Project1
Loan Granting and Status Prediction
Supporting Enterprises Around the Globe
Project2
Diabetes Prediction
Target Audience

Any one who wants to start his journey in Machine Learning domain

Anyone who wants a great handson experience in Machine Learning
Prerequisites

Basic Python programming knowledge is required
Machine Learning  Demos and Projects
Price INR 999/
Course Curriculum

2
Prerequisite Learning References
 Lesson Introduction
 1. What is Machine learning
 2. Types of Machine Learning
 3. How industries are benefiting using data science or AI
 4. Why machine learning is the future
 5. Stages of analytics

3
Demo1
 Title  Hypothesis testing

4
Demo2
 Title  Linear Algebra
 Resource  Code files and Datasets

5
Demo3
 Title  Normal Distribution
 Resource  Code files and Datasets

6
Learning References  Demo1,2,3
 Lesson Introduction
 1. Introduction of maths and statistics in ML
 2. Linear Algebra
 3. Matrix
 4. Matrix Inverse
 5. Orthogonal Matrix
 6. Traspose of matrix
 7. Dot Product of matrices
 8. Scalars and Vectors
 9. Tensors
 10. What is Descriptive Statistics
 11. Type of data
 12.Categorical Variable and its visualization
 13. Numerical variable and frequency distribution
 14. Mean median and Mode
 15. Skewness, Kurtosis and its type
 16. Standard deviation and coefficient of variance
 17. Correlation coefficient
 18. Covariance
 19. Distribution
 20. Normal distribution
 21. Standard Normal distribution
 22. Standard error
 23. Central limit theorem
 24. Random Variable
 25. Hypothesis Testing
 26. Inferential Statistics
 27. Type I and II error
 28. Rejection region of null hypothesis
 29. Ttest, ztest,ANOVA, Chisquare
 30. Feature selection using hypotheis testing (Ttest, ztest,ANOVA, Chisquare)
 31. Mutual Information
 32. pvalue

7
Demo4
 Title  Random Forest  Part 1
 Resource  Code files and Datasets

8
Demo5
 Title  Random Forest  Part 2
 Resource  Code files and Datasets

9
Demo6
 Title  Supervised Regression
 Resource  Code files and Datasets

10
Demo7
 Title  Unsupervised Algorithms
 Resource  Code files and Datasets

11
Learning References Demo4,5,6,7
 Lesson Introduction
 1. Supervised and Unsupervised Learning
 2. Linear Regression
 3. Multiple Linear Regression
 4. Time Series Forecasting
 5. Logistic Regression
 6. SVM
 7. Decision Tree
 8. Naïve Bayes
 9. Concept of Ensemble
 10. Bagging
 11. Boosting
 12. Stacking
 13. Clustering Concepts
 14. KMeans
 15. Hierarchical Clustering

12
Demo8
 Title  Statistical Testing
 Resource  Code files and Datasets

13
Learning References  Demo8
 Lesson Introduction
 1. Performance Analysis for Classification problem
 2. ROC Curve
 3. Model Specification
 4. Confusion Matrix
 5. Accuracy, Recall, Precision and F1 Score
 6. How to handle overfitting and underfitting
 7. MSE,MAE, RMSE,Rsquare, Adjusted Rsquare
 8. Grid Search and random Search
Contact Us
FAQ

After signing up for the course, after how much time would I get access to the Learning Content?
As soon as you signedup, you will have full access to the complete selfpaced content.

How my doubts will be resolved?
There is a discussion forum attached to each course in your LMS. You can post your questions and our expert(s) will answer the queries.

For how long do I have access to the course material?
The training course content is available to you for lifetime.
Our Students Love Us.
Vishal Agnihotri
This is my first experience to online learning from learnkarts. The course was very engaging and the support provided was awesome. Overall, it is was a great learning experience and it helped me to get job in Python.
Yana Sri
The instructor of the training explained all the doubts patiently. It is very easy to learn from anywhere without any problem. Online forum support is excellent.
Ankit Vohra
The project was very good. Highly recommend this for anyone looking to learn Python.
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