What is Machine Learning?
Before Machine Learning we had rule-based systems, where we need to understand a use case and define a rule for the outcome. This seems to be perfect for smaller data and scenarios where the complexity is less, and the programmer has the visibility on the rules to be applied. Basically, in rule-based model, we will define the rule and then we provide the data to the rules and see the output. This will not be sustainable when we have more rules to define and we also do not know how many variations are there.
Machine learning solves this issue by giving you a model, which is trained based on the historical data instead of rules. Therefore, any variations in the future can be handled. Machine learning provides us with a trained algorithm or model where each parameter is tuned to the best approximation. Now, whenever we introduce a new set of data, we can get a prediction based on the tuning of the model. Hence machine learning provides flexibility in analysing and providing the predictions.
Therefore, machine learning can be defined as an approach where historical data is used to train algorithm or set of algorithms so that they will be transformed into a machine learning model with tuned weights and parameters. This model can be used to predict quantity based on set of variables or classify into binary or multiple classes.
Machine learning find wide range of application in various fields and industries. For example, machine learning is used for churn prediction, Fraudulent transaction detection, predicting future prices, spam filer and many more. Wide range of algorithms also help data scientist to explore on the best suitable one for the scenario or problem they are trying to solve. Advanced version of machine learnings such as deep learning is finding uses in various applications of artificial intelligence such as image recognition and classification, object detect, analysing sound and voice data and even predicting stock market fluctuations. These uses and benefits from machine learning and deep learning are changing the world we live and operate. Innovations in machine learning algorithms and advancement in computational efficiency of computer hardware have enabled data scientist and researchers to use machine learning for various applications.
Machine learning involves various mathematical concepts such as linear algebra, probability, calculus and numerical computations. Statistics also forms the base of Machine learning, for example concepts such as linear regression and tests to identify correlation and other feature engineering methods are heavily borrowed from it. Machine learning can also term as an interdisciplinary study which takes concepts and best practise from various field of study and utilize them accordingly. For example, concept of entropy which we can find in thermodynamics is also used in Data science to define the stability or variance within the data.
To explain on how machine learning works, we need to understand that building a highly efficient and accurate machine learning model involves various steps and iterations. Each step in building a machine learning model is crucial and cannot be overlooked. First and the most important steps is Data, that is extracting the relevant data from the source. Getting access to the required data is by itself a very challenging activity. After the data is extracted, we need to clean the data as part of data pre-processing. In this stage all the necessary data transformation happens in term of required format, structure and form. For example, missing values is some thing which is very common on datasets. In data pre-processing step, we can use various data imputation methods to impute the missing values. Similarly, we can identify the outliers in the data and remove or take corrective actions. After this stage, we need to perform feature engineering to identify how we can transform or engineer the independent variables so that we will get the best prediction accuracy. All these three steps are with the data, and next steps are related to models. Before we start building a machine learning model, we need to identify which model is best suited for the use case in hand. We can choose either one or a set of algorithms for model building. Next steps are to set up base model and check the initial model accuracy so that we can finalize on the model to be used. Then we will need to tune the model to the best configuration of hyper parameter, this stage involved constantly validating the model and retuning the hyper parameter till we arrive at the best configuration. There it is an iterative step from the beginning and once the model is tuned, we will test it in a real time scenario ad retune it accordingly.
Nowadays, the scope of machine learning is in every industry. Analyst and research organizations are predicting an overall value of close to trillion US dollar in value as benefits from using machine learning approach. This also provide enormous opportunities for the students and professional who are skilled in Machine learning and Data science. Knowing machine learning can help a person to quickly get a high paying job which also provides opportunity for upskilling everyday when they perform their work as a data scientist. In near future, machine learning is a must have skillset for any professional. Therefore, it is necessary for learners and professional to quickly skill or reskill themselves in the machine learning and data science concepts.
We, Mr Data is a professional organization which focus on imparting machine learning skillset to our students. We have trainers who are industry experts and have many years of experience in building a machine learning model end to end and made it drive benefits to the organization. With this rich industry experience, our trainers can help you to understand data science and learn how to build various machine learning models. We also provide practical knowhow and guidance to our students on how to build and successfully implement a machine learning model based on real time use cases. This will help our students to get a machine learning job in the market quickly and have the best career growth.