Advanced course- Data Science, Machine Learning, Java

Explore how data science uses statistics and machine learning to extract insights from structured and unstructured data with algorithms, and guide the lifecycle—from discovery through preparation, planning, building, and communication.

Explore the data science lifecycle from preparing the analytical sandbox and preprocessing to model planning, exploratory analysis, building, evaluation, and final reports.

Explore machine learning as a subset of artificial intelligence that learns from experience. Leverage data-driven decisions as algorithms grow with data through unsupervised learning and reinforcement learning.

Explore supervised learning, with regression and classification methods such as linear and logistic regression, support vector machines, and decision trees, alongside unsupervised learning that discovers clusters from unlabelled data.

Explore reinforcement learning, where an agent interacts with an environment, takes actions, and learns from rewards and penalties to achieve the best outcomes and make predictions on new data.

Explore Python for data science, its easy syntax and vast ecosystem of open source packages, libraries, and cross-language integrations, enabling data analysis, visualization, and machine learning on real datasets.

Explore data storage and distributed computing with Apache Hadoop, a free open source framework for massive data sets, HDInsight, and cloud tools like Azure and Informatica PowerCenter.

Utilize data science tools for nighttime modelling data, applying clustering, classification, and regression; visualize with Tableau and Click, and grasp TensorFlow's tensors and computation graphs.

Explore the scikit-learn framework for machine learning, an open-source, BSD-licensed library offering supervised and unsupervised algorithms, reusable in various contexts, built on numpy, scipy, and matplotlib with Python APIs.

Spark enables scalable computation for machine learning, offering building and evaluating machine learning pipelines with AutoML, persistence, and tools for feature extraction, transformation, dimensionality reduction, and common algorithms.

Learn core machine learning concepts, including algorithms, models, and predictor variables, and how training and testing data shape predictions, using a Jupiter notebook for implementation.

Discover Python fundamentals for machine learning, learn how to leverage NumPy for scientific computing and array operations, and use Pandas for data analysis and cleaning.

Explore python machine learning with scikit-learn, an open-source BSD-licensed library offering supervised and unsupervised algorithms, and learn core scientific computing with numpy and matplotlib for array handling and 2d visualization.

Explore supervised learning techniques to train models with labeled data, covering regression, classification, linear regression, polynomial regression, and support vector machines.