Classification Models: Supervised Machine Learning in Python
A Quick Way to Learn and Implement Classification AI Algorithms in Python. A Course for Beginners.
Bullstrack
Classification Models: Supervised Machine Learning in Python udemy course
A Quick Way to Learn and Implement Classification AI Algorithms in Python. A Course for Beginners.
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:
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.
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
- Ultimate Python Bootcamp For Data Science & Machine Learning
- Microservices Architecture on AWS
- Linux Command line Basics
- Unit Testing and Test Driven Development in NodeJS
Course Details:
- 1 hour on-demand video
- 27 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of completion
Classification Models: Supervised Machine Learning in Python udemy free download
A Quick Way to Learn and Implement Classification AI Algorithms in Python. A Course for Beginners.
Demo Link: https://www.udemy.com/course/supervisedlearning/
