What We Offer?

  • 8 Demos and 2 Industry standard project with self-paced solution explainer

  • Code files and Datasets

  • Unique story style learning

  • Self-paced Highly structured Learning references

Demos

  • Hypothesis Testing

  • Linear Algebra

  • Normal Distribution

  • Random Forest - Part-1

  • Random Forest - Part-2

  • Supervised Regression

  • Unsupervised Algorithms

  • Statistical Testing

Project-1

Loan Granting and Status Prediction

Your work is to study customer data and create a machine learning model that can determine whether or not to grant the loan based on likelihood of the loan being repaid.

Supporting Enterprises Around the Globe

Project-2

Diabetes Prediction

You task is to study the historical data and find out whether the patient has diabetes based upon their diagnosed measurements.

Target Audience

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

  • Anyone who wants a great hands-on experience in Machine Learning

Pre-requisites

  • Basic Python programming knowledge is required

Machine Learning - Demos and Projects

Price INR 999/-

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Course Curriculum

  • 2

    Pre-requisite 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

    Demo-1

    • Title - Hypothesis testing
  • 4

    Demo-2

    • Title - Linear Algebra
    • Resource - Code files and Datasets
  • 5

    Demo-3

    • Title - Normal Distribution
    • Resource - Code files and Datasets
  • 6

    Learning References - Demo-1,2,3

    • 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. T-test, z-test,ANOVA, Chi-square
    • 30. Feature selection using hypotheis testing (T-test, z-test,ANOVA, Chi-square)
    • 31. Mutual Information
    • 32. p-value
  • 7

    Demo-4

    • Title - Random Forest - Part 1
    • Resource - Code files and Datasets
  • 8

    Demo-5

    • Title - Random Forest - Part 2
    • Resource - Code files and Datasets
  • 9

    Demo-6

    • Title - Supervised Regression
    • Resource - Code files and Datasets
  • 10

    Demo-7

    • Title - Unsupervised Algorithms
    • Resource - Code files and Datasets
  • 11

    Learning References -Demo-4,5,6,7

    • 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. K-Means
    • 15. Hierarchical Clustering
  • 12

    Demo-8

    • Title - Statistical Testing
    • Resource - Code files and Datasets
  • 13

    Learning References - Demo-8

    • 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,R-square, Adjusted R-square
    • 8. Grid Search and random Search
  • 14

    Project-1

  • 15

    Project-2

FAQ

  • After signing up for the course, after how much time would I get access to the Learning Content?

    As soon as you signed-up, you will have full access to the complete self-paced 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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