Disclaimer : If you haven’t read the prologue, its highly recommended to read it first and then come back here to understand the terminologies used. If you have understanding of frequent terms used in Time Series, you may continue.

Statistical Time-Series Analysis using Python

Dickey-Fuller Test :- Dickey-Fuller Test can be used to determine the presence of unit root in the series, and hence help us understand if the series is stationary or not. The intuition behind Dickey-Fuller Tests is if the series y is stationary (or trend-stationary), then it has a tendency to return to a constant mean. …


Disclaimer : This article introduces the theoretical concepts behind Time Series Analysis and the subsequent article would provide implementation and applied knowledge.

With its wide range of applications ranging but not limited to Trend Analysis, Demand Forecasting, Inventory Studies, Budgetary Analysis, Stock Market Predictions, Time Series Analysis is an integral skill required for the niche Data Science/Machine Learning Industry.

What is Time Series Analysis?

Time Series Data is simply a series of data points ordered in time. In a Time Series, time is often the independent variable and the goal is to make the forecast for future periods. …


This article is a Complete Guide to various Cross Validation Techniques that are used often during the development cycle of a Predictive Analytics Solution, in the regression or classification setting. It’d also provide a sample usage code in Python for each of the CV Technique mentioned with a real dataset.

What is Cross Validation?

Validation of the model to identify / evaluate if it fits the data accurately whilst also ensuring that it does not overfit.

Overfit : Model fits perfectly on train data but does not generalize / performs poorly on the test data.

From my experience, I’d say…


Disclaimer: If you are interested only in the code for DeConvNet, it is present at the end of the article with a ConvNet taken as an example.

Convolutional Networks (ConvNets), introduced by LeCun et al., 1989 have achieved state-of-the-art results in challenging visual classification tasks in recent times.

What caused this renewed interest in ConvNet models?

  1. Availability of Training Sets
  2. Significant Increase in Compute Power
  3. Novel Model Regularization Techniques

There is a common analogy among practitioners that insights into the internal operation and behavior of these models or the reason how they achieve such good performance is a cumbersome task…

Anant Kumar

Machine Learning & Deep Learning Practitioner | Learning is Continuous | Github : https://github.com/anant-kumar-0308

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