How to Become a Machine Learning Engineer | A Complete Learning Path

Ever since the companies have realized that the regular software are not going to address the growing competition and that they need something additional to pull them, concepts like Data Science and Machine Learning have started gaining momentum.

Whether it is Voice Recognition based searching, Fraud Detection Systems, or a Recommendation System by Amazon or Netflix, Machine Learning has been the most implemented technology over the period of time.

This is the reason every company wants to hire Machine Learning Professionals and a huge crowd of aspirants wish to become one. Let’s uncover the right way anyone can pursue this field!

In this blog we will uncover following aspects of Machine Learning:

• Definition and Applications of Machine Learning
• Data Science Vs Machine Learning
• Why Should You Learn Machine Learning?
• Industry Trends and Future Scope of Machine Learning
• Role of Machine Learning in Business
• Detailed Analysis of the ‘Machine Learning Engineer’ Profile
• Prerequisites to Become a Machine Learning Engineer
• Learning Path for Machine Learning Engineer
• A Snippet of Machine Learning Engineer Resume

What is Machine Learning?

Well, speaking broadly, Machine Learning is the field that deals with educating the machines to make them able to make decisions like humans. But, that is not enough; as it is too broad to help you understand the purview of Machine Learning.

Definition of Machine Learning

So, here are several definitions on Machine Learning:

As per SAS, “Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.”

As per IBM, “Machine learning is a form of AI that enables a system to learn from data rather than through explicit programming. However, machine learning is not a simple process.”

As per Google, “Machine learning is functionality that helps software perform a task without explicit programming or rules. Traditionally considered a subcategory of artificial intelligence, machine learning involves statistical techniques, such as deep learning (aka neural networks), that are inspired by theories about how the human brain processes information.”

So, if we combine all of them together and try to come to a common ground, the definition would be:

“Machine Learning is the field that is a subset of Artificial Intelligence, is a process that deals with educating a computer system so that it learns from its own feedback, instead of having to explicitly program it for every task.”

Applications of Machine Learning

1. Image Recognition: Machine Learning is used for Image recognition. It is one of the most popular applications of Machine Learning. Identifying objects like persons, places, etc., on the images are done using Machine Learning Techniques. Facebook uses Machine Learning for Auto-friend Tagging Suggestion.
2. Virtual Assistance: Various Virtual Assistance Systems like Cortana, Siri, Alexa recognize and respond to Natural Language using Machine Learning Algorithms. ML Algorithms decode Natural Language voice instructions and act accordingly.
3. Email Spam and Malware Filtering: Whenever a suspicious mail arrives it lands on Spam folder. Any mail that violates the filtering rules, Machine Learning Algorithms push them to junk folder. It also saves the users from unnecessary malware attacks.
4. Self-driving Cars: Companies like Google and Tesla are manufacturing Driverless cars that do not require human drivers. This is done by Machine Learning and Deep Learning Algorithms that help Cars to make decisions like humans.
5. Speech Recognition: Google’s voice-based search option works on Machine Learning and Deep Learning Algorithms. Understanding the Natural Language and fetching the web results based on indexed words from the lexicon  
6. Automatic Language Translation: Similar to Speech Recognition, Automatic Language Translation deals with Natural Language Processing and works on Machine Learning Algorithms.

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