Machine Learning Course Overview

The Machine Learning Course at INDIC Technologies is designed to equip learners with the essential skills required to build intelligent systems and perform data-driven decision-making using real-world datasets. This comprehensive program blends theoretical foundations with practical applications, empowering students to understand and implement core machine learning techniques.

Programming Foundation

• Learn programming fundamentals using Python and R, including data types, control structures, functions, and libraries such as NumPy, Pandas, and Scikit-learn.

• Hands-on experience in data manipulation, preprocessing, and data visualization.

Statistical Analysis & Mathematics

• Understand essential concepts such as probability, hypothesis testing, ANOVA, and descriptive/inferential statistics, which form the mathematical backbone of machine learning.

Supervised Learning

• Implement regression techniques (Linear, Logistic) to model relationships between variables.

• Build classification models such as Decision Trees and Random Forests to predict categorical outcomes.

Unsupervised Learning

• Explore data through clustering algorithms like K-Means to identify patterns and groupings without labeled outcomes.

Advanced Techniques & Tools

• Perform sentiment analysis and text processing using live social media data.

• Work with R programming and SQL integration for data extraction, cleaning, and transformation.

• Apply data visualization tools such as Matplotlib, ggplot2, and seaborn to communicate insights.

Version Control & Environment Tools

• Use Git & GitHub for version management and Jupyter Notebook/RStudio for development and analysis.

• Manage environments using tools like pip, virtualenv, and dependencies via requirements.txt. 

Capstone Projects & Real-World Applications

• Build and deploy end-to-end machine learning models.

• Solve business-relevant problems such as predictive analytics, customer segmentation, and sentiment classification.

• Projects include applications in e-commerce, healthcare, social media, and finance domains.