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
