Fundamentals of Machine Learning for Data Analysis

Fundamentals of Machine Learning for Data Analysis Training by Tonex

This course introduces the fundamental concepts and techniques of machine learning for data analysis. Participants will gain hands-on experience with machine learning algorithms, data preprocessing, model evaluation, and applications in various domains. The course is designed to provide a solid foundation for those new to machine learning and data analysis.

Learning Objectives:

• Understand the basics of machine learning and its applications.
• Learn data preprocessing techniques.
• Implement and evaluate machine learning models.
• Explore supervised and unsupervised learning algorithms.
• Apply machine learning to real-world data sets.
• Develop skills in using machine learning tools and frameworks.

Audience:

• Data analysts
• Statisticians
• IT professionals
• Researchers

Program Modules:

1. Introduction to Machine Learning
   • Definition and types of machine learning
   • Key concepts and terminology
   • Overview of machine learning workflow
   • Applications of machine learning
   • Tools and libraries for machine learning
   • Setting up a machine learning environment
2. Data Preprocessing
   • Data collection and cleaning
   • Handling missing values
   • Feature selection and extraction
   • Data normalization and scaling
   • Data splitting (training, validation, testing)
   • Data visualization techniques
3. Supervised Learning Algorithms
   • Linear regression and logistic regression
   • Decision trees and random forests
   • Support vector machines (SVM)
   • K-nearest neighbors (KNN)
   • Model evaluation and validation
   • Hyperparameter tuning and optimization
4. Unsupervised Learning Algorithms
   • Clustering techniques (K-means, hierarchical)
   • Principal component analysis (PCA)
   • Anomaly detection
   • Association rule learning
   • Dimensionality reduction
   • Applications of unsupervised learning
5. Model Evaluation and Deployment
   • Performance metrics (accuracy, precision, recall, F1-score)
   • Cross-validation techniques
   • Overfitting and underfitting
   • Model selection and comparison
   • Model deployment strategies
   • Monitoring and maintaining deployed models
6. Case Studies and Practical Applications
   • Case studies in finance, healthcare, and marketing
   • Hands-on projects with real-world data sets
   • Building and deploying a machine learning model
   • Challenges and best practices in machine learning
   • Future trends in machine learning
   • Resources for further learning and development