Udemy Coupon For Machine Learning From Basic to Advanced Course
Interested in the field of Machine Learning? Then this course is for you!
This course has been designed by Code Warriors the ML Enthusiasts so that we can share our knowledge and help you learn complex theories, algorithms, and coding libraries in a simple way.
We will walk you step-by-step into the World of ML. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
This course is fun and exciting, but at the same time, we dive deep into Machine Learning. It is structured the following way:
- Part 1 – Data Preprocessing
- Part 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression.
- Part 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
- Part 4 – Clustering: K-Means, Hierarchical Clustering.
And as a bonus, this course includes Python code templates which you can download and use on your own projects.
Who this course is for:
- Anyone interested in ML.
- Students who have at least high school knowledge in math and who want to start learning ML.
- 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 ML.
- 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 people who want to create added value to their business by using powerful ML tools.
WHAT WILL YOU LEARN IN THIS COURSE:
- Master ML on Python
- Make accurate predictions
- Make robust ML models
- Use Machine Learning for personal purpose
- Have a great intuition of many ML models
- Know which ML model to choose for each type of problem
- Use SciKit-Learn for ML Tasks
- Make predictions using linear regression, polynomial regression, and multiple regression
- Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, etc.