CP-MLDS Foundation stands for “Certified Professional – Machine Learning and Data Science Foundation” certification prepared and honored by "Agile Testing Alliance".
The course is applicable for all roles and knowledge, experience & certification is consciously designed for all those who want to learning practical Machine learning and Data Science.
What is new in this version 1.1 of CP-MLDS
As per this latest version of learning objective, new modules for Clustering and NLP have been added in the program structure. At the same time, the learning structure has been modified to include more practical assignments post the 3 day training.
How is CP-MLDS useful?
Machine learning is based on algorithms that can learn from data without relying on rules-based programming.
As ever more of the analog world gets digitized, our ability to learn from data by developing and testing algorithms will only become more important for what are now seen as traditional businesses. Google chief economist Hal Varian calls this “computer kaizen.” For “just as mass production changed the way products were assembled and continuous improvement changed how manufacturing was done,” he says, “so continuous [and often automatic] experimentation will improve the way we optimize business processes in our organizations.
This is where it is clear, that we are into a computer kaizen world. A world where Machine learning and Data science algorithms are driving this self-learning and continuous learning process and bringing about a massive change in almost every industry. We have machine learning and data science now being used in banking, insurance, health care, sports, manufacturing, smart cities, Iot Solutioning, Automotive, Aviation, Shipping and every other industry for that matter.
Machine learning and Data science need thus has increased multifold in past few years and would keep on increasing. At the same time there is a dearth of experienced professionals who know Machine learning and Data Science. There is no dearth of machine learning and data science programs where folks would need to spend infinite amount of time and efforts to acquire this knowledge. The challenge is for working professionals to spend so much time. Most often this rigor is lost over few weeks or months.
This program solves this issue addresses two basic needs
- Practical tool-based Machine learning and Data Science exposure for every working professional
- Allow working professionals to acquire this knowledge in the most agile manner
Am I Eligible?
There are no pre-requisites for this certification program except having some prior knowledge of any programming language and basics of mathematics and statistics. Program is Python driven and having prior knowledge of Python would be an advantage.
What is the Training and Certification Exam structure?
CP-MLDS is one of the only FastTrack track program specially designed to Reskill / Upskill working professionals.
The Training and Certification structure is composed of
- a. Lab sessions – 3 full days (24 Hrs) Instructor led fully hands on lab sessions
- b. Project assignments and Mentoring – Post the three day training program, 2 projects assignments will be shared by the mentor. The process is designed to help the participants strengthen their learnings.
- c. Certification Exam. The certification exam comprises of one Theory Section (40 Marks) and one Practical section (60 Marks). Theory section is of 1 hour and practical section is of 2 hours. Getting 60% in both the sections of exam is necessary to get the CP-MLDS certificate.
- d. The training program (Part a and b above) is not mandatory for someone to go for CP-MLDS certification.
e. Thus certification exam can be taken by following either of the two routes –
- • Route 1 - by going through a formal training (step a and step b) and then going for the certification exam.
- • Route 2 – This is a direct route which allows someone to appear for the exam and get the CPMLDS (Foundation) certification after passing the exam. This route is recommended for someone already familiar with the subject.