What tools and technologies are covered in Logistic Regression with NumPy and Python?

This course covers Jupyter, NumPy, Seaborn, Python and scikit-learn.

What career roles is Logistic Regression with NumPy and Python relevant for?

The skills and techniques in Logistic Regression with NumPy and Python are most relevant for Data Scientist positions and other data science career paths.

Logistic Regression with NumPy and Python | Coursera | Data Science & AI Course

Description

In this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels.Read more.

This resource is offered by an affiliate partner. If you pay for training, we may earn a commission to support this site.

Career Relevance by Data Role

The techniques and tools covered in Logistic Regression with NumPy and Python are most similar to the requirements found in Data Scientist data science job advertisements.

Similarity Scores (Out of 100)

Logistic Regression with NumPy and Python

Description

Career Relevance by Data Role

Fast Facts

Structure

Data Science Tools and Techniques Covered

Similar Opportunities

Monitoring Machine Learning in Python

Mastering Machine Learning Algorithms using Python

Introduction to Data Science and scikit-learn in Python

Principal Component Analysis with NumPy

Machine Learning with Python: Build & Optimize

Python: Logistic Regression & Supervised ML

Predicting CTR with Machine Learning in Python

Ensemble Methods in Python

Introduction to Data Science with Python

Select Learning Source