Top Machine Learning Online Courses in 2021 – Artificial Intelligence and Machine Learning models have a bright future in the international market for technology. They are smart enough to work in many industries. Online courses are now available with certificates.
Online courses in machine learning are available for both students and professionals who want to improve their skills. The online courses are gaining widespread recognition, in addition to the traditional five engineering degrees.
Let’s look at the top online courses in machine-learning for applied scientists, engineers, researchers and machine learning teachers.
Top Machine Learning Online Courses Solely For You In 2021
Coursera’s Machine Learning Online Course is one of the most popular in machine learning. This course covers machine learning specializations, Artificial Intelligence, and data mining. It also includes statistical pattern recognition.
The curriculum includes unsupervised learning, supervision, and decent machine learning procedures with Artificial Intelligence. You will find many case studies and applications for creating robots, text understanding, machine vision, and other areas.
Online learners can choose flexible deadlines that work around their schedules. The machine learning course on Coursera covers linear regression with one variable, line regression with multiple variables, and linear algebra review.
It also includes logistic regression, logistic regression, regularization, Octave or Matlab training, and logistic regression. Andrew Ng is the instructor, and Stanford University teaches the course.
Also Read: Best Data Science Books to Read in 2021
Machine Learning A-Z: Hands-on Python & R In Data Science from Udemy
This course teach aspirants how to create machine learning algorithms in Python or R using two Data Science professionals. This course will give you a strong intuition about machine learning models and robust analysis. You will also learn about reinforcement learning, NLP, dimension reduction, and other topics.
The 45-section curriculum includes 320 lectures and 73 articles. It takes approximately 45 hours. Regression, data processing K-NN and SVM are the ten sections. They also include reinforcement learning, para tuning, deep learning with Artificial Intelligence, and reinforcement learning. You can also download Python and R code templates that are applicable to real-world problems.
AWS Certified Machine Learning Speciality from Udemy
Udemy’s AWS Certified Machine Learning Speciality Course offers a hands-on AWS SageMaker course and practice tests.
Learn how to deploy, optimize, integrate, and train machine learning (ML in AWS Cloud) and ready-to-use Artificial Intelligence capabilities in 28 sections. Each section has 240 lectures and 92 articles.
The course lasts 17.5 hours. This specialization course in machine learning (ML) includes machine learning notions, SageMaker housekeeping, and model performance evaluation, SageMaker overview, emerging Artificial Intelligence trend, XGBoost, as well as SageMaker service overview.
Google’s Machine Learning Crash Course is regarded as one of the most popular online courses. It provides a practical introduction to machine learning (ML) and is highly rated. This self-study guide includes a series of lessons through real-world case studies and video lectures as well as practical exercises. This online machine learning (ML) course includes more than 30 exercises and 35 lessons. It takes 15 hours. Before enrolling in this crash class, there are three phases.
- A one-hour self-study course
- An introduction to machine learning problem framing
- The pandas ultraquick tutorial collab exercise
- The NumPy ultraquick tutorial collab exercise for adequate knowledge
Simplilearn’s Machine Learning Certification Course offers a fascinating branch in Artificial Intelligence. It includes 58 hours of learning, interactive labs, and four projects (Amazon, Uber, Mercedes-Benz, and IDB), as well as over 25 exercises and mentoring.
This specialization course in machine learning (ML) will enable you to immediately become a successful machine learning engineer.
This curriculum covers creating algorithms using unsupervised and supervised learning, real-time data, regression, as well as other topics. For enthusiastic students, there are three options: self-paced learning, corporate training, and online Bootcamp.