Masterclass- From Beginner to Expert-Data Science

Explore Java Server Pages, a server-side technology for dynamic, platform-independent web applications built on the Java platform, offering embedded dynamic HTML and portability across systems.

Explore Java server pages as a dynamic web page technology for Java applications, enabling user interfaces, form input, database access, and sharing information between requests.

Discover how data science uses scientific methods to extract insights from structured and unstructured data, apply classification, anomaly detection, regression, clustering, and reinforcement learning, and manage the project lifecycle.

Explore the data science life cycle from preparation in an analytical sandbox to modeling. Apply exploratory analysis, build training and testing data, and use techniques like classification, association, and clustering.

Learn what machine learning is as a subset of artificial intelligence that enables machines to learn from experience and make data-driven decisions using unsupervised learning and reinforcement learning.

Explore supervised, unsupervised, and reinforcement learning, highlighting supervised methods such as regression, logistic regression, classification, and support vector machines, plus decision trees and clustering with association in unsupervised learning.

Explore reinforcement learning, a form of machine learning where an agent interacts with an environment, learns from actions and rewards and penalties, and uses trained models to predict new outcomes.

Explore Python for data science with easy syntax, libraries via pip, and cross-language integration, then compare with R's eight thousand plus packages for visualization, hypothesis testing, clustering, and machine learning.

Explore data science tools with predefined functions and algorithms to build machine learning models without programming languages, and compare Hadoop, HDInsight, Azure, Informatica PowerCenter, and RapidMiner for storage and analytics.

Explore data modelling with machine learning algorithms—clustering, classification, and regression—for predictive models and visualize dependencies with Tableau while building and running TensorFlow computations.

Discover scikit-learn, an open-source BSD-licensed machine learning library that integrates with Python, NumPy, and visualization tools to support unsupervised learning workflows.

Explore how Spark scales computation for machine learning and enables pipelines with AutoML, featuring persistence, feature extraction, transformation, and dimensionality reduction.

Explore core machine learning concepts, including algorithms, models, predictor variables, and training and testing data. Learn to use Jupyter notebooks to implement these fundamentals and build data-driven insights.