Introduction to ML Classification Models using scikit-learn udemy course free download
What you'll learn:
- Understand how to interpret the result of Logistic Regression model and translate them into actionable insight
- Learn the linear discriminant analysis and K-Nearest Neighbors technique
- Learn how to solve real life problem using the different classification techniques
- Preliminary analysis of data using Univariate analysis before running classification model
- Predict future outcomes basis past data by implementing Machine Learning algorithm
- Indepth knowledge of data collection and data preprocessing for Machine Learning logistic regression problem
- Course contains a end-to-end DIY project to implement your learnings from the lectures
- Basic statistics using Numpy library in Python
- Data representation using Seaborn library in Python
- Classification techniques of Machine Learning using Scikit Learn and Statsmodel libraries of Python
Requirements::
- Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same
Description:
This course will give you a fundamental understanding of Machine Learning overall with a focus on building classification models. Basic ML concepts of ML are explained, including Supervised and Unsupervised Learning; Regression and Classification; and Overfitting. There are 3 lab sections which focus on building classification models using Support Vector Machines, Decision Trees and Random Forests using real data sets. The implementation will be performed using the scikit-learn library for Python.
The Intro to ML Classification Models course is meant for developers or data scientists (or anybody else) who knows basic Python programming and wishes to learn about Machine Learning, with a focus on solving the problem of classification.
Who this course is for:
- Developers and data scientists who wish to learn how to build classification models in ML
Course Details:
- 2 hours on-demand video
- 1 article
- 2 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of completion