This first bootcamp of our A.I. bootcamp series provides an engaging introduction to Data Science and Machine Learning with a strong emphasis on practical application and real-world relevance. Participants will work with tools such as pandas, numpy, and scikit-learn to clean, analyze, and model data. Through guided, hands-on exercises, you will progress from foundational concepts to building and evaluating machine learning models - equipping you with the skills needed to tackle real-world problems in your organization.

Learning Outcomes:

By the end of the program, you will be able to:

• Work confidently with real-world data, including cleaning, preprocessing, and visualization
• Understand and apply core machine learning methods (regression, classification, clustering)
• Build and evaluate both supervised and unsupervised models
• Analyze time-based data and generate forecasts
• Understand the fundamentals of neural networks and modern AI systems
• Apply AI and data science concepts to practical, domain-specific problems

Topics Overview:

• · Foundations of Data Science and Python
  • Real-world applications across industries
  • Python basics for data work: syntax, data structures, and control flow
  • Hands-on: Writing simple scripts for data manipulation
• · Data Preparation and Visualization
  • Cleaning messy, real-world datasets
  • Feature engineering and data transformation
  • Visual storytelling with data
  • Hands-on: Exploratory Data Analysis (EDA)
• · Predictive Modeling: Regression and Classification
  • Predicting outcomes and identifying patterns - Linear Regression, Logistic Regression for classification problems
  • Key evaluation metrics for decision-making (MAE, RMSE, Precision, Recall, F1-score)
  • Hands-on: Implementing regression and classification models using scikit-learn
• · Discovering Patterns: Clustering and Dimensionality Reduction
  • Segmenting customers, identifying trends
  • Simplifying complex datasets
  • K-Means Clustering, Principal Component Analysis (PCA) for dimensionality reduction
  • Hands-on: Clustering real-world data and visualizing high-dimensional data
• · Model Improvement and Advanced Techniques
  • Support Vector Machines (SVM)
  • Hyperparameter tuning for better performance
  • Hands-on: Training and tuning an SVM model
• · Time Series Analysis and Forecasting
  • Understanding trends over time
  • Forecasting demand, sales, or operational metrics
  • Hands-on: Forecasting trends using historical data
• · Introduction to Neural Networks
  
  • How modern AI systems learn
  • Key concepts behind deep learning - Perceptron, activation functions, forward/ backpropagation
  • Hands-on: Building a simple neural network
• · AI in Practice: Large Language Models (LLMs)
  
  • What LLMs are and why they matter
  • Applications in automation, summarization, and decision support
  • Hands-on: Using pre-trained AI models for real tasks