Machine Learning with Python: List of Algorithms You Need to Master

Diving into the world of Machine Learning with Python opens up a horizon of opportunities for anyone interested in the intersection of technology and innovation. Python, with its simplicity and robust libraries, is the perfect companion for those venturing into machine learning, providing an accessible yet powerful toolkit for crafting sophisticated solutions. Machine learning itself offers a canvas for creativity, where data and algorithms blend to forge systems that can adapt and learn.

The synergy between machine learning and Python paves the way for cutting-edge developments capable of addressing intricate problems. This article is designed to be an all-encompassing primer for individuals keen on navigating this vibrant domain, encompassing essential topics from the basics of Python and machine learning, to the integration of the two, the diversity of machine learning algorithms, and an overview of some key algorithms in Python.

What is Python?

It is an objective-oriented programing language that was developed in 1991 by Guido van Rossum. It is very to understand and learn. Python is popular amongst developers because it improves code reusability and program modularity. Python is a high-level interactive programming language that enables direct interaction between developers and interpreters – something that makes code writing very easy.

What is machine learning (ML)?

Machine learning is a branch of artificial intelligence that allows computers to undergo automatic learning and become better over time through experience. The main objective of machine learning is to come up with computer programs that have the capability to improve themselves based on new data without requiring any explicit programming for the same.

ML works in conjunction with statistical tools and data predict outputs. It also has an association with the Bayesian predictive model and data mining algorithm. After receiving input from the user, computers use an algorithm to deliver an output. There are several applications of machine learning, including predictive maintenance, fraud detection, automatic translation, video surveillance, and more. 

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How do machine learning and Python add up? 

Python has several features that make it an ideal match with machine learning. Some of these features are mentioned below:

1. It is easy to code. Writing code in Python is as easy as one, two, and three. It is far easier than other languages like Java and C++. 

2. Integrated. It neither takes a lot of time not effort to integrate it with C, C++, and other programming languages.

3. Portable. It is an independent programming language. The same program written using Python can be executed on macOS or Windows. It doesn’t need different codes to run on different operating systems.

4. Object-oriented. It is the perfect example of an OOPs-based programming language. Concepts like objects, classes, encapsulation, inheritance, and polymorphism, amongst others, are common with object-oriented languages. Python supports all of these and more.

5. Dynamic. It is one of the very few dynamically typed languages. This means you are not required to declare the data type while writing code as it is decided at runtime when variables are declared. 

Types of machine learning algorithms

Machine learning algorithms are broadly two categories- supervised and unsupervised. Let us discuss these two types in detail.

1. Supervised learning

Supervised learning is the most preferred type when it comes to practical machine learning problems. It has two types of variables – input variables and input variables. An algorithm is used to learn a function that maps the input to the output. The objective here is to estimate the mapping function in such a way that you or your machine can predict the output variable based on the input variable provided to you for a given data set, their are various types of supervised learning you must know.

It is referred to as supervised learning it works like how teachers supervise the learning process in the class. Here a training data set supervises the learning of an algorithm. We have the desired output – the algorithm under the supervision of the dataset continues to make iterative predictions until the desired level of performance is achieved.

This type of algorithm can be further separated into two groups- classification and regression. Classification algorithms are those that feature a category as the output variable. On the other hand, regression algorithms are those that have real value as the output variable – weight or dollars. 

2. Unsupervised machine learning

In this type of machine learning algorithm, you have the input variables. No output variables are available. The objective of unsupervised learning is modeling data distribution or data structure to learn more about the data set. These algorithms are known as unsupervised learning algorithms – because they neither provide you the desired outputs nor they have anyone supervising the learning.

Algorithms are completely on their own, and they are responsible for both finding and presenting interesting learnings in a data set. These algorithms are further grouped into association and clustering problems. Clustering problems are those that have inherent groupings in the given data. On the other hand, association problems are those that have rules that define large parts of the data. 

Some common machine learning algorithms in Python

1. Linear regression

This is a supervised machine learning algorithm in Python. It predicts an outcome and observes features. Based on the number of variables it runs on – one or many – we can refer to it as simple linear regression or multiple linear regression. It is amongst the most popular ML algorithms in Python. 

It has a simple function – creating a line by putting weights against variables and then making a prediction. Linear regression is often used to predict real values like the cost of items. If there is a line that optimally defines the relationship that exists between independent and dependent variables, it is the regression line. Learn more about linear regression in Machine Learning.

2. Logistic regression

Again, this is a supervised ML algorithm. It is used in predicting discrete values, such as true or false, 0 or 1, and yes or no. It works on independent variables. A logistic function is used to make an estimation that provides either 0 or 1 as output. Though it is named regression, this algorithm is actually the classification type. 

3. Support vector machines (SVM)

This is also a supervised learning algorithm. It belongs to supervised algorithm classification. It creates a line that separates different categories of a data set. This line is optimized by calculating the vector. It is done to make sure that the points that are the closest in each are the farthest apart from each other. Mostly it is the linear vector but sometimes it could be something else too. 

4. Decision tree

This again falls under the supervised ML algorithms. However, it is used for both regression and classification. How does this algorithm work? It takes an instance, navigates the entire tree, and holds a feature comparison using a conditional statement. The side descends is based on the result. This ML algorithm in Python can work on continuous as well as categorical dependent variables. 

Read: Prerequisite of Machine Learning

5. Naïve Bayes

This classification method is based on Bayes’ theorem. This classification method holds an assumption between predictors. So a Naïve Bayes classifier works on the assumption that a specific feature in a class has no relation whatsoever with any other feature of the same class. For instance, a fruit has several characteristics that make it what it is.

According to a Naïve Bayes classifier, each of these characteristics will contribute independently to the probability of that fruit being a certain type. This holds true even if the features are dependent on each other. Its model is quite simple and works great with larger data sets. 

Also read: Machine Learning Libraries You Should Know About


In this blog, we learned about machine learning in Python and the various algorithms that we can use to train our machines to predict and perform better.

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What are the languages used in Machine Learning other than Python?

Besides Python, developers use R, Javascript, Java, C++, etc. R provides a software environment at no cost for statistical analysis and visualizations using the graph data structure. R is prioritized for biomedical data and bioengineering statistics. Javascript has popular libraries like Tensorflow.js, an advanced project developed by Google. Flexible APIs are available to train and build models directly in Javascript. Java provides software environments like Elka, RapidMiner, Weka, JavaML, Deeplearning4j, etc., for machine learning problems. C++ has many powerful libraries like Torch, TensorFlow, mlpack, etc., and efficiently performs tasks.

What are the differences between Supervised Learning and Unsupervised Learning?

Supervised learning contains known input data with labels to classify possible outcomes. Unsupervised learning deals with random input data that is further classified using unsupervised algorithms. Supervised learning uses offline interpretations, whereas unsupervised learning uses real-time data interpretations. The number of possible outcomes is already known in supervised learning, whereas, in the case of unsupervised learning, algorithms perform computations to find the number of results. Accuracy and reliability in supervised learning are better than unsupervised learning due to known possible classes of outcomes. Supervised learning predicts output based on categories, whereas unsupervised learning finds patterns in data for its predictions.

How is Linear Regression different from Logistic Regression?

Linear regression uses a set of independent variables to predict a continuous variable, whereas Logistic regression predicts a categorical variable. Linear regression is used for regression problems, and Logistic regression is used for classification problems. Linear regression gives a straight line, linear graph plot with a value that can exceed the limit from zero to one. Logistic Regression gives an S-shape curve in the graph plot within the range of zero to one to classify the inputs. Linear regression requires a linear relationship between the independent and dependent variables, which is not necessary in the case of Logistic regression.

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