Understand data and its types accordingly sample data and derive meaningful information from the data in different statistical parameters.

learn about probability, interpret & solve real-life problems using chance. You will get to know the power of possibility with Bayesian Inference.

Illustrate inferences from present data and construct predictive models utilizing various inferential parameters (as a constraint)

understand the various methods of testing the alternative hypothesis.

introduction to Clustering as part of this Module which forms the basis for machine learning.

Discover the roots of Regression Modelling operating statistics.

learn data and its types and will accordingly sample data and derive meaningful information from the data in terms of different statistical parameters

learn about probability, interpret & solve real-life problems using chance. You will get to know the power of possibility with Bayesian Inference.

learn to draw inferences from present data and construct predictive models using different inferential parameters (as the constraint).

understand the various methods of testing the alternative hypothesis.

get an introduction to Clustering, which forms the basis for machine learning.

know about the roots of Regression Modelling operating statistics.

introduction to Data Science and see how Data Science helps to analyze large and unstructured data with various tools.

know about various statistical techniques and terminologies utilized in data analysis.

Discuss the various sources available to extract data, organize the data in a structured form, examine the data, and represent the data in a graphical format.

introduction to Machine Learning, discuss the different categories of Machine Learning and execute Supervised Learning Algorithms.

learn the Supervised Learning Techniques and implement various techniques, such as Decision Trees, Random Forest Classifier, etc.

Know about Unsupervised Learning and the different types of Clustering that can be utilized to analyze the data.

know about association rules and various types of Recommender Engines.

Examine Unsupervised Machine Learning Techniques and the execution of various algorithms, for example, TF-IDF and Cosine Similarity.

learn about Time Series data, different components of Time Series data, Time Series modeling - Exponential Smoothing models, and ARIMA model for Time Series Forecasting.

Intro to Reinforcement Learning and Deep Learning concepts, discuss Artificial Neural networks, the building blocks for Artificial Neural Networks, and a few Artificial Neural Network terminologies.

get a brief idea of it and learn the basics.

understand various types of sequence structures, related operations, and their usage. Understand diverse ways of opening, reading, and writing to files.

learn how to create generic scripts, address errors/exceptions in code, and finally, how to extract/filter content using Regex.

understand the basics of statistics, different measures and probability distributions, and the supporting libraries in these operations.

understand in detail about Data Manipulation.

Know the concept of Machine Learning and its types.

Understand Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest classifiers, etc.

learn about the impact of dimensions within data, perform factor analysis using PCA and compress sizes, developing an LDA model.

Understand Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest classifiers, etc.

Know about Unsupervised Learning and the various types of Clustering that can be utilized to analyze the data.

know Association rules and their extension towards recommendation engines with the Apriori algorithm.

learn about developing an intelligent learning algorithm such that the Learning becomes more and more accurate as time passes by.

Know about Time Series Analysis to forecast dependent variables based on time, different models for time series modeling such that you analyze accurate time-dependent data for Forecasting.

learn about selecting one model over another and how to convert weaker algorithms into stronger ones.

Understand Big Data and its components, such as HDFS. Learn about the Hadoop Cluster Architecture, Introduction to Spark, and the distinction between batch processing and real-time processing.

Understand the basics of Scala that are needed for programming Spark applications.

Know about object-oriented programming and functional programming techniques in Scala.

and learn how to develop Spark applications and perform data ingestion using Sqoop.

Get an insight into Spark - RDDs and other RDD-related manipulations for implementing business logic (Transformations, Actions, and Functions performed on RDD).

learn about SparkSQL, which is used to process structured data with SQL queries, data-frames and datasets in Spark SQL, and different kinds of SQL operations performed on the data-frames.

Discover why machine learning is required, various Machine Learning techniques/algorithms, and Spark MLlib.

Implement various algorithms supported by MLlib, such as Linear Regression, Decision Tree, Random Forest, etc.

Learn about Kafka Cluster and how to configure different types of Kafka Cluster. Get introduced to Apache Flume, its architecture, and its integration with Apache Kafka for event processing.

know about the various streaming data sources such as Kafka and flume. Create a spark streaming application.

understand the concepts of Deep Learning and know how it varies from machine learning.

you should be able to get yourself introduced to TensorFlow 2. x. You will install and validate TensorFlow 2. x by creating a Simple Neural Network to indicate handwritten digits and operating Multi-Layer Perceptron to improvise the model's accuracy.

comprehend how and why CNN came into existence after MLP and learn about Convolutional Neural Network (CNN) by studying the theory behind how CNN is utilized to predict 'X' or 'O'. Use CNN VGG-16 utilizing TensorFlow 2 and predict whether the given image is of a 'cat' or a 'dog' and save and load a model's weight.

understand the concept and working of RCNN and figure out why it was developed in the first place.