
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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