Certified Machine Learning Professional (CMLP)
ABOUT CERTIFICATION
GSDC’s Certified Machine Learning Professional gives learners a broad introduction and all aspects of machine learning. Machine learning is everywhere nowadays because we are businesses wants the machine to learn to behave like human but in an efficient way.
After getting this qualification you will be able to use the probability concepts and statistical methods to design and build a core machine learning algorithm, Also candidates will learn two types of ML which are supervised and unsupervised.
Human beings are facing both types of ML algorithms every day like a spam filter in your mail or voice recognition from your Smartphone which comes under supervised ML. In unsupervised ML you will learn how ecommerce platform shows you products you want to buy.
OBJECTIVES
Clear understanding of:
- You will learn Machine Learning concepts and future scope.
- Learn about deep learning fundamentals.
- Learn Supervised and Unsupervised learning.
- You will learn to design Machine Learning algorithms.
TARGET AUDIENCE
- IT Professionals
- Software Developers
- Process Managers
- Project Managers
- Data Analysis Professionals
- Web Developers
- Application Developers
BENEFITS
- Showcase your expertise.
- Prove your Machine Learning skills and understanding.
- Implement your skills in your organization.
- Scale up your career.
- You will become a part of this industrial revolution.
PRE-REQUISITES
- There are no pre-requisites for getting this certification.
- Basic knowledge of programming and statistics will be beneficial.
EXAMINATION
- Multiple-choice exam of 40 marks.
- You need to acquire 26+ marks to clear the exam.
- In case the Participant failed then they will be free 2nd attempt.
- Re-examination can be taken up to 30 days from the date of the 1st exam attempt.
Course Features
- Lectures 28
- Quizzes 0
- Duration 14 hours
- Skill level All levels
- Language English
- Students 0
- Assessments Yes
-
1. Introduction to Machine Learning
- What is Machine Learning – Iterative learning from data and What s old is new again
- Definition of Big Data
- Big Data in Context with Machine Learning
- The Need to Understand and Trust your Data
- Hybrid Cloud And Its importance
- Leveraging the Power of Machine Learning – Descriptive analytics & Predictive Analytics
- When Statistics and Data Mining Teams Up with Machine Learning
- Machine Learning in Context
- Approaches towards Machine Learning – Supervised learning, Unsupervised learning & Reinforcement learning
- Neural networks and deep learning
-
2. Applying Machine Learning to Business Needs
-
3.Looking Inside Machine Learning
-
4. Getting Started With Machine Learning
-
5. Learning Machine Skills
-
6. Using Machine Learning To Provide Solutions With Business Problems
-
7.Ten Predictions On The Future Machine Learning
No items in this section
