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Train with an AI & ML curriculum that's comprehensive. Get certified with world-class online training in Artificial Intelligence & Machine Learning. Get mentored by Ivy League educated business practitioners led by industry-renowned Dr Mahendra Mehta.

Your course curriculum at ISET (280 hrs)

• Mathematical & Statistical Foundation for AI/ML 75 hrs

  Data Science can be defined as a science by which scientists use advanced mathematical & statistical concepts to extract embedded information from a complex dataset. Data scientists employ a number of tools and techniques which has originated from Mathematics & Statistics. Thus, the good knowledge of Mathematics & Statistics is vital and very important for understanding data science. The course will start by laying the foundation of Mathematics & Statistics for understanding how these concepts are used to solve the problem in AI & ML.

• Python Language 25 hrs

  Python is an open-source interpreted, high-level general-purpose popular programming language. Its design philosophy focuses on code readability. Python Language constructs and its object-oriented approach aim to help programmers write clear, logical code for various applications.

  In recent years, Python has become a very popular language for the use of ML & AI application.

  It is a very rich language that has very comprehensive libraries for various applications in ML & AL, Visualization, Neural Networks, Deep Learning, etc.

  Learning Python forms an important part of the course.

  Python provides a good learning ecosystem, relatively easy to learn, huge flexibility, platform-independent, easily readable, excellent visualization options, outstanding community support and growing popularity.

• R language 25 hrs

  R is an open-source language used for statistical computation, data analysis and graphical analysis of the complex data set. This is very popular among statisticians. The language is rich by virtue of its packages which helps the user to solve a variety of problems in many disciplines including ML & AI. It is the second most popular language for solving problems in ML & AL.

  R is very popular with academicians for data analysis & visualization.R allows data scientist to quickly code complex concepts in much fewer, easy to understand, lines of codes.

• Data Warehousing 10 hrs

  A data warehouse is described as a technique for collecting and managing data from varied source to provide meaningful business insights. It is a blend of technologies and components which aids the strategic use of data. A Data Warehouse is used extensively for Airlines, Banking, Healthcare, Public Sector, Retail Chain, Investment and Insurance sector, Telecom industry, etc. A typical and popular function of data warehousing is Extract, Transform and Load (ETL). It is combined into one tool to put data of one database and to place it into another database.

• ML- Regression 25 hrs

  Linear Regression is one of the most well-known and well-undetermined algorithms in statistics and machine learning. Under this topic, various other types of regression such as Logistic regression, Ridge regression, Lasso, Robust regression, etc. will be covered. These techniques are widely used to solve many practical problems. These are also the starting point for more complex algorithms.

• ML- Classification 25 hrs

  ML Classification algorithm forms an important part in data science. The subject matter covers a variety of algorithms such as random forest, support vector machine and several other popular learning techniques and their applications.

• ML- Clustering 25 hrs

  This is unsupervised learning and this also forms an important part of learning from data in data science. There are many practical applications where clustering is useful and gives a very useful insight into the complexity of the data.

• Natural Language Processing (NLP) 25 hrs

  NLP is a subfield of Computer Science, Information Engineering and AI. NLP is concerned with the interaction between computers to process and analyze large amounts of natural language data. The field includes Speech Recognition, Natural Language Understanding, and Natural Language Generation. This area is rapidly becoming a vital component of ML, AI, Robotics and several other data science discipline.

• Business Applications using Data Science 25 hrs

  Subject experts and corporate leaders would be brought in to talk about the difficulties they face in the real world. Students would be able to interact with people from various industries to help broaden their horizons. 

  This will facilitate informed decision-making for students when selecting which field to go into.

• SQL/NoSQL 10 hrs

  SQL is a domain- specific language used in programming and designed for managing data held in a Relational Database Management System (RDBMS). It is very popularly used in handling structured data where there is a relation between different entities/variables of the data. This is extensively used in AI & ML when one encounters numerical and textual data.

  NoSQL provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational business. NoSQL databases are increasingly used in big data and real-time applications. NoSQL systems are called Not Only SQL to emphasize that they may support more than SQL like queries. This has now become an integral part of the databases for ML & AI problems when non-structured data are encountered.

• Deployment of AI & ML applications 10 hrs

  Real-world ML system is composed of ML code and a big part is model deployment, model retraining, maintenance, on-going updates and experiments, versioning and monitoring.