Introduction

Introduction to machine learning

Data preprocessing

Untitled item

First classifier: Logistic regression

Model evaluation and analysis

More classifiers

Hyperparameter Tuning

Regression

Conclusion and sklearn documentation

Apply Machine Learning in your projects through scikit-learn

Scikit-learn is an open source machine learning library that supports supervised and unsupervised learning. Scikit-Learn is characterized by a clean, uniform, and streamlined API. It also provides various tools for model fitting, data preprocessing, model selection and evaluation, and many other built-in machine learning algorithms and models. In this course, I provide a basic introduction to scikit learn and machine learning.
You do not need to have a background in machine learning. However, knowledge of python is required for this course.
By the end of this course, you should be able to apply basic ML models in your application using scikit-learn.

I graduated from BITS Pilani Goa Campus in 2015 with B. E. (Hons.) Computer Science and M. Sc. (Hons.) Mathematics. After graduation, I worked with Google from 2015 to 2020 with operations research team. With the operations research team I worked as a developer of Google OR-Tools. I Left Google in 2020 to start my PhD under the supervision of Prof. Andrea Lodi and Prof. Guy Desaulniers in applied mathematics.



My research interests:

Discrete optimization

Column Generation

Machine Learning.

Krunal Patel

Linear Algebra for Machine Learning

Introduction to scikit learn

Mr J Conlan

Certificate Details

Introduction to scikit learn

Course Goals

Krunal Patel