About the Course
- Course cost: $1,400
- Dates: July 31 and Aug 1
- Location: 2127 Innerbelt Business Center Drive, St. Louis, MO 63114
- The program will be held in person, with a livestream option if needed.
- Prerequisites: Basic Python programming knowledge
Course Goals and Objectives
- Gain practical experience in data preprocessing, visualization, and model development.
- Understand and apply key machine learning techniques, including regression, classification, and clustering.
- Build and implement both supervised and unsupervised ML models.
- Learn to analyze time series data and develop forecasting models.
- Explore the fundamentals of neural networks and their applications.
Course Overview
- Introduction to Data Science and Python Basics - Overview of Data Science and real-world applications, Python fundamentals: syntax, data structures, and control flow.
Hands-on: Writing Python scripts for data manipulation.
- Data Preprocessing and Visualization - Data cleaning techniques, Feature engineering, Data visualization with Matplotlib and Seaborn.
Hands-on: Exploratory Data Analysis (EDA) with a real dataset.
- Supervised Learning: Regression and Classification - Understanding regression vs. classification, Linear Regression, Logistic Regression for classification problems, Model evaluation metrics (MAE, RMSE, Precision, Recall, F1-score).
Hands-on: Implementing regression and classification models using Scikit-learn.
- Unsupervised Learning: Clustering and Dimensionality Reduction - K-Means Clustering, Principal Component Analysis (PCA) for dimensionality reduction.
Hands-on: Clustering real-world data and visualizing high-dimensional data.
- Advanced Classification Techniques - Support Vector Machines (SVM) for classification tasks, Hyperparameter tuning for better performance.
Hands-on: Training and tuning an SVM model.
- Time Series Analysis and Forecasting - Basics of Time Series data, Forecasting techniques using moving averages and ARIMA.
Hands-on: Forecasting trends using historical data.
- Introduction to Neural Networks - Neural networks basics: Perceptron, activation functions, forward/backpropagation.
Hands-on: Building a simple neural network.
- Model Interpretability - Why Model Interpretability Matters, Importance of explainability in ML, Trade-offs: Accuracy vs. Interpretability, Regulatory and ethical considerations (e.g., GDPR, fairness in AI), Explaining black-box models - SHAP, LIME, etc.
For bulk purchasing options, information on our other offierings, and any administrative needs associated with this course listing please contact us at cec.stl@mst.edu .
