Knowledge Hut

MARCH 19, 2024

In today's digital transformation era, machine learning has emerged as a transformative technology driving innovation across industries. Machine Learning Software Engineers are at the forefront of this revolution, applying their expertise to develop intelligent systems and algorithms.

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Knowledge Hut

JANUARY 16, 2024

Embarking on a journey in the highly demanded field of Machine Learning (ML) opens doors to diverse career opportunities. The avenues to acquire the essential skills for a career in ML are plentiful, ranging from Machine Learning online courses and certifications to formal degree programs. What Is Machine Learning?

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Picnic Engineering

DECEMBER 12, 2023

At Picnic, we track the latest machine learning developments to ensure the best possible demand forecasting. This blog post delves into the learnings and challenges on our journey towards implementing and scaling state-of-the-art deep learning approaches. For that use case a simple SQL query can be enough.

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U-Next

AUGUST 8, 2022

Before heading out for a Machine Learning interview, find time to go through this quick recap blog on the fundamentals of Machine Learning. Introduction to Machine Learning Interview Questions. Data Science and Machine Learning are two of the most widely used technologies around the globe nowadays.

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ProjectPro

JULY 9, 2021

When firing Siri or Alexa with questions, people often wonder how machines achieve super-human accuracy. All thanks to deep learning - the incredibly intimidating area of data science. This new domain of deep learning methods is inspired by the functioning of neural networks in the human brain.

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LinkedIn Engineering

JUNE 15, 2023

Co-authors: Jonathan Hung , Pei-Lun Liao , Lijuan Zhang , Abin Shahab , Keqiu Hu TensorFlow is one of the most popular frameworks we use to train machine learning (ML) models at LinkedIn. To remove this bottleneck, we built AvroTensorDataset , a TensorFlow dataset for reading, parsing, and processing Avro data.

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ProjectPro

JANUARY 15, 2021

Undoubtedly, everyone knows that the only best way to learn data science and machine learning is to learn them by doing diverse projects. But yes, there is definitely no other alternative to data science and machine learning projects. Table of Contents What is a dataset in machine learning?

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ProjectPro

JANUARY 27, 2023

Machine learning applications have been making waves across all industries, and the energy sector is no exception. From smart grid technology to predicting equipment failures to forecasting wind and solar power generation, applications of machine learning in energy sector are widespread.

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Knowledge Hut

JULY 28, 2023

These labels provide context and meaning to the data, enabling machine learning algorithms to learn and make predictions. If you are new to this domain and wanted to learn how to label data for machine learning problems, then you’ve landed on the right page. What is Data Labeling for Machine Learning?

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ProjectPro

OCTOBER 16, 2021

TensorFlow and Scikit-learn, two of the most popular words from the jargon of the Machine Learning world! If you are wondering what is the reason behind their popularity, continue reading as we answer that question in this blog by exploring hands-on machine learning with Scikit-learn and TensorFlow.

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ProjectPro

SEPTEMBER 29, 2021

The term artificial intelligence is always synonymously used Awith complex terms like Machine learning, Natural Language Processing, and Deep Learning that are intricately woven with each other. One of the trending debates is that of the differences between natural language processing and machine learning.

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Knowledge Hut

FEBRUARY 29, 2024

Then, based on this information from the sample, defect or abnormality the rate for whole dataset is considered. Hypothesis testing is a part of inferential statistics which uses data from a sample to analyze results about whole dataset or population. It offers various blogs based on above mentioned technology in alphabetical order.

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Confluent

FEBRUARY 6, 2019

Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. It takes much more effort than just building an analytic model with Python and your favorite machine learning framework. Therefore, the majority of machine learning/deep learning frameworks focus on Python APIs.

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ProjectPro

SEPTEMBER 29, 2021

Probability and Statistics are two intertwined topics that smoothen one’s path to becoming a Machine Learning pro. In this blog, you will find a detailed description of all you need to learn about probability and statistics for machine learning. How to become good at Statistics for Machine Learning?

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ProjectPro

OCTOBER 18, 2021

Machine Learning Projects are the key to understanding the real-world implementation of machine learning algorithms in the industry. It is because these apps render machine learning models that try to understand the customer's taste. can help you model such machine learning projects.

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ProjectPro

AUGUST 25, 2021

This blog introduces the critical differences that one encounters when anyone performs an analysis of logistic regression vs linear regression. Firstly, we introduce the two machine learning algorithms in detail and then move on to their practical applications to answer questions like when to use linear regression vs logistic regression.

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ProjectPro

OCTOBER 27, 2021

Wondering how to implement machine learning in finance effectively and gain valuable insights? This blog presents the topmost useful machine learning applications in finance to help you understand how financial markets thrive by adopting AI and ML solutions.

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ProjectPro

APRIL 12, 2021

Deep learning job interviews. Most beginners in the industry break out in a cold sweat at the mere thought of a machine learning or a deep learning job interview. How do I prepare for my upcoming deep learning job interview? A necessary evil. What questions should I ask them? The good news?

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ProjectPro

OCTOBER 28, 2021

In this blog, we have mentioned all the topics that are considered as prerequisites for learning machine learning. We have covered all the subjects and the best resources that will help you learn them thoroughly. Machine learning is no exception to that. Why should you learn Machine learning?

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ProjectPro

JULY 30, 2021

As an aspiring machine learning professional, a portfolio is the most important asset to have in your job search. But what if you don’t have a machine learning portfolio because you are going to need diverse skills and projects under your belt to land a top machine learning gig.

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AltexSoft

DECEMBER 15, 2021

Machine learning evangelizes the idea of automation. Citing Microsoft’s principal researcher Rich Caruana, ‘75 percent of machine learning is preparing to do machine learning… and 15 percent is what you do afterwards.’ This leaves only 10 percent of the entire flow automated by ML models. MLOps cycle.

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ProjectPro

AUGUST 13, 2021

If you are a beginner searching for Machine Learning GitHub Projects, you are on the right page. Below you will find a list of Machine Learning projects on Github that are beginner-friendly and popular among Data Science enthusiasts. Face Detection Kaggle Machine Learning Projects on GitHub 1. Tessaract 4.

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ProjectPro

AUGUST 16, 2021

Sending out the exact old traditional style data science or machine learning resume might not be doing any favours in your machine learning job search. With cut-throat competition in the industry for high-paying machine learning jobs, a boring cookie-cutter resume might not just be enough.

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DareData

FEBRUARY 28, 2024

In this post of the PyTorch Introduction, we’ll learn how to use custom datasets with PyTorch, particularly tabular, vision and text data PyTorch is one of the hottest libraries in the Deep Learning field right now. Learn how to work with non-linear activation functions and how to solve non-linear problems.

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Knowledge Hut

DECEMBER 26, 2023

A simple usage of Business Intelligence (BI) would be enough to analyze such datasets. The job of a data engineer is to develop models using machine learning to scan, label and organize this unstructured data. They analyze datasets to find trends and patterns and report the results using visualization tools.

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Edureka

JANUARY 18, 2024

This guide provides a comprehensive understanding of the essential skills and knowledge required to become a successful data scientist, covering data manipulation, programming, mathematics, big data, deep learning, and machine learning technologies. Table of Contents Introduction to Data Science What is Data Science?

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RandomTrees

DECEMBER 12, 2023

What Is Synthetic Data In Machine Learning And How To Generate It? Synthetic data relates to data generated through machine learning algorithms that mimic real-world patterns. Additionally, providing DevOps teams with datasets to test and confirm software. Streamlines And Reduces The Cost Of Creating Datasets.

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AltexSoft

AUGUST 25, 2021

Natural language processing or NLP is a branch of Artificial Intelligence that gives machines the ability to understand natural human speech. But today’s programs, armed with machine learning and deep learning algorithms, go beyond picking the right line in reply, and help with many text and speech processing problems.

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Scott Logic

JANUARY 9, 2024

As part of an internal study group aimed at getting a good overview of artificial intelligence (AI) and machine learning (ML), we conducted a deep dive into the world of fast.ai. is a deep learning Python library that is primarily used for adding higher-level functionality in standard deep learning domains.

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U-Next

MARCH 1, 2023

You can find many Artificial Intelligence applications in this blog that you can use as project ideas for your academic assignments or personal growth. Datasets are obtained, and forecasts are made using a regression approach. You’ll get to experience Python programming and machine learning techniques.

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Data Engineering Weekly

DECEMBER 24, 2023

[link] Google: Advancements in machine learning for machine learning Google writes about exciting advancements in ML for ML. The blog explores how Google uses ML to improve the efficiency of ML workloads!

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ProjectPro

JANUARY 31, 2023

Data scientists and machine learning engineers often come across this scenario where the data for their project is not sufficient for training a machine learning model, often resulting in poor performance. Given enough training data, machine learning models can smoothly solve challenging problems.

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DareData

DECEMBER 16, 2023

In my last blog post, we’ve learned how to work with PyTorch tensors , the most important object in the PyTorch library. Tensors are the backbone of deep learning models so naturally we can use them to fit simpler machine learning models to our datasets. of the song Song loudness Song tempo.

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Knowledge Hut

MAY 9, 2024

In this blog post, we will look at some of the world's highest paying data science jobs, what they entail, and what skills and experience you need to land them. Skills Required Skills necessary for AI engineers are programming languages, statistics, deep learning, natural language processing, and problem-solving with communication skills.

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DareData

JANUARY 13, 2024

Continuing the Pytorch series, in this post we’ll learn about how non-linearities help solve complex problems in the context of neural networks In the last blog posts of the PyTorch Introduction series, we spoke about introduction to tensor objects and building a simple linear model using PyTorch. Let’s start!

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Knowledge Hut

NOVEMBER 27, 2023

Developing technical skills is essential, starting with foundational knowledge in mathematics, including calculus and linear algebra, which underpin machine learning and deep learning concepts. Through the article, we will learn what data scientists do, and how to transits to a data science career path.

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RandomTrees

APRIL 30, 2024

By using deep learning algorithms and large datasets, researchers can recognize favorable drug candidates more quickly and accurately. Generative AI algorithms can examine various datasets, like electronic health records, genomic data, and environmental factors, to recognize patterns and risk factors related to various diseases.

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