Machine learning engineers’ demand is exponentially increasing as companies want to execute and make ML a pivot feature in their products, with Machine Learning being a modern AI field. The profession is in demand and is on the list of the best jobs.
1. Machine Learning on Google Cloud Platform Specialization
Comprises of 5 courses will take you from an overview of the value of Machine Learning to the lectures about creating ML models. The program consists of basic level lessons and covers what machine learning is capable of, followed by classes that concentrate on Tensorflow, which is an open-source machine learning structure. There are also many hands-on events to enhance the accuracy of ML.
Registration: Every two months
Course Duration: 9 weeks
Mode of Teaching: Online
Pre-requisites: Computer science background.
Highlights
- The course covers everything from basics to what kind of problems machine learning can solve.
- Teaches to build machine learning models that scale in TensorFlow.
- Teaches to combine the right combination of parameters
- Get hands-on labs possible with the Google cloud platform
- Earn a Specialization Certificate.
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2. Professional Certificate Program in ML and AI
This course is recommended for professionals as well as undergraduates to grow their careers. The course assures businesses and individuals to obtain an education and basic training to be prosperous in the AI-powered future. The certificate provides the best practices and knowledge required to put the organization at the fore of the AI revolution. The MIT faculty experts present participants to the most developed breakthroughs in cutting-edge technologies, research, and other practices used for creating advanced AI-systems. The program provides the basis of knowledge that can be set to immediate use to help people and organizations promote cognitive technology.
- Registration: May 2020
- Course Duration: This can vary
- Mode of Teaching: Online
- Pre-requisites: Bachelor’s degree in computer science, statistics, physics, or electrical engineering.
Highlights
- Personal training from leading industry practitioners.
- Learn essential concepts and skills needed to improve practical AI systems.
- Apply industry-relevant, cutting-edge knowledge
- Interact with an experienced group of peers from around the globe.
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3. Professional Certificate in the Foundations Of Data Science
This course provides a new lens through which to explore issues. It shows us how to combine data with Python programming skills to examine problems encountered in any field. The program also supports aspiring data scientists to analyze a diverse array of real data sets. The course teaches inference as well, which helps to quantify uncertainty. Ultimately, all the information is put together and to guide prediction machine learning. The program strives to make data science available to everyone.
Registration: 2-4 months
Course Duration: 4months
Mode of Teaching: Online
Pre-requisites: This course is mainly designed for beginners who do not have any computer or statistics background
Highlights
- To draw conclusions based on incomplete information by using critical thinking.
- Python 3 programming language
- Computational thinking and skills
- To make predictions.
- To interpret data and results using a wide array of real-world examples.
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4. Machine Learning Stanford Online
The course presents an introduction to statistical pattern recognition. Distinguishes between supervised and unsupervised learning, learning theory, and reinforcement learning. Explores modern applications and designs algorithms for machines.
Registration: August -September
Course Duration: 3 months
Mode of Teaching: Online
Pre-requisites: Computer science or engineering background.
Highlights
- Basics theories
- Generative learning algorithms
- Bias tradeoff and VC dimension
- Value and policy iteration
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5. Certification of Professional Achievement in Data Science
Many courses, such as machine learning for data science, statistics, probability, and exploratory data analysis, are included in this course. This course is suitable for aspirants having former knowledge in statistics, linear algebra, probability, & calculus. The certification supports students and prepares them to expand their career prospects.
Registration: By February 15 for Fall
Course Duration: 12 months
Mode of Teaching: Online, Campus
Pre-requisites :
- Undergraduate degree
- Prior quantitative coursework
- Fundamental computer programming coursework
Highlights
- Study the basics of computational thinking by using Python.
- Learn to use probable thinking to make conclusions.
- Learn to use machine learning
Signup here.