Learn to create Machine Learning Algorithms in Python Data Science enthusiasts. Code templates included.
What you will learn
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.
Description
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
3.Regression Miniproject
4.Classification Algorithms on the real-time dataset
5.Classification Miniproject
6.Model Fine-Tuning
7.Deployment of the ML model
English
Language
Content
Introduction
What is ML? Application & Types of ML
Data Preprocessing Techniques
What is NumPy?
Data Manipulation with Pandas
Regression
Simple Linear Regression
Multiple Linear Regression
Polynomial Regression
Support Vector Regression(SVR)
Decision Tree Regression
Random Forest Regression
Regression Mini Project
Classification
Logistic Regression
K-Nearest Neighbour
Support Vector Machine (SVM)
Kernel SVM
Naive Bayes Classification
Decision Tree Classification
Random Forest Classification
Classification Mini Project
Problems With ML
Underfitting and Overfitting
Model Selection
Cross Validation And Grid Search
Model Deployment
ML Model With Deployment
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