Machine Learning with SciKit-Learn in Python
Machine Learning With SciKit
Lectures -55
Resources -2
Duration -8 hours
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Course Description
The goal of this course is to help the trainee’s expertise in working with the python based Scikit-learn library. This training will enable one to implement the concepts of Machine learning using applications by the virtue of Scikit-learn. The sole purpose of this course is to provide a practical understanding of the Scikit-learn library to the trainees. After completing this training, the trainees will be able to endure the application development that requires ML implementation using the Scikit-learn library. In this unit, you will be getting a brief introduction to the concept which includes all the basic details together with the topics that are important to understand. You will understand how this library helps the application by helping the developers in adding machine learning-based concepts. After the mid part of the video, you will be learning about the topics that fall under the court of advanced-level concepts. After this unit, you will be able to work to implement the concepts of Machine learning with the help of SciKit-Learn.
Scikit-learn can be defined as the python based library which is used to implement the concepts of machine learning in the application. It could also be explained as the predefined set of functions that is leveraged to bring the features in the application which are considered linked with machine learning. It is a library that consists of various tools for statistical modeling and machine learning. Regression, clustering, and classification are some of the most useful tools that could be found in this library. It is built on top of NumPy, SciPy, and Matplotlib which is one of the reasons behind the functions it provides. Being based on python, it will only be supported while implementing things using the python programming language. It can be used the same way as other libraries are used in python but the features it will offer will be unique and focused on Machine learning.
Goals
What will you learn in this course:
- This course has been carefully crafted in order to upgrade oneself in the genre of Data Analysis and Data Science. This training will go a long way in making you the data scientist organizations are looking out for by means of making you understand deeper concepts of Machine Learning!
Prerequisites
What are the prerequisites for this course?
- This course is open to all who want to master working with this library. We have developed the course in a manner so that I could have something for any sort of audience. Students who want to grow their career in python and want to learn about this library can be the best target audience for this course.
- Developers who are working in other programming languages and want to jump to Python to begin working with Machine learning can be the best target audience for this course. They will be learning about this library in a very detailed manner and will also learn how to implement this in python.
- Educators who are training folks in python or machine learning can also be the best target audience for this Scikit-learn Training. They will be learning about this library very deeply and will be able to deliver their understanding to their trainees.
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
2 Lectures
- Introduction to Machine Learning 05:34 05:34
- Advantages and Disadvantages of Machine Learning 07:40 07:40
NumPy
2 Lectures
NumPy Array
10 Lectures
Indexing Arrays of Arrays
3 Lectures
Matlplotlib
3 Lectures
Pandas
8 Lectures
Scikit Learn
4 Lectures
Learning and Predicting
5 Lectures
Cross Validation
15 Lectures
Movie Review Analysis
3 Lectures
Instructor Details
Corporate Bridge Consultancy Private Limited
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