data science Data Science Cheatsheet This is the most complete Data Science Cheatsheet which you should follow to revise all Data Science concepts within 30 minutes and get ready for Interviews and stay in form.
List of Interview Questions Culture Fit interview for Data Science job [Mock] In this article, we will get to know the flow of a culture fit interview for a Data Science job and some pointers on how you could answer the questions asked.
data science How is Deep Learning used for Data Science tasks? In this article, we will see how deep learning is used in Data Science.
data science Project ideas for Data Science In this article, we will get to know some ideas for data science projects.
data science Python questions for Data Science interviews We will discuss a basics of Python here as importance of Python for data science cannot be emphasized enough.
data science Different types of Hypotheses In this article, we will get to know about the various types of hypotheses in statistics.
data science Advanced interview questions on Data Science In this article, we have discussed 35 advanced data science questions asked in interviews.
data science Different Datasets available In this article, we will take a look at the various datasets available that can be used for different problems.
data science Sites to find different datasets In this article, we will see some of the data sources from where we can download and use datasets for free for our data science projects.
data science AO* algorithm AO* algorithm is a best first search algorithm. AO* algorithm uses the concept of AND-OR graphs to decompose any complex problem given into smaller set of problems which are further solved.
Natural Language Processing (NLP) Types of NLP models Natural Language Processing (NLP) refers to a branch of Artificial Intelligence (AI) in Computer Science that gives computers the ability to analyze and interpret human language.
data science F Test F-tests get their name from the F test statistic, which was named after Sir Ronald Fisher. The F-statistic is just a two-variance ratio. Variances are a metric for dispersion, or how far the data deviates from the mean. Greater dispersion is shown by higher values.
data science Time Series Classification In this article, we will learn about a beginner-level approach to time series classification.
data science Fuzzy Relations, Propositions, Implications and Inferences In this article, we will first learn about fuzzy relations and the different types of operations that can be performed on them. Then, we will learn about the truth values of fuzzy propositions, about fuzzy implications or if-then rules.
data science Introduction to Feature Engineering In this article, we will be learning about an important step in the machine learning process: feature engineering.
Machine Learning (ML) Performance Comparison of Different Models and Data Preprocessing Techniques In this article, we will be comparing the performance of different data preprocessing techniques (specifically, different ways of handling missing values and categorical variables) and machine learning models applied to a tabular dataset.
data science Fuzzy Inference System: Introduction In this article, we will discuss a few fundamental concepts that govern the functioning of Fuzzy Systems. We will also discuss some of their real world applications while comparing and contrasting them to Crisp (Hard Computing) Systems.
data science EEG Signal Analysis With Python In this article, we will learn how to process EEG signals with Python using the MNE-Python library.
Machine Learning (ML) Different Hyperparameter optimization techniques In this article, we will explore various techniques used for optimizing hyperparameters of the machine learning model such as Grid Search, Bayesian Optimization, Halving randomized search and much more.
clustering algorithm Spectral Clustering Spectral clustering is an interesting Unsupervised clustering algorithm that is capable of correctly clustering Non-convex data by the use of clever Linear algebra.
data science Statistical Features in Data Science In this article, we will understand about various statistical features like median, quartiles, inter quartile range (IQR), bias and variance.
data science Confidence intervals This article aims to improve our understanding on one of the basic concepts of statistics: confidence intervals.
data science Relation of Data Science to ML, AI, NLP, DL In this article, we will understand how data science is related to various other domains such as Machine learning, Artificial intelligence. Natural language Processing and Deep learning.
data science Over and under sampling This article aims to improve our understanding of oversampling and under sampling which are important concepts in Data Science.
data science Phases / life cycle of Data Science This article serves as an introduction to data science life cycle and gives an overview on the various phases.