With the technological revolution gaining rapid momentum over the last few decades, machine learning has taken centre stage in the modern world. Its growing importance is evident in the fact that 51% of organisations worldwide call themselves ML-first in 2021. Moreover, studies suggest that the market demand for machine learning is projected to keep rising steadily in the coming years. And because of this, more and more computer engineers and scientists are specialising in machine learning these days.
Machine learning is a key component of artificial intelligence, a technology deemed to be the guiding force of our digital future. More and more businesses worldwide are increasingly depending on machine learning and artificial intelligence to enhance their offerings and functioning. Therefore, having a comprehensive understanding of machine learning – its principles and techniques – is essential for anyone wanting to grab a space in the technology bandwagon.
In this article, we will break down an essential classification technique used frequently in machine learning and data-mining applications – logistic regression – and how Scikit Learn logistic regression is used in Python. The article will also discuss how to have a career in machine learning and artificial intelligence – where to get started, and how to progress.
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What Is Logistic Regression?
Let’s start at the basics.
Data science, especially machine learning, is heavily rooted in statistics and classification problems comprise a considerable portion of all problems in this field. Logistic regression is a common and effective regression method used to solve a binary classification problem. More specifically, it is a machine learning algorithm that tackles two-class classification. The real-world application of logistic regression can be seen in spam detection, disease prediction, social media engagement analytics, etc.
Logistic regression works by defining and estimating the relationship between one or more dependent binary and independent variables. In other words, it is a statistical tool for predicting binary classes that finds applications in Python and other algorithm-based programming languages. There are only two possible outcomes from a logistic regression because of which the outcome is called dichotomous.
Fundamentally, logistic regression is a unique form of linear regression, but instead of the usual outcome as the case with the latter, the target variable in the former is categorical in nature. Logistic regression estimates the probability of a binary event by using a logit function where the dependent variables are a log of odds.
There are a few different kinds of logistic regression:
- Binary – This is a kind of logistic regression where the target variable can only have no more than two possible outcomes. An example of this can be a Cancer test where the result can either be positive or negative.
- Multinomial – In this kind of logistic regression, the target variable has more than two nominal categories of the outcome. An example of this can be seen when predicting the flavours of food.
- Ordinal – Here, the target variable can have more than two ordinal categories. Examples of this are seen in restaurant or service ratings, where a scale from 1 to 5 or 1 to 10 is often presented.
Sklearn Logistic Regression in Python
Python’s Scikit-Learn library has an extensive collection of learning algorithms for processes like data analysis or even data mining. Regarding its application in machine learning, Scikit-learn finds relevance in several processes, including classification, clustering, model selection and, of course, logistic regression.
One of the best features of Python’s sklearn library is its 4-step modelling system that makes building a machine learning classifier infinitely easier. As a result, Scikit-learn is useful for performing various functions like preprocessing data, reducing the scale of problems, appropriately selecting and validating models, solving regression and classification problems and implementing cluster analysis.
Python uses the sklearn.linear_model.LogisticRegression module to implement logistic regression. Below we have an example of how this can be done.
from sklearn import datasets
from sklearn import linear_model
from sklearn.datasets import load_iris
X, y = load_iris(return_X_y = True)
LRG = linear_model.LogisticRegression(
random_state = 0,solver = ‘liblinear’,multi class = ‘auto’
Logistic regression in Scikit learn in Python follows a few necessary steps. The first step is to load the specific libraries and modules required to build a model. The second step is to load the dataset into the model’s environment. After loading, the next step is to visualise the classification and desired prediction. The fourth step is to define the dependent and the independent variables clearly. Finally, we create the function for logistic regression and implement it.
Scikit learn logistic regression is one of the simplest functions to execute in Python that makes several different kinds of classification problems easy to implement.
The Way Forward
With the rising demand for machine learning, deep learning and artificial intelligence-based applications in small and large enterprises, machine learning scientists and engineers have become the most sought after assets for any business globally.If you are someone looking to venture into Python development and machine learning, there is no better time than now to upskill in machine learning and grab the millions of opportunities that this domain has to offer.
To have a successful career in machine learning, it is essential to possess an in-depth knowledge of the basics of the subject. A strong foundation ensures easy execution of everyday tasks and responsibilities of a machine learning professional, which in many ways is quite a demanding job. upGrad offers a comprehensive Master of Science in Machine Learning & AI program, one of India’s highest-ranking programs with a 4.5-star rating. It is offered in partnership with two globally leading technology institutes – Liverpool John Moores University, UK and IIT Madras, India.
Designed for working professionals, this 18 months executive PG program offers the best features to get stronger in Machine Learning. This includes flexible class hours, 24×7 student support, one-on-one career mentorship sessions and high-performance coaching, and live coding classes and profile building workshops.
In addition, students have access to 20 programming languages, tools and libraries, including Python, Keras, TensorFlow, MySQL, and even Excel. Through 15+ assignments and case studies, six practical hands-on capstone projects, and a career boot camp, students emerge as industry-focussed, skilled professionals who are well-versed with the sector’s latest trends, demands, and challenges.
upGrad is one of India’s most prolific online education platforms that has a learner base of over 40,000 paid learners and over 500,000 impacted working professionals across 85+ countries. Its unique industry-relevant programs aim to strengthen a student’s aptitude for securing the top jobs in the market.
The ML sector is currently expanding, which means opportunities are galore in the field. This is one of the best career paths for developers and software engineers in the market right now, ensuring high remuneration and long-term stability.
Who can learn ML?
Anyone can learn machine learning. There are no barriers. However, professionals of a mathematical or statistical background will find it easier to understand Machine Learning. Machine learning is ideal for data scientists or data analysts, statisticians, data engineers, software developers, and computer engineers.
What are the differences between ML, DL and AI?
Technically, ML and DL fall within the ambit of AI. AI is the mother discipline that includes everything related to automation and building smart machines. ML is a specific AI-based system where the machine can self-learn based on preset algorithms without human interventions. DL is a subset of ML in that it is an ML program that is applied to large data sets.
What are the eligibility criteria for upGrad’s program?
The minimum requirement for a candidate to apply to upGrad’s MSc in ML and AI programme is 50% or equivalent score in a Bachelor’s degree in mathematical or statistical background subjects. Additionally, an applicant is recommended to have at least one year of work experience in programming or analytics.