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

Machine Learning: from Zero to Hero: (1) Introduction to ML udemy course free download

Machine Learning, Artificial Intelligence

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.

    8.4 Self-supervised learning.

    8.5 Reinforcement learning.

9. ML types: Based on whether or not the system can learn incrementally from a stream of incoming data.

    9.1 Batch learning.

    9.2 Incremental learning.

10. ML types: Based on how they generalize.

    10.1 Instance based learning.

    10.2 Model based learning.

11. Python Example of:

    11.1 Applying model based learning to solve LINEAR REGRESSION problem

    11.2 Applying instance based learning to solve LINEAR REGRESSION problem.

    11.3 Applying model based learning to solve CLASSIFICATION problem

    11.4 Applying instance based learning to solve CLASSIFICATION problem.