#103 Machine Learning & Data Science Challenge 103

PublishedFebruary 10, 2023

•1 min read

#103 Machine Learning & Data Science Challenge 103

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 are the techniques of Forecasting?

There are so many statistical techniques available for time series forecast however we have found a few effective ones which are listed below:

Simple Moving Average (SMA)
Exponential Smoothing (SES)
Autoregressive Integration Moving Average (ARIMA)

#machine-learning #data-science #2articles1week #forecast #hashnode

Comments

Join the discussion

No comments yet. Be the first to comment.

Machine Learning & Data Science Interview Challenges

Part 15 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

#102 Machine Learning & Data Science Challenge 102

Give some examples of the Time-Series forecast. There is almost an endless supply of time series forecasting problems. Below are ten examples from a range of industries to make the notions of time series analysis and forecasting more concrete. For...

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.

Command Palette

What are the techniques of Forecasting?

Comments

Machine Learning & Data Science Interview Challenges

#102 Machine Learning & Data Science Challenge 102

More from this blog