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Top Machine Learning Courses to Pursue

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Machine learning

Top Machine Learning Courses to Pursue

Global Tech Outlook Brings the list of the Top Machine Learning courses to pursue to excel in it.

Machine learning (ML), is the study of computer algorithms, that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms, build a model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.


Why is it Important?

In simple words, machine learning is a subset under the broad umbrella of artificial intelligence. AI is a concept that helps to design and create intelligent machines that can function like humans and learn from experience (without being explicitly programmed). And, ML is the branch of AI that gives shape and meaning to the concept of creating intelligent machines. It is a method that involves data parsing and data analysis to automate analytical model building. In machine learning, the systems can learn from data to identify patterns and make informed decisions with minimal or no human intervention.

Automation is one of the most significant contributions of ML. By automating routine and menial tasks, ML allows employees to devote their time to more important tasks that require human cognitive abilities. Furthermore, ML allows organizations to save both time and money without compromising on the product/service quality. Online fraud detection, real-time customer support, malware filtering, and traffic/weather predictions, are some of the significant applications of ML.


Criteria for choosing ML course

The course should:

  • Strictly focus on machine learning.
  • Explain how the algorithms work mathematically
  • Be self-paced, on-demand, or available every month or so
  • Have engaging instructors and interesting lectures
  • Have above-average ratings and reviews from various aggregators and forums.


Now that we have an idea on ML, lets have a glance at the courses available on ML in India

Machine Learning Engineer Nanodegree Program – Udacity

This intermediate course on machine learning is offered on the Udacity platform and is in partnership with Kaggle. It is formulated for professionals looking to build advanced skills in ML techniques and algorithms. Since the program is for intermediate level, candidates need to have familiarity with data structures, 40 hours of experience in scripting along with numpy and panda’s awareness, a basic understanding of machine learning and deep learning algorithms and techniques. You need to devote 10 hours a week for three months to this program. The program will take you through advanced techniques along with packaging, and deployment of the ML models to a production environment, you will gain experience on Amazon SageMaker to deploy web applications and evaluate model’s performance. To achieve the prerequisite for this intermediate course in case you are lacking the required skills, you may go through the intro to machine learning nano degree program first.


Machine Learning A-z: Hands-On Python & R in Data Science – Udemy

This course is forged for candidates interested in machine learning and looking to build a successful career in it. Whether you are a college student or professional wanting to switch to data science or someone who wants to add value to their business, you may opt for this course.  Candidates can access the course on the Udemy platform. This 44-hour online program takes you through the world of ML in small steps to develop exceptional and desirable skills in the field. The course includes 75 articles and 35 downloadable resources and divided into 10 parts. This course is suitable for intermediate level candidates having basic knowledge in machine learning and exploring to gain enhance their skills. This course helps you to master ML using python and R, make precise predictions, effective analysis, and create ML models. The course includes exercises on real-world problems to ensure practical exposure along with theoretical knowledge.


Machine Learning – Coursera

Machine learning program brought to you by Stanford University, California, and available on the Coursera platform. This online program spans 54 hours and can be completed at your own pace and schedule. It is available in English with subtitles in various languages including Hindi, Arabic, and Japanese. It is meticulously formulated with a focus to cover all aspects of learning including theoretical knowledge, practical learnings, and industry best practices. The program covers the most effective machine learning techniques and their implementations. The program starts with an introduction to machine learning, statistics, pattern recognition and built upon other topics such as parametric/non-parametric algorithms, support kernels, vector machines, neural networks, clustering, dimensionality reduction, variance theory, algorithms to build robots, web search, computer vision, medical informatics, database mining, and so on. Program completion will equip you with all the machine learning techniques required to solve real-world problems.


Machine Learning – edX

The advanced level course drafted by edX in sync with ColumbiaX spans for 12 weeks with 8-10 hours per week of dedicated time. Since the course contains advanced level information, one is recommended to have basic knowledge in calculus, linear algebra, probability, statistics, and scripting. The course is divided into two parts, the first part deals with the prediction of output based on various inputs based on algorithms mostly supervised learning. While the second part deals with unsupervised learning without clear-cut end goals. The course syllabus includes maximum likelihood estimation, various regression techniques, Bayes rule, classifications, Laplace approximation, kernel methods, clustering, factorization, space models, and so on.