This course is for you If you are being fascinated by the field of Machine Learning?
Basic Course Description
This course is designed to cover one of the most interesting areas of machine learning called classification. I will take you step-by-step in this course and will first cover the basics of MATLAB. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically we will be looking at the MATLAB toolbox called statistic and machine learning toolbox.We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor Naive Bayes Discriminant Analysis Decision Tress Support Vector Machines Error Correcting Ouput Codes and Ensembles. Following that we will be looking at how to cross validate these models and how to evaluate their performances. Intuition into the classification algorithms is also included so that a person with no mathematical background can still comprehend the esesential ideas. The following are the course outlines.
- Sgement 1: Instructor and Course Introduction
- Segment 2: MATLAB Crash Course
- Segment 3: Grabbing and Importing Dataset
- Segment 4: K-Nearest Neighbor
- Segment 5: Naive Bayes
- Segment 6: Decision Trees
- Segment 7: Discriminant Analysis
- Segment 8: Support Vector Machines
- Segment 9: Error Correcting Ouput Codes
- Segment 10: Classification with Ensembles
- Segment 11: Validation Methods
- Segment 12: Evaluating Performance
At the end of this course
- You can confidently implement machine learning algorithms using MATLAB.
- You can perform meaningful analysis on the data.
Your Benefits and Advantages:
- You receive knowledge from a PhD. in Computer science (machine learning) with over 10 years of teaching and research experience In addition to 15 years of programming experience and another decade of experience in using MATLAB
- The instructor has 6 courses on Simpliv on MATLAB including a best seller course.
- The overall rating in these courses are (4.5/5)
- You have lifetime access to the course
- You have instant and free access to any updates i add to the course
- You have access to all Questions and discussions initiated by other students
- You will receive my support regarding any issues related to the course
- Check out the curriculum and Freely available lectures for a quick insight.
Who should take this course
Who is the target audience?
Researchers Entrepreneurs Instructors and Teachers College Students Engineers Programmers and Simulators
Course Completion Certificate
What you will learn:
- Use machines learning algorithms confidently in MALTAB
- Build classification learning models and customize them based on the datasets
- Compare the performance of different classification algorithms
- Learn the intuition behind classification algorithms
- Create automatically generated reports for sharing your analysis results with friends and colleague
- Instructor and Course Introduction
- MATLAB Crash Course
- Grabbing and Importing a Dataset
- K-Nearest Neighbor
- Naive Bayes
- Decision Trees
- Discriminant Analysis
- Support Vector Machines
- Error Correcting Output Codes
- Classification with Ensembles
- Validation Methods
- Performance Evaluation
About Course Provider
Simpliv LLC, a platform for learning and teaching online courses. We basically focus on online learning which helps to learn business concepts, software technology to develop personal and professional goals through video library by recognized industry experts or trainers.
With the ever-evolving industry trends, there is a constant need of the professionally designed learning solutions that deliver key innovations on time and on a budget to achieve long-term success.
Simpliv understands the changing needs and allows the global learners to evaluate their technical abilities by aligning the learnings to key business objectives in order to fill the skills gaps that exist in the various business areas including IT, Marketing, Business Development, and much more.
How to enroll?
You can book the course instantly by paying on GulfTalent.