Become Certified Machine Learning Professional

From beginning to pro level

This course is designed for beginner to pro-level machine learning engineers. In this course, we will start from the very basics and will go to an advanced level. I have designed this course in a way that everybody can understand easily. Additionally, I have given tutorials for python,Numpy and matplotlib because these are very important for machine learning. In the end, we will do some practical projects and will apply all the concepts which we have learnt so far. We will see these concepts

What you’ll learn

  • You will get deep knowledge about Machine Learning.
  • You will get overview of Natural Language Processing.
  • You will be able to apply machine learning models according to problem.
  • You will be able to manage and clean data before applying machine learning.

Course Content

  • Introduction –> 24 lectures • 2hr 31min.
  • Data Preprocessing –> 4 lectures • 24min.
  • Supervised learning : Regression models –> 7 lectures • 32min.
  • Other Models –> 8 lectures • 59min.
  • Unsupervised Learning –> 12 lectures • 1hr 23min.

Become Certified Machine Learning Professional

Requirements

  • No prerequisties . I will teach everything including python also.

This course is designed for beginner to pro-level machine learning engineers. In this course, we will start from the very basics and will go to an advanced level. I have designed this course in a way that everybody can understand easily. Additionally, I have given tutorials for python,Numpy and matplotlib because these are very important for machine learning. In the end, we will do some practical projects and will apply all the concepts which we have learnt so far. We will see these concepts

  1. Linear Regression(Single Variable and Multiple Variable)
  2. Logistic Regression(Single Variable and Multiple Variable)
  3. Decision Tree
  4. Random Forest
  5. Naive-Bayes
  6. Support Vector Machine
  7. Ensemble Learning
  8. K-fold Method
  9. Unsupervised Learning
  10. Feature Engineering
  11. Deep Learning
  12. Outlier Detection
  13. One Hot Encoding
  14. Basics of python
  15. Basics of NumPy
  16. Basics of Pandas
  17. Basics of Data-Science
  18. Basics of matplotliband much more

Who this course is for:

  • Anyone interested in Machine Learning.
  • Students who have at least high school knowledge in math and who want to start learning Machine Learning.
  • Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
  • Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
  • Any students in college who want to start a career in Data Science.
  • Any data analysts who want to level up in Machine Learning.
  • Any people who are not satisfied with their job and who want to become a Data Scientist.
  • Any people who want to create added value to their business by using powerful Machine Learning tools.
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