Learn to Implement Classification Models in Scikit-learn ( sklearn ) : A Python Artificial Intelligence Library
What you'll learn
Describe the input and output of a classification model
Prepare data with feature engineering techniques
Tackle both binary and multiclass classification problems
Implement and use Support Vector Machines, Naive Bayes models on Python
Implement and use Decision Tree, Random Forest models on Python
Implement and use K-Nearest Neighbors, Neural Networks models on Python
Implement and use logistic regression models on Python
Use a variety of performance metrics such as confusion matrix, accuracy, precision, recall, ROC curve and AUC score.
Requirements
Basic knowledge of Python Programming
Description
Artificial intelligence and machine learning are touching our everyday lives in more-and-more ways. There's an endless supply of industries and applications that machine learning can make more efficient and intelligent. Supervised machine learning is the underlying method behind a large part of this. Supervised learning involves using some algorithm to analyze and learn from past observations, enabling you to predict future events. This course introduces you to one of the prominent modelling families of supervised Machine Learning called Classification. This course provides the learners with the foundational knowledge to use classification models to create business insights. You will become familiar with the most successful and widely used classification techniques, such as
Support Vector Machines.
Naive Bayes
Decision Tree
Random Forest
K-Nearest Neighbors
Neural Networks
Logistic Regression
You will learn to train predictive models to classify categorical outcomes and use performance metrics to evaluate different models. The complete course is built on several examples where you will learn to code with real datasets. By the end of this course, you will be able to build machine learning models to make predictions using your data. Get ready to do more learning than your machine! Happy Learning.
Career Growth
Employment website Indeed has listed machine learning engineers as #1 among The Best Jobs in the U.S., citing a 344% growth rate and a median salary of $146,085 per year. Overall, computer and information technology jobs are booming, with employment projected to grow 11% from 2019 to 2029.
Who this course is for
Research scholars and college students
Industry professionals and aspiring data scientists
Beginners starting out to the field of Machine Learning

Homepage

https://www.udemy.com/course/supervisedlearning/