The Ultimate Revelations About Machine Learning

‘Machines can teach themselves.’ This phrase had captured our imagination the day it was coined, and it continues to do so. What kind of algorithms encompasses such a phenomenon? What are the Machine Learning basics? The answers to such questions have been revealed in various capacities over the years, but the curiosity around the subject strives for more.
One of the few misconceptions around Machine learning is that it doesn't involve human intervention.
Machine learning algorithms are based on python programming and other such languages. These algorithms are not self-sufficient, at least in the initial stages. They are supervised and trained using data sets, to obtain primary outputs in the beginning. Once the algorithms based on python programming mature, they start recognizing complex relationships within data.
To get optimum results, the quality of Data used matters. The training data should be free from misclassifications. Otherwise, it may hamper the learning process of algorithms.  Only a few algorithms can overcome such misclassifications.
The quantity of Data should be calibrated as well. Exposing the algorithms to humongous sets of test-data may make them responsive to a specific information-niches. They may provide inaccurate results when fed with something other than test-data. So, it’s about maintaining a balance between under-training and over-training an algorithm.
Now that we are done with the revelations, let's understand Machine learning basics. It comprises three essential components:

• Model: This component is responsible for identifying relationships and making predictions.
• Parameters: These are the factors which Model takes into consideration while making decisions.
• Learner: This component is responsible for comparing the predictions made and the actual outcome. Based on the dissimilarities found between the two, it adjusts the parameters.

Let’s understand the Machine Learning basics via a real-world scenario.
Let’s assume that there is a teacher, who wants her students to attain the best grade in a test. She wants to calculate the time her students should devote to their studies, to obtain the desired results. Let’s see how machine learning can help her find the solution.
Firstly, the teacher will set the parameters for the Model. In this case, parameters will be ‘Hours spent on studying’ and ‘Resulting scores’.
Suppose the teacher gives the following relationship between the parameters:

Based on the relationship as mentioned earlier, the machine learning algorithms will form a predictive line of results for different inputs.
Once the machine learning model is established, the actual test results are entered by the teacher. Let's assume that she enters the scores of four students along with their study-hours.
The above results or scores will act as the training data, through which the learner will refine the Model. It will assess the difference between the predictive results given by the original Model and the actual results. The parameters will be adjusted accordingly by the learner, to improve the accuracy of the Model.
For example, the relationship mentioned above between the parameters may be modified into the following.

As you can see, the predictions have been reworked, to get closer to the actual results. It must be noted that the Learner makes very minute adjustments for refining the Model. The training cycle can be repeated again and again until the perfect Model is created. A Model that can predict the correct scores based on study-hours.
Similar training cycles can be conducted for creating Models that can identify events and objects. There is so much to learn and reveal about Machine Learning that one write-up cannot suffice. Still, we hope that this write-up gave you a good insight into the mysteries of Machine Learning.