Machine learning using python : Terminologies
In this blog post, I will discuss some of the high-level introduction of machine learning terminology and libraries in python which will be helpful to you in getting started with this technology.
You will get to know the basic differences between all the buzz words and libraries and what you should learn first in order to start learning machine learning.
You must have heard a lot of the buzz words like- Machine Learning, Artificial Intelligence, Deep Learning these days. Well, they are different in terms of the features and solutions they provide. You can read more about them here. We will just discuss Machine Learning in this blog.
So, let’s get started.
What is Machine Learning ?
Machine learning is the subfield of computer science that gives
Computers the ability to learn without being explicitly programmed.
– Arthur Samuels
Machine learning follows the same process that a 4-year-old child uses to learn, understand, and differentiate between things. So, machine learning algorithms are typically inspired by the human learning process.
We basically build models that look at the feature sets and learns through it. These models help us in a variety of tasks, such as object recognition, summarization, recommendation, and so on.
Some examples of real-world situations where machine learning is used –
- Recommendation system of Netflix.
- Banks making a decision whether to approve load application or not.
- Chatbots for support and help on any website.
Each of these examples uses a different machine learning algorithm and techniques.
Various techniques of Machine Learning
Let’s explore various techniques :
It is used to predict a continuous value. Such as predicting the price of the house based on the features.
It is used to predict item class OR category. Such as the given animal is a cat or dog.
It is used for finding the structure of data, summarization. For example: grouping of patients with the same disease.
Associating frequent co-occurring items. For example, grocery items that are usually bought together by a particular customer.
- Anomaly detection
It is used in discovering abnormal, unusual cases. For example – credit card fraud detection.
- Sequence mining
Predicting the next event. For example – Identifying click stream in websites.
- Dimension reduction
It is used to reduce the size of the data.
- Recommendation systems
It is used in recommending items. Such as books or movies. This associates people’s preferences with others who have similar tastes and recommends new items to them, such as books or movies.
These were some of the techniques.
Libraries and modules used
Now let’s discuss the libraries and modules that are implemented in python which will be helpful in your journey to learn machine learning.
- Pandas – Pandas library is a very high-level Python library that provides high performance easy to use data structures. It has many functions for data importing, manipulation and analysis. We can create data sets using CSV or XLS and preprocess that data in order to do further computation.
- Numpy – It is a math library to work with N-dimensional arrays in Python. It enables you to do computation efficiently and effectively. It is better than regular Python because of its amazing capabilities. For example, for working with arrays, dictionaries, functions, datatypes and working with images you need to know NumPy.
- Scipy – SciPy is a collection of numerical algorithms and domain-specific toolboxes, including signal processing, optimization, statistics and much more. SciPy is a good library for scientific and high-performance computation.
- Matplotlib – Matplotlib is generally used to create visualizations. It is a very popular plotting package that provides 2D plotting, as well as 3D plotting of charts and some other libraries such as folium which are built on the top of matplotlib also provides a way to create maps.
- Scikit learn – SciKit Learn is a collection of algorithms and tools for machine learning. SciKit Learn is a free Machine Learning Library for the Python programming language. It has most of the classification, regression and clustering algorithms, and it’s designed to work with a Python numerical and scientific libraries: NumPy and SciPy.
Most of the tasks that need to be done in a machine learning pipeline are implemented already in Scikit Learn including pre-processing of data, feature selection, feature extraction, train test splitting, defining the algorithms, fitting models, tuning parameters, prediction, evaluation, and exporting the model.
These were some of the basic terms and libraries that you will hear every time. But this is not enough, there is a lot more in machine learning. I just tried to cover all the basic terms that you will encounter in your first lesson only.
You can checkout our next blog on getting started with machine learning to know more.
Thank you and have fun with learning.
Happy Learning 🙂