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You must have heard the term “Machine learning”, “Artificial intelligence” and “Deep learning” a lot nowadays. Most tech companies use Artificial intelligence to gain more incites predict the success of a new product and even for advert placement.

Now what is ML?

According to IBM, ”Machine learning is a branch of Artificial Intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy”.

Massachusetts Institute of Technology (MIT) defines Machine Learning as a subfield of Artificial Intelligence, Which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to hoe humans solve problems

Machine learning can simply be defined as a subcategory of Artificial intelligence which deals with letting algorithms discover recurring patterns in datasets and using this patterns to learn and improve their performance while performing specific tasks.   

Types of Machine learning

Machine learning being a subset of Artificial intelligence; it is also divided into subsets and these are:

. Supervised Learning

. Unsupervised Learning

. Reinforcement Learning

Supervised Learning

Also known as Supervised Machine learning, this is a type of Machine learning in which the machine learns through Labeled datasets and predicts an output for future unforeseen data.

Just like Machine learning, supervised learning can also be divided into two main subsets;

. Classification

. Regression

Unsupervised Learning

Just like the name suggests, unsupervised learning mainly deals with learning without supervision or previous training. In Unsupervised learning the agents is trained with Unlabelled or Unclassified data. The agent needs to learn from patterns without corresponding output values.

Unsupervised learning can also be divided into two categories:

. Clustering

. Association

Reinforcement Learning

This is a Machine learning training method based on rewarding positive behaviors and/or punishing the undesired ones. A Reinforcement learning agent has the ability to perceive and interact with its environments; the agent takes actions and learns through a series of trial and errors. An example of this is: A mouse runs through a maze to get as much cheese as it can and it learns the best way to get as much cheese with minimal errors.

Uses of Machine Learning

Machine learning has a lot of use cases and here are some common applications of Machine learning

1. Image Recognition
2. Speech Recognition
3. Product Recommendation
4. Self driving cars
5. Catching Spam mails
6. Virtual Personal Assistant
7. Stock market price prediction
8. Traffic prediction
9. Sentiment Analysis
10. Customer support

Job Opportunities

Machine learning as a field has a lot of Careers underneath it:

1. Big Data Engineer
2. Data scientist
3. Data Analyst
4. Machine learning Engineer
5.  Product Manager
6. Robotics scientist
7. Software Developer
8. Natural Language Processing (NLP) scientist

Breaking into Machine learning

.  Having basic computer science skills

.  Learn specifics about Machine learning

. Work on real world projects and build your portfolio

. Join a Machine learning community and attending conferences

. Improve on your communication skills  

This article should have given you an insight to what machine learning is and a beginner level knowledge on Machine learning.

References and more reading:

https://www.ibm.com/topics/machine-learning

https://emeritus.org/blog/machine-learning-what-are-machine-learning-applications/

https://emeritus.org/blog/artificial-intelligence-machine-learning-jobs-in-machine-learning/

https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained

https://www.indeed.com/career-advice/finding-a-job/how-to-break-into-machine-learning