#98 Machine Learning & Data Science Challenge 98

PublishedFebruary 7, 2023

•1 min read

#98 Machine Learning & Data Science Challenge 98

Greetings.

I am a machine learning engineer based in India, possessing a sustained interest in machine learning since my undergraduate studies. I have completed Stanford University's machine learning course (Andrew Ng) via Coursera, and IBM's machine learning and deep learning curriculum. My current focus is on machine learning and data science projects, aiming to leverage my expertise for impactful, real-world problem-solving.

Part of seriesMachine Learning & Data Science Interview Challenges

What is the Augmented Dickey-Fuller Test?

The Dickey-Fuller test:

It is one of the most popular statistical tests. It is used to determine the presence of unit roots in a series and hence helps us to understand if the series is stationary or not.

The null and alternate hypothesis for this test is:

Null Hypothesis: The series has a unit root (value of a =1)
Alternate Hypothesis: The series has no unit root.

If we fail to reject the null hypothesis, we can say that the series is non-stationary.
This means that the series can be linear or difference stationary.

#machine-learning #data-science #2articles1week #hashnode #programming-blogs

Comments

Join the discussion

No comments yet. Be the first to comment.

Machine Learning & Data Science Interview Challenges

Part 20 of 50

Machine learning and data science are increasingly among the most sought-after skills in tech. Read this article for advice on how to prepare for machine learning and data science interviews.

Up next

#97 Machine Learning & Data Science Challenge 97

What do you understand by the trend of data? A general systematic linear or (most often) nonlinear component that changes over time and does not repeat. There are different approaches to understanding trends. A positive trend means it is likely tha...

More from this blog

#117 Machine Learning & Data Science Challenge 117

What are the benefits of TensorFlow over other libraries? The following benefits are: • Scalability • Visualisation of Data • Debugging facility • Pipelining

Feb 18, 20231 min read

#117 Machine Learning & Data Science Challenge 117

#116 Machine Learning & Data Science Challenge 116

What is the difference between CNN and RNN? CNN (Convolutional Neural Network): • Best suited for spatial data like images. • CNN is powerful compared to RNN. • This network takes a fixed type of inputs and outputs. • These are ideal for video and im...

Feb 17, 20231 min read

#116 Machine Learning & Data Science Challenge 116

#115 Machine Learning & Data Science Challenge 115

What is a Pooling Layer? It is generally used in reducing the spatial dimensions and not depth, on a convolutional neural network model.

Feb 17, 20231 min read

#115 Machine Learning & Data Science Challenge 115

#114 Machine Learning & Data Science Challenge 114

What is Keras? It is an Open Source Neural Network library written in Python that runs on top of Theano or TensorFlow. It is designed to be modular, fast, and easy to use. It was developed by François Chollet, a Google engineer

Feb 16, 20231 min read

#114 Machine Learning & Data Science Challenge 114

#113 Machine Learning & Data Science Challenge 113

What are the very important steps of Tensorflow architecture? There are three main steps in the Tensorflow architecture are: • Pre-process the Data • Build a Model • Train and estimate the model

Feb 16, 20231 min read

#113 Machine Learning & Data Science Challenge 113

Blog Vision

153 posts

A mountain hiker, space enthusiast, and technology aficionado who transitioned into a career as a data scientist.