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.

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.

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.

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

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.

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.

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?