Data Science and AI Foundation Certification Program

Statistics

What You'll Learn

• Descriptive Statistics: Understand data distributions, measures of central tendency (mean, median, mode), and variability (variance, standard deviation).

• Probability Concepts: Grasp the principles of probability, conditional probability, Bayes' Theorem, and their role in predictive modeling.

• Inferential Statistics: Learn hypothesis testing, confidence intervals, and the significance of sample data in drawing conclusions about populations.

• Distributions and Their Applications: Explore common probability distributions like normal, binomial, and Poisson, and understand their use in machine learning.

• Correlation and Regression Analysis: Master relationships between variables and predictive modeling techniques like linear regression.

• Advanced Topics: Dive into feature scaling, outlier detection, and how statistical methods integrate with machine learning algorithms and many more!!

Why This Module Matters

In the world of machine learning, statistics acts as the backbone of understanding data, building robust models, and validating results. This module ensures you are well-equipped to interpret data patterns, design experiments, and optimize machine learning workflows.

By the end of this module, you’ll have the statistical knowledge and confidence to transition seamlessly into advanced machine learning concepts and techniques.

Machine Learning

What You'll Learn

• Introduction to Machine Learning: Understand the core concepts, workflow, and real-world applications of machine learning.

• Supervised Learning Algorithms:

  • Regression Techniques: Dive into Linear Regression, Polynomial Regression, and Ridge/Lasso Regression for predicting continuous outcomes.

  • Classification Algorithms: Learn Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Naive Bayes for categorical predictions.

  • Ensemble Methods: Master boosting and bagging techniques like Gradient Boosting, XGBoost, and AdaBoost for improving model performance.

• Unsupervised Learning Algorithms:

  • Clustering: Explore K-Means for grouping similar data points.

  • Dimensionality Reduction: Learn Principal Component Analysis (PCA) and t-SNE for reducing data complexity while retaining critical information.

• Evaluation Metrics: Use metrics like accuracy, precision, recall, F1-score, and confusion matrices for classification, and RMSE/MAE for regression to measure model performance.

• Cross-Validation : Implement techniques to prevent overfitting and optimize model performance using Hyperparameter search

• Case study : Coding based case study included for the algorithms and many more!!

Why This Module Matters

Machine learning is revolutionizing industries by enabling systems to learn from data and make intelligent decisions. This module not only teaches you how to implement cutting-edge algorithms but also focuses on interpreting results, optimizing models, and solving real-world problems effectively.

By the end of this module, you will have a robust understanding of machine learning algorithms and their applications, giving you the confidence to tackle complex data challenges and drive AI-powered innovation.

Python Essentials for AI

What You'll Learn

• Python Basics: Master the syntax, data types, operators, and control structures essential for writing efficient Python code.

• Functions and Loops: Understand how to build reusable code blocks using functions and automate repetitive tasks with loops.

• Data Structures: Work with lists, dictionaries, sets, and tuples, and understand how to manipulate them effectively for AI applications.

• File Handling and Modules: Learn to handle data files, use Python libraries, and modularize your code for better functionality.

• Regular Expressions (Regex): Discover how to extract, search, and manipulate text patterns—an essential skill for processing unstructured data.

• Introduction to Libraries for AI: Get hands-on with key Python libraries like NumPy for numerical computations, Pandas for data manipulation, and Matplotlib for data visualization.

• Seaborn for Advanced Visualization: Create stunning and insightful visualizations to better understand data trends and relationships.

• Debugging and Optimization: Hone your debugging skills and write optimized code for performance-critical applications and many more!!

Why This Module Matters

Python is at the heart of modern AI and machine learning workflows. Its simplicity, extensive library support, and active community make it the preferred language for AI professionals. This module ensures you have a solid foundation in Python programming, setting the stage for tackling complex AI algorithms and data analysis tasks.

By the end of this module, you’ll be proficient in Python programming and ready to explore advanced AI concepts and projects confidently.

Note: Prior knowledge of programming/coding is not required as this module is designed to go from scratch.

Machine Learning Capstone Project

What You'll Do

• End-to-End Project Development: Work on real-time projects from problem formulation by applying all the concepts you've learned in previous modules.

• Diverse Domains: Gain exposure to a variety of industries with projects such as:

  • Finance: Build credit risk models to predict loan defaults or detect fraudulent transactions using classification techniques.

  • Healthcare: Develop diagnostic tools using machine learning algorithms to predict diseases or analyze medical images.

  • E-Commerce: Implement recommendation systems to enhance user experience and boost sales.

  • Marketing and Retail: Perform customer segmentation and predict churn rates to optimize marketing strategies.

  • Manufacturing: Use predictive maintenance techniques to minimize downtime and enhance operational efficiency and many more.

• Comprehensive Workflow:

  • Data Collection and Cleaning: Work with real-world datasets, clean, and preprocess them for analysis.

  • Feature Engineering: Identify and create the most relevant features for optimal model performance.

  • Algorithm Selection and Implementation: Choose the right supervised or unsupervised algorithms based on project requirements.

  • Model Evaluation and Tuning: Use advanced evaluation metrics and hyperparameter optimization for robust results.

Why This Module Matters

The Machine Learning Capstone Project is your chance to demonstrate mastery of machine learning by solving real-world problems. It prepares you to tackle industry challenges, showcase your expertise to potential employers, and add impactful projects to your portfolio.

By the end of this module, you will have hands-on experience and confidence to deliver AI-powered solutions across industries, making you job-ready for data science and AI roles.