A Decision Tree is a hierarchical breakdown of a dataset from the root node to leaf node based on the governing attributes to solve a classification or regression problem
Customer Segmentation helps businesses to develop customer-focused marketing strategies and tactics. Every customer has a different taste; therefore, one single approach is not applicable for all the customers.
k-NN is the first “machine learning” algorithm developed. The unique thing is, it learns but without explicitly mapping input variables to the target variables.
Character recognition is a primary step in recognizing whether any text or character is present and whether our algorithms can recognize it or not in the given image.
We generally get confused and loosely refer maximal margin classifier, support vector classifier, and the support vector machine as support vector machines. After this blog, this confusion will be gone.
In this article, we will implement the complete machine learning process without using any of the frameworks. We will use NumPy and Matplotlib libraries to build our Machine Learning model from scratch.
From the above definition, we can easily sense that data can be in mainly four different forms, Numerical, Textual, Visual or Audio. But this raw form of data can not be used directly for building machine learning models.
In this blog, we will do hands-on on all these preprocessing techniques. We will use different datasets for demonstration and briefly discuss the intuition behind the methods.
In Machine Learning solutions, we need to have the most coordination between technology and business verticals. For any Machine Learning project from business experts, there are mainly seven different verticals or phases it has to pass. All of these seven verticals are mentioned in the image above.
Time Series Forecasting refers to the use of statistical models to predict future values using the previously recorded observations.
The surrounding environment highly influences house prices but machine learning techniques can help us accurately predict the house price by taking account of all important features.
Time series data is found everywhere, and to perform the time series analysis, we must preprocess the data first. Time Series preprocessing techniques have a significant influence on data modelling accuracy.
To learn the optimal values of these parameters, machines randomly try several combinations. But if we keep selecting values randomly then it may take infinite time and we take help of gradient descent.
Unsupervised learning algorithms come into existence that can extract meaningful information from the junk and present it in a human-readable format. Clustering is one of them.
Bias, Variance, and Bias-Variance tradeoff are the most popular terms in machine learning and the most frequent questions asked in machine-learning interviews.
In earlier stages, machines might be making some mistakes and learning from several experiences. But how? Let’s move towards finding the answer to this question.
Computers only understand numbers, not text. So we need to convert our text into vectors using vector encoding.
Machine Learning and Data Science are some of the most exciting areas in which people are attracted to make a career. Through this article, we will start our career-focused journey towards one of these emerging technologies: Machine Learning.
Text data pre-processing ensures optimal results when executed properly. Fortunately, Python has excellent support of NLP libraries such as NLTK, spaCy, and Gensim to ease our text analysis.
Artificial Intelligence and Machine Learning are the most famous buzzwords in the technical industries. Generally we use them as synonyms but in actual it is not.
Logistic regression is a most used supervised learning algorithm for solving the classification problems in machine learning industry.
Linear regression is a supervised learning algorithm and used to solve the regression problems in machine learning industries.
Classification of movie reviews into positive and negative review categories using sentiment analysis and NLP is discussed in this article..
Optimization of error function is the respiratory process for Machine learning algorithms. But this error function varies for classification and regression problems.
whenever we say that we have built a machine learning model, the first question that comes to us is, What is the accuracy of your model?
We try to compare the performance of Machine Learning models to check its accuracy or robustness. For a regression model we measure error between predicted and actual target variables.
Prediction of wine quality is a challenging task for humans but using machine learning and data science techniques, it can be made easy.
Scikit-learn is a free machine learning framework available for Python, providing an interface for supervised and unsupervised learning. Its free nature makes it more popular and accessible.
Regularization is the technique that is used to solve the problem of overfitting in machine learning. The gap between training and testing error become huge and model loose generalizability.
Companies like Cars24 and Cardekho.com uses Regression analysis to estimate the used car prices based on specific attributes.
Big organizations in data science and machine learning domains record many attributes/properties to ensure they do not lose any critical information.
In this article, we will be discussing those 10 most common misconceptions that are so popular that every one of us must have come across at least once in our ML journey.
With ongoing advancements in Machine Learning and Data Science, we can precisely predict the remaining life span of a person given the essential parameters.
Uber is one such tech giant that continuously explores Machine Learning and Data Science methods and dedicated experiments to better customer experience.
One of the smartest use-cases of data science and machine learning is building the machine learning application for financial security.
Machine learning technologies are now able to predict the individual's personality based on their social media usage. Personality-based communications are highly used in dating apps and recommendation systems.
The use of recent technologies like Machine Learning and Data Science in agriculture will increase the quantity and quality and simultaneously ease the process of farming by a huge.
To learn any new concept, we must know how exactly that started. Every computer science field has a different history, reflecting the struggle that earlier researchers faced and made our journey easy. This article will discuss the 10 most interesting historical facts considered the turning points for machine learning.
In this blog, we will try to make our computers ready for machine learning practicals. There are various ways to make computer systems AI-enabled, but in this blog, we will try the most preferred and easy-to-use method, i.e., Python3 with Sublime Text 3. Overview of the blog: Installation Of Python3 on Windows, Linux & Mac, Installation of Sublime Text 3 on Windows, Linux & Mac, Installation of pip/pip3, Installation of Numpy, Scipy, Pandas, Installation of Machine Learning Framework, Installation of Deep-learning Framework, Installation of a Visualisation tool.
Many major companies like Google, IBM, etc., are exploring machine learning potential in this domain. Cancer classification is one such area where ML can deliver a robust predictive model to identify the cancer possibility based on given observations.
In this article, we will try to give the 5 most important reasons that justify the need for knowledge of DS Algo in the field of Data Science & Machine Learning or Deep-Learning.
Recommender System refers to a kind of system that could predict the future preference for a user based on his/her previous behavior or by focusing on the behavior of similar kind of users.
Both classification and regression deal with the problem of mapping a function from input to output. However, when it comes to classification, the output is a discrete (non-continuous) class label or categorical output. While on the other hand, when the problem is a regression problem, the output is continuous.
Based on the nature of input that we provide to a machine learning algorithm, machine learning can be classified into 4 major categories - Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, Reinforcement Learning.
In this article, we will try to find the answer to another most critical question in machine learning and artificial intelligence - How exactly the machine learns?
In this blog one can find the definitions of all the terms which are related to machine learning. We would suggest to pin this blog for quick revision.
K-NN implementation and Gmail, Yahoo, and Outlook case studyIn 2019, on average, every person was receiving 130 emails each day, and overall, 296 Billion emails have been sent in that year.
If we try to find the answer to different machine learning types, we will get different answers, like classification and regression, supervised and unsupervised, probabilistic and non-probabilistic, and many more.
Machine Learning and Artificial Intelligence are some of the hottest topics that can ensure a brighter future for any individual in the upcoming decade.
In starting, Machine Learning or Artificial intelligence may be a buzzword to many who are curious but don’t know where to get started. To start with, Machine Learning may seem like a formidable task, especially if one lacks a certain background.
We will answer the basic questions related to the fundamentals of machine learning: 1) What is Machine Learning? Then, why do we need Machine Learning? Finally, where can we use Machine Learning?