Machine Learning Tutorial for Beginners 2024 | Types of Machine Learning | Machine Learning Course

Machine Learning tutorial for beginners 2024 | Types of Machine Learning | Machine Learning Course
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Topics Covered👇:
00:15 - What is AI?
04:25 - AI vs ML vs Data Science
10:41 - Machine Learning Paradigms
12:49 - Supervised machine learning
15:54 - Classification Vs Regression
18:58 - Classification Example
23:24 - Regression Examples
29:29 - Unlabelled Data: unsupervised learning
35:52 - What is Clustering in unsupervised learning
38:42 - Clustering for outlier detection
41:32 - Pattern Recognition
43:24 - Labelled Data
46:36 - Labeled + Unlabelled Data
50:55 - Example
52:08 - Key Elements of Reinforcement Learning
56:25 - Applications of Reinforcement Learning
01:00:41 - What is Deep Learning?
01:01:16 - ML vs Deep Learning
01:04:01 - Applications of Deep Learning

Machine learning, deep learning, and artificial intelligence are some of the terms used when talking about anything related to data science. They are not synonyms; each of these terms refers to a specific set of abilities. However, to understand any of these techniques.
Generalization
The ability to predict or assign a label to a "new" observation based on the model built from past experience
Understanding
The ability to assign meaning to all the parts solely based on the context so that the whole makes sense

Machine learning can be broadly classified into four categories, which can be summarised as follows:
Supervised learning
In supervised learning, the model learns how to create a mapping between the input and the output. In other words, the model learns from past data, which consists of pairs made up of inputs, and the corresponding output (labeled data); then, it learns based on this data.
Supervised learning has multiple applications such as classification, regression, retrieval, and recommendation. All of these problems consist of some form of input and output. You will learn about these paradigms in the subsequent segments.

Unsupervised learning
In unsupervised learning, the model tries to find some structure or pattern in the data. Unsupervised learning mainly deals with unlabeled data, i.e., the output is not mentioned and acts on this data without any guidance.
Some examples of the applications of unsupervised learning are as follows:
-Clustering
-Density estimation
-Pattern recognition

Semi-supervised learning
As the name suggests, semi-supervised learning is a combination of supervised and unsupervised learning. We generally apply semi-supervised learning with labeled and unlabeled data with the aim of building better mappings between the input and the output.

Reinforcement learning
In reinforcement learning, the model learns a sequence of actions to perform a specific task that a machine is trained to do. Consider the example of autonomous driving vehicles. Specific tasks such as vehicle path planning and motion prediction are carried out using reinforcement learning. You will learn more about this paradigm in the upcoming segments.

Deep learning paradigm
Deep learning is a subfield of machine learning that mimics the workings of the human brain and can solve any problem in which thinking is required. Most deep learning methods use a neural network architecture. So, deep learning is often referred to as "deep neural networks." "Deep" refers to the layers that these neural networks have.

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