All content related to machine-learning-concepts

Pre-processing of Time Series Data In Machine Learning

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.

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Parameter Learning and the Intuition Behind Gradient Descent

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.

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K-Means Clustering Algorithm In Machine Learning

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.

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Bias-Variance Tradeoff In Machine Learning

Bias, Variance, and Bias-Variance tradeoff are the most popular terms in machine learning and the most frequent questions asked in machine-learning interviews.

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Cost function and process of learning in Machine Learning

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.

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Word Vector Encoding: Make Machines Understand Text

Computers only understand numbers, not text. So we need to convert our text into vectors using vector encoding.

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Introduction To Machine Learning and Comparison With Traditional Programming

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.

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Pre-processing Of Text Data In Machine Learning Part 1

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.

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Difference Between Artificial Intelligence And Machine Learning

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.

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Logistic Regression: A Detailed Overview

Logistic regression is a most used supervised learning algorithm for solving the classification problems in machine learning industry.

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Linear Regression: A complete understanding

Linear regression is a supervised learning algorithm and used to solve the regression problems in machine learning industries.

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Famous Loss Functions and Cost Functions in Machine Learning

Optimization of error function is the respiratory process for Machine learning algorithms. But this error function varies for classification and regression problems.

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Methods To Check The Performance Of Classification Models

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?

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Methods To Check The Performance Of Regression Models

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.

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Introduction to Scikit-Learn in Machine Learning: A Complete Understanding

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.

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Regularization: A Fix To Overfitting In Machine Learning

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.

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Need of Feature Scaling in Machine Learning

Big organizations in data science and machine learning domains record many attributes/properties to ensure they do not lose any critical information.

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Classification and Regression Problems in Machine Learning

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.

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Supervised, Unsupervised, And Semi-Supervised Learning With Real-Life Usecase

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.

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How exactly machine learns in Machine 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?

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Machine Learning Glossary: A-2-Z Terms In Machine Learning

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.

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Classification of Machine Learning Models

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.

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Introduction to Machine Learning: Comparison With Artificial Intelligence

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?

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