Classification Models: Supervised Machine Learning in Python

Classification Models: Supervised Machine Learning in Python, available at $44.99, has an average rating of 4.5, with 22 lectures, 1 quizzes, based on 5 reviews, and has 1038 subscribers.

You will learn about Describe the input and output of a classification model Prepare data with feature engineering techniques Tackle both binary and multiclass classification problems Implement Support Vector Machines, Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors, Neural Networks, logistic regression models on Python Use a variety of performance metrics such as confusion matrix, accuracy, precision, recall, ROC curve and AUC score. This course is ideal for individuals who are Research scholars and college students or Industry professionals and aspiring data scientists or Beginners starting out to the field of Machine Learning It is particularly useful for Research scholars and college students or Industry professionals and aspiring data scientists or Beginners starting out to the field of Machine Learning.

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Summary

Title: Classification Models: Supervised Machine Learning in Python

Price: $44.99

Average Rating: 4.5

Number of Lectures: 22

Number of Quizzes: 1

Number of Published Lectures: 22

Number of Published Quizzes: 1

Number of Curriculum Items: 23

Number of Published Curriculum Objects: 23

Original Price: ₹799

Quality Status: approved

Status: Live

What You Will Learn

Describe the input and output of a classification model
Prepare data with feature engineering techniques
Tackle both binary and multiclass classification problems
Implement Support Vector Machines, Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors, Neural Networks, logistic regression models on Python
Use a variety of performance metrics such as confusion matrix, accuracy, precision, recall, ROC curve and AUC score.

Who Should Attend

Research scholars and college students
Industry professionals and aspiring data scientists
Beginners starting out to the field of Machine Learning

Target Audiences

Research scholars and college students
Industry professionals and aspiring data scientists
Beginners starting out to the field of Machine Learning

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 will teach you to implement supervised classification machine learning models in Python using the Scikit learn (sklearn) library. 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. The complete Python programs and datasets included in the class are also available for download. This course is designed most straightforwardly to utilize your time wisely. 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.

Course Curriculum

Chapter 1: Fundamentals

Lecture 1: Introduction

Lecture 2: Artificial Intelligence

Lecture 3: Machine Learning

Lecture 4: Supervised Learning

Lecture 5: Supervised Learning: Classifications

Lecture 6: Installation of Python Platform

Chapter 2: Building and Evaluating Classification ML Models

Lecture 1: Important Terminologies

Lecture 2: Support Vector Machines

Lecture 3: Support Vector Machines: Using CSV

Lecture 4: Support Vector Machines: Iris Dataset in URL

Lecture 5: Splitting Data

Lecture 6: Confusion Matrix

Lecture 7: Accuracy of Model

Lecture 8: Precision

Lecture 9: Recall (or Sensitivity)

Lecture 10: Naive Bayes

Lecture 11: Decision Tree

Lecture 12: Random Forest

Lecture 13: K-Nearest Neighbors

Lecture 14: Neural Networks

Lecture 15: AUC – ROC Curve

Lecture 16: Logistic Regression

Instructors

Karthik Karunakaran, Ph.D.
Transforming Real-World Problems with the Power of AI-ML

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3 stars: 1 votes
4 stars: 1 votes
5 stars: 3 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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