into projects on problems from various domains.
    • Full comprehensive DataScience 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
Model selection and cross validation Quiz 00:10:00
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

Course Reviews

N.A

ratings
  • 5 stars0
  • 4 stars0
  • 3 stars0
  • 2 stars0
  • 1 stars0

No Reviews found for this course.

Related Courses

TAKE THIS COURSE
  • ₹5,999
  • UNLIMITED ACCESS
160 STUDENTS ENROLLED