Through machine learning we train machines to perform various actions. A simple example of this action is spam filtering in email.
But as long as we talk about these actions that machine learning performs, the sky’s the limit.
Let’s take a look at other various tasks or actions.
These actions might include predictions, recommendations, estimations, and many more.
Machine learning is also a subset of artificial intelligence or AI.
On the other hand, data science is another branch that might use machine learning.
Therefore machine learning basically overlaps these two fields – artificial intelligence and data science.
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.
Now the question is how do we train machines?
Certainly, we need some historical data or past experience.
Otherwise we cannot train machines to predict something which is more or less accurate. Right?
As a result machine Learning enables computers to behave almost like human beings.
However, when we talk about simulation we actually talk about AI.
With the help of past experience and predicted data we train a machine and reach closer to the accurate prediction.
Machine learning also enables an algorithm to determine what characteristics it needs to identify a face.
In this article we will see three key aspects of Machine Learning.
Firstly, the task. We define a task as the main problem in which we take interest.
Now we can say that a task is also a problem which we can associate with the predictions, recommendations or estimations, and many other similar tasks.
Secondly, we just talked about learning from historical or past data.
Why do we need this?
We use the experience to estimate and resolve future tasks.
Finally, performance matters.
Every machine has a capacity to resolve any machine learning task or problem and provide the best outcome for the same.
But we need to remember that performance depends on the type of machine learning problems.
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