Learn to create Machine Learning Algorithms in Python Data Science enthusiasts. Code templates included.
☑ Master Machine Learning on Python
☑ Make accurate predictions
☑ Make robust Machine Learning models
☑ Use Machine Learning for personal purpose
☑ Have a great intuition of many Machine Learning models
☑ Know which Machine Learning model to choose for each type of problem
☑ Use SciKit-Learn for Machine Learning 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.
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 Machine Learning. 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:
You can do a lot in 21 Days. Actually, it’s the perfect number of days required to adopt a new habit!
What you’ll learn:-
1.Machine Learning Overview
2.Regression Algorithms on the real-time dataset
4.Classification Algorithms on the real-time dataset
7.Deployment of the ML model
What is ML? Application & Types of ML
Data Preprocessing Techniques
What is NumPy?
Data Manipulation with Pandas
Simple Linear Regression
Multiple Linear Regression
Support Vector Regression(SVR)
Decision Tree Regression
Random Forest Regression
Regression Mini Project
Support Vector Machine (SVM)
Naive Bayes Classification
Decision Tree Classification
Random Forest Classification
Classification Mini Project
Problems With ML
Underfitting and Overfitting
Cross Validation And Grid Search
ML Model With Deployment