Machine Learning with SciKit-Learn with Python

Machine Learning with SciKit-Learn with Python, available at $39.99, has an average rating of 4.1, with 54 lectures, based on 125 reviews, and has 27694 subscribers.

You will learn about This Scikit-learn Training has been designed in a manner so that it can contain all the topics that the trainees have to expertise so that they can work effectively with this library. At the starting of the course, you will get to learn about Machine Learning with SciKit-Learn which is one of the important components of this course where you will be learning every single thing about SciKit-Learn. You will be getting deep exposure to python in this training. Once you are done with this course, you will be possessing an ample skillset to work efficiently with the SciKit-Learn library. This course is ideal for individuals who are 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. The 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. or The 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. or The 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. It is particularly useful for 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. The 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. or The 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. or The 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.

Enroll now: Machine Learning with SciKit-Learn with Python

Summary

Title: Machine Learning with SciKit-Learn with Python

Price: $39.99

Average Rating: 4.1

Number of Lectures: 54

Number of Published Lectures: 54

Number of Curriculum Items: 54

Number of Published Curriculum Objects: 54

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

This Scikit-learn Training has been designed in a manner so that it can contain all the topics that the trainees have to expertise so that they can work effectively with this library. At the starting of the course, you will get to learn about Machine Learning with SciKit-Learn which is one of the important components of this course where you will be learning every single thing about SciKit-Learn.
You will be getting deep exposure to python in this training. Once you are done with this course, you will be possessing an ample skillset to work efficiently with the SciKit-Learn library.

Who Should Attend

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. The 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.
The 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.
The 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.

Target Audiences

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. The 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.
The 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.
The 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.

The goal of this course is to help the trainee’s expertise 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 of 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 the 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 reason 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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to Machine Learning

Lecture 2: Advantages and Disadvantages of Machine Learning

Chapter 2: NumPy

Lecture 1: NumPy Introduction

Lecture 2: Features and Installation

Chapter 3: NumPy Array

Lecture 1: NumPy Array Creation

Lecture 2: NumPy Array Attributes

Lecture 3: NumPy Array Operations

Lecture 4: NumPy Array Operations Continue

Lecture 5: NumPy Array Unary Operations

Lecture 6: Numpy Array Splicing

Lecture 7: NumPy Array Shpe

Lecture 8: Stacking Together Different Arrays

Lecture 9: Splitting one Array into Several Smaller ones

Lecture 10: Copies and Views

Chapter 4: Indexing Arrays of Arrays

Lecture 1: NumPy Array Indexing

Lecture 2: NumPy Array Indexing Continue

Lecture 3: NumPy Array Boolean

Chapter 5: Matlplotlib

Lecture 1: Introduction to Matlplotlib

Lecture 2: Understanding Various Functions of Pyplot

Lecture 3: Multiple Figures and Subplots

Chapter 6: Pandas

Lecture 1: Intro to Pandas

Lecture 2: Intro to Pandas Continue

Lecture 3: Data Structure in Pandas

Lecture 4: Data Structure in Pandas Continue

Lecture 5: Pandas Column Select

Lecture 6: Remove Operations

Lecture 7: Pandas Arithmetic Operations

Lecture 8: Pandas Arithmetic Operations Continue

Chapter 7: Scikit Learn

Lecture 1: Introduction to Scikit Learn

Lecture 2: Supervised

Lecture 3: Unsupervised Learning

Lecture 4: Load Data Set

Chapter 8: Learning and Predicting

Lecture 1: Scikit Example Digits

Lecture 2: Digits Dataset Using Matplotlib

Lecture 3: Understading Metrics of Predicted Digits Dataset

Lecture 4: Persisting Models

Lecture 5: K-NN Algorithm with Example

Chapter 9: Cross Validation

Lecture 1: Cross Validation

Lecture 2: Cross Validation Techniques

Lecture 3: K-Means Clustering Example

Lecture 4: Agglomeration

Lecture 5: PCA Pipeline

Lecture 6: Face Recognition

Lecture 7: Face Recognition Output

Lecture 8: Right Estimator

Lecture 9: Text Data Example

Lecture 10: Extracting Features

Lecture 11: Occurrences to Frequencies

Lecture 12: Classifier Training

Lecture 13: Performance Analysis on the Test Set

Lecture 14: Parameter Tuning

Lecture 15: Language Identifcation

Chapter 10: Movie Review Analysis

Lecture 1: Movie Review Screen Stream

Lecture 2: Movie Review Screen Stream Continue

Instructors

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Rating Distribution

1 stars: 2 votes
2 stars: 3 votes
3 stars: 28 votes
4 stars: 40 votes
5 stars: 52 votes

Frequently Asked Questions

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