At a broad level, machine learning can be
classified into three types:
1. Supervised
learning
2. Unsupervised
learning
3. Reinforcement
learning
1) Supervised
Learning
Supervised learning is a type of machine learning method in which
we provide sample labeled data to the machine learning system in order to train
it, and on that basis, it predicts the output.
The system creates a model using labeled data to understand the
datasets and learn about each data, once the training and processing are done
then we test the model by providing a sample data to check whether it is
predicting the exact output or not.
The goal of supervised learning is to map input data with the
output data. The supervised learning is based on supervision, and it is the
same as when a student learns things in the supervision of the teacher. The
example of supervised learning is spam filtering.
Supervised learning can be grouped further in two categories of
algorithms:
- Classification
- Regression
2)
Unsupervised Learning
Unsupervised learning is a learning method in which a machine
learns without any supervision.
The training is provided to the machine with the set of data that
has not been labeled, classified, or categorized, and the algorithm needs to
act on that data without any supervision. The goal of unsupervised learning is
to restructure the input data into new features or a group of objects with
similar patterns.
In unsupervised learning, we don't have a predetermined result.
The machine tries to find useful insights from the huge amount of data. It can
be further classifieds into two categories of algorithms:
- Clustering
- Association
3)
Reinforcement Learning
Reinforcement learning is a feedback-based learning method, in
which a learning agent gets a reward for each right action and gets a penalty
for each wrong action. The agent learns automatically with these feedbacks and
improves its performance. In reinforcement learning, the agent interacts with
the environment and explores it. The goal of an agent is to get the most reward
points, and hence, it improves its performance.
The robotic dog, which automatically learns the movement of his
arms, is an example of Reinforcement learning.
Best Python
Library for ML
Machine Learning, as the name suggests, is
the science of programming a computer by which they are able to learn from
different kinds of data. A more general definition given by Arthur Samuel is –
“Machine Learning is the field of study that gives computers the ability to
learn without being explicitly programmed.” They are typically used to solve
various types of life problems.
In the older days, people used to perform Machine Learning tasks by manually
coding all the algorithms and mathematical and statistical formula. This made
the process time consuming, tedious and inefficient. But in the modern days, it
is become very much easy and efficient compared to the olden days by various
python libraries, frameworks, and modules. Today, Python is one of the most
popular programming languages for this task and it has replaced many languages
in the industry, one of the reason is its vast collection of libraries. Python
libraries that used in Machine Learning are:
·
Numpy
·
Scipy
·
Scikit-learn
·
Theano
·
TensorFlow
·
Keras
·
PyTorch
·
Pandas
·
Matplotlib
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