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Machine Learning: from Zero to Hero: (1) Introduction to ML
Udemy MOOC / Non-credit 0

Machine Learning: from Zero to Hero: (1) Introduction to ML

About this course

Machine Learning: From Zero to Hero is a series of courses designed for anyone looking to start a career as an ML engineer, DL Engineer, data scientist, or AI engineer.This series approaches Machine Learning from two main perspectives: Scientific and Practical. We provide the necessary scientific knowledge for each ML concept while avoiding unnecessary details. This approach helps learners understand how to use, implement, and develop ML models accurately, minimizing time spent on trial and error.The second perspective is practical, allowing learners to both implement ML models from scratch to deeply understand their operation and use pre-built ML models from packages such as Scikit-learn.These perspectives ensure learners become well-versed ML engineers, DL Engineers, data scientists, and AI engineers.This is the first course in the series: Introduction to Machine Learning.This is not just like any introduction to ML you may saw; it is a comprehensive and detailed one, which is why it is offered as a separate course.In this comprehensive introduction, you will learn the following:1. What is the AI?2. The difference between AI and Data Science (DS)?3. What is Soft Computing (SC)?4. What exactly is ML?5. Why we need ML?6. What is the Deep Learning (DL)?7. ML Applications.8. ML types: Based on amount of Supervision.     8.1. Supervised learning.           8.1.1 Regression.           8.1.2 Classification.     8.2 Unsupervised learning.           8.2.1 Clustering.           8.2.2 Anomaly detection.           8.2.3 Dimensionality reduction.           8.2.4Association rule.    8.3 Semi-supervised learning.

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69/100

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What you'll learn

  • understand the definitions of AI and machine learning
  • identify the difference between AI and data science
  • grasp the fundamental concepts of soft computing and machine learning types
  • explore supervised and unsupervised learning techniques

Course objectives

  • to build a foundational understanding of machine learning
  • to introduce practical implementation of ML models
  • to highlight ML applications across different industries
Machine Learning #deep learning #scikit-learn #regression #classification #artificial intelligence #machine learning #dimensionality reduction #unsupervised learning #data science #supervised learning #anomaly detection #clustering #association rules #soft computing #semi-supervised learning
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