In this article we want to have a simple Introduction to Python Machine Learning, now let’s talk about some concepts.
Introduction To Python Machine Learning
Machine learning is a term coined around 1960 composed of two words—machine corresponding to a computer, robot, or other device, and learning an activity, or event patterns, which humans are good at.Machine learning is the design and study of software artifacts that use past experience to make future decisions;
it is the study of programs that learn from data. The fundamental goal of machine learning is to generalize, or to induce an unknown rule from examples of the rule’s application. The best example of machine learning is spam filtering. By observing thousands of emails that have been previously labeled as either spam or ham, spam filters learn to classify new messages.Machine learning mimicking human intelligence is a sub field of artificial intelligence—a field of computer science concerned with creating systems. Software engineering is another
field in computer science. Generally, we can label Python programming as a type of
software engineering. Machine learning is also closely related to linear algebra, probability
theory, statistics, and mathematical optimization. We usually build machine learning
models based on statistics, probability theory, and linear algebra, then optimize the models
using mathematical optimization. The majority of us should have at least
sufficient knowledge of Python programming.
Machine Learning Types
1: Unsupervised Machine learning: when learning data contains only indicative signals
without any description attached, it is up to us to find structure of the data
underneath, to discover hidden information, or to determine how to describe the
data. This kind of learning data is called unlabeled data. Unsupervised learning
can be used to detect anomalies, such as fraud or defective equipment, or to
group customers with similar online behaviors for a marketing campaign.
2: Supervised Machine learning: when learning data comes with description, targets or
desired outputs besides indicative signals, the learning goal becomes to find a
general rule that maps inputs to outputs. This kind of learning data is called
labeled data. The learned rule is then used to label new data with unknown
outputs. The labels are usually provided by event logging systems and human
experts. Besides, if it is feasible, they may also be produced by members of the
public through crowdsourcing for instance. Supervised learning is commonly
used in daily applications, such as face and speech recognition, products or
movie recommendations, and sales forecasting.
We can further subdivide supervised learning into regression and classification.
Regression trains on and predicts a continuous-valued response, for example
predicting house prices, while classification attempts to find the appropriate class
label, such as analyzing positive/negative sentiment and prediction loan defaults.
If not all learning samples are labeled, but some are, we will have semi-
supervised learning. It makes use of unlabeled data (typically a large amount)
for training, besides a small amount of labeled. Semi-supervised learning is
applied in cases where it is expensive to acquire a fully labeled dataset while
more practical to label a small subset. For example, it often requires skilled
experts to label hyperspectral remote sensing images, and lots of field
experiments to locate oil at a particular location, while acquiring unlabeled data
is relatively easy.
3: Reinforcement Machine learning: learning data provides feedback so that the system
adapts to dynamic conditions in order to achieve a certain goal. The system
evaluates its performance based on the feedback responses and reacts
accordingly. The best known instances include self-driving cars and chess master AlphaGo.
So in Python Programming language we can use Scikit-learn for Machine Leaning, for Machine learning first of all you need to install Python Programming Language.
also if you want to learn python you can check my these articles on Python.
What Is Training Set And Testing Set?
Machine learning is about learning some properties of a data set and then testing those properties against another data set. A common practice in machine learning is to evaluate an algorithm by splitting a data set into two. We call one of those sets the training set, on which we learn some properties; we call the other set the testing set, on which we test the learned properties.
What is Scikit-Learn?
According to Wikipedia Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.
How to Install Scikit-Learn
The simple way installation for Scikit-Learn is to pip install scikit-learn
How To Install Pandas And Matplotlib ?
Pandas is an open source library that provides data structures and analysis tools for
Python. pandas is a powerful library, and several books describe how to use pandas
for data analysis. We will use a few of panda’s convenient tools for importing data
and calculating summary statistics.pandas can be installed on Windows, OS X, and Linux using pip with the following command:
pip install pandas
Pandas can also be installed on Debian- and Ubuntu-based Linux distributions using
the following command:
apt-get install python-pandas
Matplotlib is a library used to easily create plots, histograms, and other charts with
Python. We will use it to visualize training data and models. matplotlib has several
dependencies. Like pandas, matplotlib depends on NumPy, which should already
be installed. On Debian- and Ubuntu-based Linux distributions, matplotlib and its
dependencies can be installed using the following command:
apt-get install python-matplotlib
Binaries for OS X and Windows can be downloaded from Matplotlib Website
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