What do we mean by the classification of machine learning? Firstly, classification is a kind of algorithm that we need to train a machine.
Secondly, it is a concept that has an association with supervised learning.
Finally, classification of machine learning basically categorizes a set of training data into classes.
In the previous article we have learned what a training set is in machine learning.
In short, classification algorithms identify a pattern or make a new observation based on the training data.
This is one of the characteristics of supervised learning.
As a result, it can identify speech, handwriting, or a face from a bunch of images, etc.
Since classification belongs to supervised learning techniques, we need to know about it also.
If you are a complete beginner your journey to learn TensorFlow might start from here.
For the TensorFlow beginners we have a dedicated category – TensorFlow for Beginners.
But besides that, you may need to learn several other machine learning and data science libraries.
As a result, you may check these categories as well – NumPy, Pandas, Matplotlib.
However, without learning Python, you cannot learn the usages of these libraries. Why? Because they all use Python as the Programming language.
Therefore please learn Python at the very beginning and start learning TensorFlow.
And, finally please check our Mathematics, Discrete Mathematics and Data Structures categories specially. We have tried to discuss from basic to intermediate level so that you can pick up the core ideas of TensorFlow.
There are different types of machine learning systems. There are four major categories.
- supervised learning
- unsupervised learning
- semi supervised learning
- Reinforcement Learning
The category depends on the amount of supervision and moreover, the type of supervision it gets during training.
In the next section we will learn more about supervised learning.
After that we will dig deep into other categories as well.
So stay tuned.
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