Course Description

In today’s data-driven world, organizations rely heavily on insights derived from data to make informed decisions and drive innovation. The Data Science and Machine Learning Training Program is designed to equip learners with the knowledge and practical skills required to extract meaningful patterns, build predictive models, and apply intelligent algorithms to solve real-world problems.

This program provides a strong foundation in data analytics, statistics, and programming, combined with in-depth exposure to machine learning techniques and tools. Through a blend of theory, coding exercises, and hands-on projects, participants will learn how to turn raw data into actionable insights and deploy machine learning models for practical use cases.

The course offers a comprehensive learning journey through the key stages of the data science and machine learning pipeline. Participants begin with Python programming for data analysis, followed by data preprocessing, visualization, and exploratory data analysis (EDA) using libraries such as NumPy, Pandas, Matplotlib, and Seaborn. Building on this foundation, learners explore statistical modeling, supervised and unsupervised machine learning algorithms, including linear and logistic regression, decision trees, clustering, support vector machines, and neural networks.

The course also introduces advanced topics like deep learning, natural language processing (NLP), and model deployment using frameworks such as TensorFlow, Scikit-learn, and Keras. Real-world case studies and capstone projects ensure that learners gain hands-on experience in developing, training, evaluating, and deploying ML models across various domains.