Guest Lecture on “Employability skill set and career opportunities in new normal”

Machine learning is a subset of AI, which enables the machine to automatically learn from data, improve performance from past experiences, and make predictions. Machine learning contains a set of algorithms that work on a huge amount of data. Data is fed to these algorithms to train them, and on the basis of training, they build the model & perform a specific task. The resource person gave an introduction to the power of Artificial Intelligence and Machine Learning concepts. The students were provided insight into ML algorithms help to solve different business problems like Regression, Classification, Forecasting, Clustering, and Associations. Based on the methods and way of learning, the students were given insights on Supervised Machine Learning, Unsupervised Machine Learning, Semi-Supervised Machine Learning, Reinforcement Learning. Supervised learning is a process of providing input data as well as correct output data to the machine learning model. The aim of a supervised learning algorithm is to find a mapping function to map the input variable(x) with the output variable(y). In the real-world, supervised learning can be used for Risk Assessment, Image classification, Fraud Detection, spam filtering, etc. Regression algorithms are used if there is a relationship between the input variable and the output variable. 

It is used for the prediction of continuous variables, such as Weather forecasting, Market Trends, etc. Below are some popular Regression algorithms which come under supervised learning: Linear Regression, Regression Trees, Non-Linear Regression, Bayesian Linear Regression, Polynomial Regression. Classification algorithms are used when the output variable is categorical, which means there are two classes such as Yes-No, Male-Female, True-false, etc. Spam Filtering, Random Forest, Decision Trees, Logistic Regression, Support vector Machines. Supervised learning models are not suitable for handling the complex tasks.

o Supervised learning cannot predict the correct output if the test data is different from the training dataset.

o Training required lots of computation times.

o In supervised learning, we need enough knowledge about the classes of object.

The idea on how application areas related to data science such as marketing, sales, data quality, finance, business intelligence, etc., and even serve as a consultant with leading data-driven firms were presented. Various job roles related to data science such as Data Scientist, Data Analyst, Data Engineer, Data Architect, Data Storyteller, Machine Learning Scientist, Machine Learning Engineer, Database Administrator and the skill sets to fit into those positions were presented. Statistical analysis and computing. 

The importance of Machine Learning, Deep Learning, Processing large data sets, Data Visualization, Data Wrangling, Mathematics and Programming were also discussed. An overview on how an IBM tool named Watson Studio provides the environment and tools for data scientist to collaboratively work on data to solve your business problems was presented. How the data scientist can choose the tools they need to analyze and visualize data, to cleanse and shape data, to ingest streaming data, or to create and train machine learning models was explained. How the architecture of Watson Studio is centered around the analytics project and how data scientists and business analysts use analytics projects to organize resources and analyze data were discussed. Finally, the real time projects based on AI and ML and the tools used by IBM to build those projects were discussed. The session ended with an insights and skill sets to become industry ready professionals.

The students gained an idea of what is the need of machine learning and artificial intelligence for become a data scientist, analyst, data engineer and AI/ML architect.