Top 11 Machine Learning Frameworks You Need to Know!

Software Engineering is not limited to developing and implementing applications or tools anymore. With changing technology landscape, software Engineering has come a long way, thanks to the evolving intelligent systems. Machine Learning and Deep Learning Technologies have created avenues to execute tasks efficiently and more intelligently.

In this entire transformations, Machine learning and Deep Learning frameworks have played a huge role allowing innovation to take the centre stage.

In this blog we will unfold following aspects:

• What is Machine Learning?
• Machine Learning Real-World Examples
• How does Machine Learning Help us?
• Types of Machine Learning Algorithms
• Top 11 Machine Learning Frameworks and Some Other Frameworks Too

What is Machine Learning?

So much is said about Machine Learning and the multifaceted benefits it offers. But it is actually difficult to comprehend the advantages unless the fundamentals are laid out clearly. So before going into much detail, let’s understand what is Machine Learning?

Machine Learning is the field that deals with educating the machines to make them able to make decisions like humans.

To define it more specifically,

“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.”

For an instance, when you buy something on Amazon, it starts showing you the similar products related to your last purchase and recommends you some additional products that you may prefer. All these are the tasks possible through Recommendation system that is executed through Machine Learning Algorithms.

Let’s take a look at the real-world examples of Machine Learning!

Machine Learning Real-World Examples

The modern time companies are harnessing the power of Machine Learning in innumerable ways. Some of them are as follow:

1. Financial Services: Financial Institutions use Machine Learning Algorithms for various reasons like Fraud Detection, Stock Market Trading, Risk Analysis, and much more. Banks and other financial bodies identify the fraudulent activities by identifying the faulty transactions, their IDs, locations, log in & log out time, etc. For these institutes it is important to profile the clients which appear to be defaulters so that appropriate actions can be taken at the right time.

Not just this, but investing money in the stock market, anticipating the time of boom, predicting the right funds to put the money in, so that maximum profit can be gained are also achieved with the help of Machine Learning.

2. Healthcare: Healthcare is a research-intensive industry that has been using modern technologies for decades. With Machine Learning and Deep Learning as the latest tools in the kit, Medical Scientists and Pharmaceutical Companies industries have been able to diagnose the patients, find the antibiotics and medicines, research about a disease, and much more.

3. Retail: Retail industry has witnessed tremendous shift of medium and crowd over the years, thanks to ecommerce. Though it has reduced the chain of supply but has increased the importance of smart technologies to take the centre stage. With Machine Learning being the most important factor behind them the ecommerce platforms are able to perform tasks such as product recommendation, maintaining inventory, formulating strategies for pricing and routing, etc.

4. Manufacturing: Manufacturing Industry has been benefitted by Machine Learning in innumerable ways. Whether it is about bringing improvement in the General Processes, Formulating the strategies for New Product Development or it is about quality control through waste reduction and practice of lean manufacturing. Machine Learning helps the companies detect the anomalies and monitor the performance through IoT devices.

5. Transportation: Transport Industry has been using Machine Learning Technologies up to a great extent. With digitization becoming the new medium, smart technologies have found a new way to proliferate. In the era of disruptive innovations like Uber, the features like reflecting the traffic congestions and shortest possible routes to be travelled in real-time, are applicable with the help of machine Learning. Also, finding the optimum price and price surge at particular situations are some of the complex Machine Learning Algorithms working at the back-end. Not only this but companies like Google and Tesla are coming up with driverless cars that are totally based on Machine Learning and Deep Learning Techniques.

6. Oil and Gas: Oil and Gas Industry has also been adopting Machine Learning for processes like Accurate Modeling and Drilling Automation. Machine Learning helps Oil and Gas firms to analyze what is beneath the surface and also monitor the operations.

This course for Artificial Intelligence and Machine Learning is just the right package for Machine Learning aspirants to land a high-paying job in no time.

How does Machine Learning Help us?

Read more

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.

Read more: