Introduction to Python for Machine Learning

Python for ML

Author: Bashir Alam
Reviewer: Deepak Prasad


Introduction to Python for machine learning

Machine Learning is the field of Computer Science where machines are trained in a way that they start working like a human being. It is the latest technology that enables machines/computers to automatically learn from past data. Machine Learning using Python is very popular nowadays. In this article, we will introduce machine learning using Python.

We will discuss the history of machine learning, what machine learning is and how it works. We will also learn about different categories of machine learning including supervised learning, unsupervised learning, and reinforcement learning. Moreover, we will discuss why python is popular for machine learning and will cover some of the python packages that are used in ML. In a nutshell, this tutorial will briefly introduce Python for machine learning.


Getting started with Python for machine learning

Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. The dictionary definition of machine learning is “the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data”. The most popular language for ML is Python because of its various useful packages which we will discuss later. But now let us briefly discuss the history of Machine learning.


A brief history of machine learning

Machine learning is necessary equipment for the goal of leveraging technologies around artificial intelligence. Due to its learning and decision-making abilities, machine learning is often referred to as Artificial Intelligence (AI), though, in reality, it is a subdivision of Artificial Intelligence. Until the late 1970s, it was a part of AI’s evolution. Then, it branched off to evolve on its own. Machine learning has become a very important response tool for cloud computing and eCommerce and is being used in a variety of cutting-edge technologies. The following diagram shows different eras of machine learning.

Python for machine learning


In 1950 Alan Turing created the ‘Turing Test’ to find out whether a machine has intelligence or not. To pass the test the computer has to fool a person in a way that the person should believe that another person is talking to him/her. In simple words, the computer has to fool the person in a way that the person should consider the computer a person. In 1952, Arthur Samuel had written the first computer learning program. The program was a game of checkers.

The program was written in a way that the more the computer played the game it was improving its performance. Simply it was improving from its past experiences. Hence it was the start of Machine Learning. In 1957 Frank Rosenblatt, the first neural network known as perceptron which simulated human thoughts. In 1967 nearest neighbor algorithm was written. Afterward, Machine Learning become a popular field.


What is Machine Learning?

As we discussed Machine Learning is the sub-branch of Artificial Intelligence. Artificial Intelligence is the science and engineering of making machines more intelligent. Machine Learning being the sub-branch of Artificial Intelligence provides the ability to computers or other machines which can learn automatically and can improve from their past experiences.

In Machine Learning first, we are training the computer/machine using some sort of training data and later we are testing it by giving some relevant data. Interestingly the computer learns from its past experience and improves its functions over time. Machines/computers are being allowed to learn from what they have experienced in the past.


How does Machine learning work?

Machine Learning works with observations or data such as direct experience, instruction, or examples. A Machine Learning algorithm looks for patterns in data and analyzes the examples we feed it. On the basis of these examples, it generates insights that allow making smarter decisions. Generally, learning systems of Machine Learning algorithms are divided into three parts

  1. Decision Process: Machine Learning algorithms are used to make predictions or classifications. Based on some sort of input that can be labeled or unlabelled, the algorithm will produce an estimate of a pattern in the data.
  2. Error Function: An error function serves to evaluate the prediction of the model. If there are known examples, an error function can make a comparison to assess the accuracy of the model.
  3. Model Optimization Process: If the model can fit better to the data points in the training set, then weights are adjusted to reduce the discrepancy between the known example and the model estimate. The algorithm will repeat this evaluation and optimize the process, updating weights autonomously until a threshold of accuracy has been met.


Different Categories of Machine learning

As with any method, there are different ways to train machine learning algorithms, each with its own advantages and disadvantages. To understand the pros and cons of each type of machine learning, we must first look at what kind of data they ingest which we will discuss later in more detail. The following diagram shows different types of machine learning.

Python for machine learning

Basically, there are four categories of Machine Learning: Supervised learning, Unsupervised learning, Semi-supervised learning, and Reinforcement learning. In the coming paragraphs, we will discuss in detail each category.


1. Supervised Machine Learning

As the name suggests we are supervising the machine to learn from the instructions provided to it by the external sources. In this method, the machines are taught by using examples. The operator provides the machine learning algorithm with a known dataset that includes desired inputs and outputs, and the algorithm must find a method to determine how to arrive at those inputs and outputs. While the operator knows the correct answers to the problem, the algorithm identifies patterns in data, learns from observations, and makes predictions.

The algorithm makes predictions and is corrected by the operator – and this process continues until the algorithm achieves a high level of accuracy/performance. Supervised learning is analogous to training a child to walk. You will hold the child’s hand, show him how to take his foot forward, walk yourself for a demonstration, and so on until the child learns to walk on his own.

This method is one of the most basic types of machine learning. In this type, the machine learning algorithm is trained on labeled data. Even though the data needs to be labeled accurately for this method to work, supervised learning is extremely powerful when used in the right circumstances.


2. Unsupervised Machine Learning

In unsupervised learning, we do not specify a target variable to the machine, rather we ask the machine “What can you tell me about X?”. More specifically, we may ask questions such as given a huge data set X, “What are the five best groups we can make out of X?” or “What features occur together most frequently in X?”. To arrive at the answers to such questions, we can understand that the number of data points that the machine would require to deduce a strategy would be very large.

In the case of supervised learning, the machine can be trained with even about a few thousand data points. However, in the case of unsupervised learning, the number of data points that are reasonably acceptable for learning starts in a few million. These days, the data is generally abundantly available. The data ideally requires curating. However, the amount of data that is continuously flowing in a social area network, in most cases data curation is an impossible task.

For example, when unsupervised learning is used to identify elephants and giraffes, the machine must decide which of the 100 photos provided are elephants and which are giraffes and do the classification at the same time. In future predictions, the machine identifies which animal it is according to the characteristics and classification it detects. However, the results identified by the machine are not necessarily correct.


3. Semi-supervised Machine Learning

A small amount of data are labeled. Computers only need to find features through labeled data and then classify other data accordingly. This method can make predictions more accurate and is the most commonly used method. If there are 100 photos, 10 of them which are elephants and which are giraffes are labeled. Through the characteristics of these 10 photos, the machine identifies and classifies the remaining photos. Because there is already a basis for identification, the predicted results are usually more accurate than unsupervised learning.

Semi-supervised learning is similar to supervised learning but instead uses both labeled and unlabelled data. Labeled data is essential information that has meaningful tags so that the algorithm can understand the data, whilst unlabelled data lacks that information. By using this combination, machine learning algorithms can learn to label unlabelled data.


4. Reinforcement Machine Learning

Reinforcement learning focuses on regimented learning processes, where a machine learning algorithm is provided with a set of actions, parameters, and end values. By defining the rules, the machine learning algorithm then tries to explore different options and possibilities, monitoring and evaluating each result to determine which one is optimal. Reinforcement learning teaches the machine trial and error. It learns from past experiences and begins to adapt its approach in response to the situation to achieve the best possible result.

Let us consider training a pet dog, we train our pet to bring a ball to us. We throw the ball at a certain distance and ask the dog to fetch it back to us. Every time the dog does this right, we reward the dog. Slowly, the dog learns that doing the job rightly gives him a reward and then the dog starts doing the job the right way every time in the future. Exactly, this concept is applied in the “Reinforcement” type of learning.


Python for Machine Learning

As in the initial part of this article, we have discussed the importance of Python in Machine Learning. Python is one of the most popular and important languages for Machine Learning. Machine learning using Python has many benefits such as simplicity and consistency, access to great libraries and frameworks for machine learning, flexibility, platform independence, and a wide community. Python is among the best-suited programming languages for machine learning. As compared to other languages, building machine learning systems with Python is easier and faster and is prone to fewer errors.


Why Python for Machine Learning?

Python is a programming language that supports the creation of a wide range of applications. Developers regard it as a great choice for Artificial Intelligence (AI), Machine Learning, and Deep Learning projects. It has a huge number of libraries and frameworks: The Python language comes with many libraries and frameworks that make coding easy. This also saves a significant amount of time.

The most popular libraries are Numpy, which is used for scientific calculations; Scipy for more advanced computations; and Scikit, for learning data mining and data analysis. These libraries work alongside powerful frameworks like Tensorflow and Apache Spark. These libraries and frameworks are essential when it comes to machine and deep learning projects. Python code is concise and readable even to new developers, which is beneficial to machine and deep learning projects. Due to its simple syntax, the development of applications with Python is fast when compared to many programming languages. Furthermore, it allows the developer to test algorithms without implementing them.


Packages of Python for Machine Learning

Python is the most popular language for Machine Learning has a wide range of packages/libraries/modules which are very frequently used. Here I will list down some of the popular packages used in Machine Learning.

  • Numpy
  • Scipy
  • Scikit-learn
  • TensorFlow
  • Keras
  • pandas
  • PyTorch
  • matplotlib
  • Theano



We have discussed in detail Python for Machine Learning. We have also talked about the types of Machine Learning. For instance, we have provided brief information about the categories such as Supervised learning, Unsupervised learning, Semi-supervised learning, and Reinforcement learning. We also discussed how Python is used in Machine Learning and why it is so popular in Machine Learning. Moreover, we have also mentioned some popular Python packages which are widely used in Machine Learning. To summarize, this tutorial contains a brief introduction to Python for Machine learning series.


Further Reading

Python pandas
Python sklearn
Python tensorflow


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