Machine Learning Fundamentals

Machine learning is a rapidly growing field of study that involves using algorithms and statistical models to enable machines to learn from data without being explicitly programmed. In this course, you will learn the fundamental concepts, techniques, and tools used in machine learning, including supervised and unsupervised learning, data preprocessing, model selection, and evaluation. You will also learn how to use popular machine learning libraries such as scikit-learn and TensorFlow to apply these concepts to real-world problems.

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Price: 325 EUR

Durtation:

20 hours

Category:

Hot Topics

Level:

Fundamentals

Machine Learning Fundamentals

Course Description

Target Audience: This course is designed for anyone who wants to learn the fundamentals of machine learning, including data scientists, software developers, engineers, and researchers. No prior experience in machine learning is required, but some knowledge of Python programming and statistics is recommended. By the end of the course, you will have a solid understanding of machine learning concepts and be able to apply them to real-world problems.

Course Topics:

  1. Introduction to Machine Learning
  2. Data Preprocessing
  3. Supervised Learning
  4. Clustering
  5. Model Performance Evaluation
  6. Cross-validation
  7. Overfitting and Underfitting
  8. Regularization
  9. Ensemble Methods
  10. Deep Learning
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