Machine Learning

Machine Learning has been the latest talk of the town among people of all ages. The buzz around this study seems to be growing and growing. Many are pursuing this field, while the others are intrigued and want to venture in it. Machine Learning has become part of our daily lives without us realising it. There must be dozens of instances everyday in which you might have encountered it. Further, we will dive into the interesting world of machine learning, its algorithms and frequently asked machine learning questions in interviews. 

What is Machine Learning?

Machine Learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data.It is the science of getting computers to act without being explicitly programmed. Arthur Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term in 1959. ML is literally what the name suggests, the ability of machines to learn. It is considered as a part of Artificial Intelligence. From now on whenever anyone debates about machines taking over the world, we can assume that ML had a part to play. 

People commonly assume that Machine Learning and Statistical Learning are the same thing. Although analytical models have ML algorithms, there is more to it than just statistical usage. We can say that Statistical learning is a part of Machine Learning. 

Machine Learning Algorithm

As already said, this field has penetrated everyone’s daily life. From practical speech recognition, effective web search to data science models to self driving cars; machine learning is everywhere. After understanding its meaning and usage, we will try to understand the different algorithms of machine learning. There are three kinds of algorithms:

Supervised Learning

In this type of learning, the way to get the machine to interpret the algorithm is with the help of examples. The input and outputs are already provided to the algorithm which they interpret. With this interpretation they find ways to reach the end and this way the machine learns the operation.

Even though the operator knows the answer, the purpose of this is to make the machine accurate with the help of supervisors. The algorithm tries to find patterns and make observations to become more accurate. The same way a teacher tests a student even though the teacher knows the right answer, so that the student can learn. Therefore the end result is an accurate algorithm.

Unsupervised Learning

In unsupervised learning after analysing and interpreting the data provided the algorithm makes patterns. There is no answer key or human operator. Here the machine is expected to make relations and find patterns in the huge sets of data given to it. In such situations, the algorithm tries to give structure to the present data in a way to understand it. This structure could be made by the algorithm through making the data into clusters or any other sort that provides some sort of accuracy and efficiency. 

Reinforcement Learning 

In this learning, the machine learning algorithm is given output, set of action as well as parameters.  This kind of learning is strictly controlled. Since this algorithm is more defined, the way the algorithm learns is through trial and error. It finds ways to reach the end through different approaches and finds the most optimal one. As said it learns from trial and error, which is through past experiences. 

Machine Learning use cases

Speech to text translation is possible using machine learning. Live voice and recorded speech can both be converted to text files using certain software tools. Intensities on time-frequency bands can also be used to segment speech. Examples of speech recognition in the real world: Search using your voice, Dialling by voice, Controlling the appliances. Devices like Google Home and Amazon Alexa are some of the most common uses of speech recognition software.

In the real world, image recognition is a well-known and widely used example of machine learning. Based on the intensity of the pixels in black and white or colour photos, it may recognise an object as a digital image. Face recognition within an image is another application of machine learning. The technology can discover commonalities and match them to faces using a database of people. This is a term that is frequently used in law enforcement.

Conclusion

One can say Machine Learning is definitely worth the buzz around it. With the help of this study, humans have found ways to ease every other task and it just gets more simpler each day. From the physical world to search engines the power of machine learning is seen far and wide. Making it ever so enticing for students to venture into these fields and try it for themselves.