Course ,

Machine Learning Course

Learn this exciting branch of Artificial Intelligence and gain skills needed to become a successful Machine Learning Engineer today.
Course
Curriculum

The goal of the Machine Learning Course Free is to provide machine learning engineers, data scientists, and artificial intelligence professionals with a strong foundation and employable abilities. Learn by doing in the areas of text mining, time series, supervised and unsupervised learning, and data preprocessing. Anyone wishing to learn machine learning and launch their profession will find the curriculum to be suitable.

What will you learn in Machine Learning Course Free?

  1. Machine Learning Introduction
  2. Statistics and Mathematics
  3. Linear Algebra
  4. Matrix
  5. Matrix Inverse
  6. Orthogonal Matrix
  7. Transpose of matrix
  8. Dot Product of matrices
  9. Scalars and Vectors
  10. Tensors
  11. Basic Statistics – Covariance, Distribution etc.
  12. Advanced Statistics – Hypothesis Testing, Inferential Statistics, Type I and II error ,T-test, z-test, ANOVA, Chi-square
    feature selection using hypothesis testing (T-test, z-test, ANOVA, Chi-square), p-value
  13. Type of Machine learning algorithms
  14. Supervised ML Algorithm Techniques
  15. Unsupervised ML Algorithm Techniques
  16. Model Evaluation techniques

Requirement

A basic understanding of Python is required.

Description

A warm welcome to Machine learning with Python training course.

So why Machine learning?
As per Glassdoor, the average salary of a Machine learning engineer is 11L/annum and there are 1 million+ job vacancies.
Applications using Machine learning have become integrated part of our life without us even realizing that. Whether its google search, Youtube recommending you which videos to watch, or Ola/Uber giving you fair estimation. The examples are countless.

This Machine Learning Training using Python helps you gain expertise in various machine learning algorithms such as Logistic Regression, SVM, Decision Tree, Naïve Bayes, Concept of Ensemble, Bagging, Boosting, Stacking, K-Means, Hierarchical Clustering, time series concepts. You will also gain expertise on Model Evaluation techniques like ROC Curve, Model Specification, Confusion Matrix, Accuracy, Recall, Precision and F1 Score, How to handle overfitting and underfitting, MSE, MAE, RMSE,R-square, Adjusted R-square, Grid Search and random Search and many more.
This course will help you to kick-start your career as a Machine learning engineer. It is based on python so requires understanding of that.

Python refresher concepts that are required for understanding this course has been covered in this course itself.

Who is this course for?

1. College Freshers
2. Anyone who is willing to learn Machine learning in simple and easy steps using Python as a programming language

Course Content

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Course Includes

  • 6 Lessons
  • 73 Topics
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