Understanding Machine Learning and It’s How’s!

Machine Learning (ML) is part of artificial intelligence. In the background of most AIs there is machine learning at play. The term machine learning emerged in 1980s when computers started gaining their grounds. As the them name suggests machine learning, it is programs that help the machines learn and do statistical analysis and give results on probabilities and possible outcomes based on data.

Machine learning (ML) helps analyze large volumes of data based on several mathematical algorithms and share possible probabilities and possible outcomes within minutes. ML gives results based on algorithms and the results vary based on conditional variables and algorithm types we apply.

The results can’t be always called accurate because a lot varies based on what parameters and algorithms are applied to the data set. It also varies based on amount and quality of data used.

ML is used on large volumes of data which are impossible for humans to analyze with speed and accuracy. Machines can do this based on rules and programs set to produce results with speed. We can run the data through multiple algorithms and collect all the insights. Based on the insights collected further slice and dice can be performed and then we can decide the based way forward.

ML can also be used on any type of data, so not just text and numbers but also images and videos. In general, the ML process starts with,

1.     Cleaning the data set to ensure its consistency and no missing data. 

2.     Dividing the data set into training data set and data set for analysis.

3.     The training data set is used to train the machine learning algorithm on how to identify and what results to be expected. 

4.     The outcome of training data set is the trained model. To this model we feed the test data set to get the desired results. 

5.     The systems classify the test data set based on the patterns learned and logic used as part of the trained model.

6.     The prediction results can be verified with the trained model outcome to see the consistency. Based on the consistency the results and prediction outcomes can be accepted.

The machine learning is of 3 types. Namely Supervised Learning (SL), Unsupervised Learning (UL) and Reinforcement Learning (RL).

Supervised Learning (SL), as the name suggests, it is supervised means its trained with the labelled data set for identifying data sets and patterns to produce results based on the trained data set and other rules and algorithms to produce predictions. Supervised Learning (SL) works best for the classification problems, E.g., if we feed orange and other fruit images to the model as test data set along with the label specifying ‘its orange’ and for other fruits we ask it to label as “not orange”. Once the model is trained, we feed in the test data set with all fruits to check if it can identify and categorize fruits correctly in the outcomes.

Unsupervised Learning (UL), as the name suggests, it is unsupervised means it learns on its own from unlabeled data set. The algorithm automatically identifies various patterns in the data set and share the prediction outcomes based on the same. The full dataset if fed as input in the Unsupervised Learning (UL). UL identifies and creates its own group of data set based on various data attributes. E.g. if we feed multiple images of fruits to the data set, it can identify and group them based on attributes, like color, shape, weight, size etc. It will not know that this is a fruit or not but it will still efficiently group it and share the predictions and outcomes based on the data set attributes.

There is hybrid approach of combining Supervised and Unsupervised learning as Semi-Supervised learning where both SL and UL are used as certain attributes of dataset can be classified and trained while other remain unclassified and predicted using unsupervised learning. E.g. in the dataset if we know oranges clearly then we can train the model for identifying oranges accurately under supervised learning while all unknowns can be fed to unsupervised learning and that can group it and give predictions based on other attributes and features available in the data set.

Reinforcement Learning (RL), this works through a reward system where the system is rewarded for all right predictions and outcomes. This allows the system to learn based on its own actions that lead to rewards or penalties. Rewards are given for right predictions and penalties for wrong predictions. E.g., in a robotics factory every right action done by robot gets the reward and wrong actions gets the penalty. This allows the robotic system to learn the right and wrong actions based on the incentives (feedback) received.

Machine Learning (ML) has several algorithms and methods that are applied on the data set to build the complete model and share predictions that are impossible for human brain to manually achieve with in time limit of minutes and even hours.

Machine Learning (ML) is used for seemingly difficult predictions and probabilities to make effective decisions based on patterns identified by machine. Decisions like, 

1.     How many customers we must keep and which customer segment is most important for an organization in long term.

2.     Based on trends of past 2 decades when will be the predicted stock market crash.

3.     What age group purchases similar set of products

4.     We should tie up with which brand to combine our product bundles to increase sales

5.     Will Heart attacks increase and by what percentage

The results of the ML model can be verified using various performance metrics scores. In general, 6 performance metrics scores are checked to make a decision on viability of outcomes. The methods used are confusion matrix, F1 score, accuracy, precision, recall, and specificity.

As far as the tools are considered many of the ML capabilities are built in MS Excel. There are also standardized tools like Microsoft Azure ML, Google ML, IBM and many other preoperatory software tools. ML can be also managed using Python, R, Java, Julia, LISP programming languages.

Machine learning (ML) is heavily used in our day-to-day AI applications. ML is used by many market research, commercial, industrial organizations for identifying patterns and making difficult decisions. 

There is a lot of room for organisations for using ML to its fullest to make decisions. Opportunities are immense for all organizations and possibilities are wide spread across industries as we have big data available.

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