Our dedicated and industry-experienced trainers are here to teach you the core concepts of each subject. After mastering these fundamentals,you'll work on real-world projects to gain practical experience. We place special emphasis on these projects,ensuring that when you secure a placement,you'll be ready to seamlessly integrate and contribute to your new team.
Building REST APIs for Machine Learning Using Flask and FastAPI Once a machine learning model is trained and serialized, it must be exposed to users or applications for predictions. The
Model Serialization in Machine Learning (Pickle and Joblib) After training a machine learning or deep learning model, the next crucial step is saving the model so it can be reused
NoSQL Databases in Big Data (MongoDB and Cassandra) Traditional relational databases struggle with scalability, flexibility, and performance when handling massive volumes of unstructured and semi-structured data. NoSQL databases were designed
Working with Streaming Data in Big Data In many real-world applications, data is not generated in batches but arrives continuously in real time. Examples include sensor data, financial transactions, social
Spark SQL and DataFrames While RDDs give low-level control, most real-world Big Data applications work with structured or semi-structured data. Spark SQL and DataFrames provide a high-level, optimized, and user-friendly
Apache Spark Basics Apache Spark is a powerful, open-source Big Data processing framework designed for fast, in-memory computation. Unlike MapReduce, Spark processes data in memory, making it significantly faster for
HDFS and MapReduce in Big Data HDFS and MapReduce are the two core pillars of the Hadoop framework. HDFS handles the storage of massive datasets, while MapReduce processes those datasets
Introduction to Big Data and Hadoop Ecosystem In today’s digital world, massive amounts of data are generated every second from social media, sensors, mobile devices, transactions, and online platforms. Traditional
Introduction to Transformers and BERT in Natural Language Processing Traditional NLP models such as RNNs and LSTMs struggle with long-range dependencies and parallel processing. Transformers revolutionized NLP by enabling models
Topic Modeling in Natural Language Processing Topic modeling is an unsupervised learning technique used to automatically discover hidden themes or topics within a large collection of text documents. Unlike text