Length: 2 Days
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Fundamentals of Machine Learning for Data Analysis Training by Tonex

Mastering Big Data and Analytics in 2 Days 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

 

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