Learn from Home Offer
SCIKIT-LEARN Course Bundle - 4 Courses in 1
This Scikit-learn Machine Learning Training includes 4 courses with 28+ hours of video tutorials and Lifetime Access and several mock tests for practice. In this course, we are going to learn about one of the libraries of Python that is mainly concerned with Machine learning and used by the developers to bring its concepts in the application. It is known as Scikit-learn and we are going to learn many machine learning algorithms and its application in this course.
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What you get in this SCIKIT-LEARN Course Bundle - 4 Courses in 1?
28+ Hours
4 Courses
Course Completion Certificates
Lifetime Access
Self-paced Courses
Technical Support
Mobile App Access
Case Studies
About SCIKIT-LEARN Course Bundle
| Courses | You get access to all 4 courses, Projects bundle. You do not need to purchase each course separately. |
| Hours | 28+ Video Hours |
| Core Coverage | The main aim of this course is to provide a wide understanding of machine learning and its application through Scikit-Learn |
| Course Validity | Lifetime Access |
| Eligibility | Anyone serious about learning Scikit-learn Machine Learning Training |
| Pre-Requisites | Basic knowledge about Python and statistics |
| What do you get? | Certificate of Completion for each of the 4 courses, Projects |
| Certification Type | Course Completion Certificates |
| Verifiable Certificates? | Yes, you get verifiable certificates for each course with a unique link. These link can be included in your resume/Linkedin profile to showcase your enhanced skills |
| Type of Training | Video Course – Self Paced Learning |
SCIKIT-LEARN Course Bundle Curriculum
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MODULE 1: Scikit-Learn Essentials Training
Courses No. of Hours Certificates Details Supervised Machine Learning in Python 8h 37m ✔ -
MODULE 2: Learning from Practicals
Courses No. of Hours Certificates Details Machine Learning ZERO to HERO - Hands-on with Tensorflow 13h 03m ✔ Project on Tensorflow - Implementing Linear Model with Python 1h 46m ✔ AI Artificial Intelligence & Predictive Analysis with Python 6h 15m ✔
Goals
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.
Objectives
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.
Course Highlights
The course will be started with eight hours long unit which has been named Machine Learning with SciKit-Learn. 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 the 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.
Machine Learning with Tensorflow will be the next important unit that we will be covering in this course. It is going to be around thirteen hours long video, where we will be learning to implement the concepts of Machine learning with the help of Tensorflow. Every single topic that is considered with Tensorflow will be covered in this unit with the help of precise examples that have been introduced to help the trainees understand the concepts easily.
Artificial Intelligence with Python will be the last unit and here we will be learning about the implementation of artificial intelligence-based concepts in the applications using python. We have added some of the sample questions in this course which has been added to fortify your understanding of the subject. Those sample questions will be the simple topics where you will need to solve them with the help of understanding that you would have gathered so far. While implementing things, you will be facing some of the issues that come up sometimes due to incorrect implementation approaches. You will be learning how to tackle or handle those issues that help your applications work smoothly. After finishing this course, you will be able to work proficiently with this library and will also be able to implement things perfectly.
Project Highlights
Project on Tensorflow – Implementing Linear Model with Python will be the next unit in this course where we will be working towards a single topic. In this project, we will be developing the project on Implementing the linear model with the help of python. All the concepts that you would have walked through this course will be used in this course to complete the project. You will also be learning about error solving in this unit. Once the project is completed, you will have a deep idea of how things work with the help of TensorFlow.
Scikit-learn Course – Certificate of Completion
What is 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 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.
What skills will you learn in this Course?
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 also be learning about Tensorflow in this course and to offer you practical exposure, we have practically presented things. Since all the topics will be based on python, you will be getting deep exposure to python in this training. Artificial intelligence is another important topic covered in this course. Once you are done with this course, you will be possessing an ample skillset to work efficiently with the SciKit-Learn library.
Pre-requisites
- Several topics or concepts are there for which you should have a basic understanding of to make the learning of this library easy for you. The very first thing is the basics of python. As this library is entirely based on python, the trainees need to have a basic understanding of the concepts of python. If you would have worked with python, you will find the concepts covered here pretty simple. The next important concept is the basics of Machine learning. With the help of this library, we will be implementing the concepts of ML. So it is very necessary to understand how it could be used. In this Scikit-learn Training, we have included all the topics that we are considering as the prerequisite here so that the trainees can brush up their understanding before beginning this training.
Target Audience
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- 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
- and want to learn about this library can be the best target audience for this course. You will be learning about python, Scikit-learn library, and Machine Learning which will eventually help them to secure a good job. 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.
FAQ’s- General Questions
How long it may take to complete this course?
This course contains four different units and collectively all of them could be finished within thirty hours. In terms of days, it may take around 15 days to complete the course. For the folks who want to master working with Scikit-learn Machine Learning, it may take months based on the expertise of the trainees in python. For the developers who are working in python, they will be able to master this library around one and half months while for the folks who are fresh to this, they may take more than two months to master working with the library.
Why should I take this Scikit-learn Training?
This course is solely based on Scikit-learn Machine Learning Training and we have ensured to cover all the topics that are directly or indirectly concerned with this. For the folks who want to learn this library, this course is like a one-stop solution as they will be learning all the concerned topics here. Additionally, the educator has explained all the topics in a very easy manner using simple language. Once you complete this course, you will be able to work with this library and will also be able to implement all the concepts concerned with the topic.
Sample Preview
Career Benefits
- Scikit-learn is one of the popular libraries that is used very extensively throughout the world to implement the concept of Machine learning. The high demand for organizations to get the robust applications developed which includes the features of machine learning has also increased the demand of python developers who have hands-on Scikit-learn libraries. The current job market is full of opportunities for the developers who are proficient in working with this library and the count of opportunities is increasing day after day. If you want to leverage this high demand to get an entice turn in your professional growth, you may want to opt for this course and begin learning about the Scikit-learn library within merely a few clicks.
Reviews
Machine Learning – Statistics Essentials
I enjoyed this Machine learning course and found the statistical approaches useful – especially the regression and cluster sampling sections. The course pacing was easy to understand, and the topics covered in a comprehensive but not over-explained way. Thanks for giving such an interesting and useful course!
Linked
Matthew Rolley
Great!
Nice introductory Machine learning certification course if you want to get a basic understanding of what Machine Learning is. You don’t need ridiculous technical chops to gain something from this Machine learning training course, it’s put together in a way that anyone can value from and truly understand it. I’d recommend it to anyone who just wants to dip their toes in the Machine Learning waters.
Linked
Ammar Khan
Machine Learning with R
The Machine learning certification covers a wide range of areas into linear and multiple linear regression and decision trees. It also covers both theory and application using R neural network, time series analysis, and gradient boosting machines. I have enjoyed learning in this Machine learning course very much and found it useful in my work.
Linked
Tsui Man Kit
Summary
This Machine learning training course is good and it shows practical examples as well. However in the first part it shows examples with not appropriate enlarge, so the commands and the outputs are not properly visible. I suggest to include more practical examples related to MatLab. The second part’s examples are visible.
Linked
