IBM Journey to AI blog

DECEMBER 20, 2023

The most common unsupervised learning method is cluster analysis, which uses clustering algorithms to categorize data points according to value similarity (as in customer segmentation or anomaly detection ).

Machine Learning 75

Machine Learning Machine Learning Supervised Learning Clustering 75

DataRobot

DECEMBER 20, 2021

Image recognition is one of the most relevant areas of machine learning. Deep learning makes the process efficient. However, not everyone has deep learning skills or budget resources to spend on GPUs before demonstrating any value to the business. DataRobot Visual AI. Multimodal Clustering.

Clustering 97

Clustering Deep Learning Deep Learning Exploratory Data Analysis 97

Data Science Blog

FEBRUARY 23, 2023

1, Data is the new oil, but labeled data might be closer to it Even though we have been in the 3rd AI boom and machine learning is showing concrete effectiveness at a commercial level, after the first two AI booms we are facing a problem: lack of labeled data or data themselves.

Supervised Learning 264

Supervised Learning Machine Learning Machine Learning Deep Learning 264

Smart Data Collective

JUNE 4, 2021

Basic knowledge of statistics is essential for data science. Statistics is broadly categorized into two types – Descriptive statistics – Descriptive statistics is describing the data. Visual graphs are the core of descriptive statistics. Clustering (Unsupervised). Deep Learning. Ensembling.

Data Science 104

Data Science Exploratory Data Analysis Machine Learning Machine Learning 104

Pickl AI

JANUARY 23, 2023

The machine learning models developed by TensorFlow are simple to construct, capable of producing reliable results, and allow for effective experimentation in research. TensorBoard, a large package that is typically overlooked, is included within TensorFlow and is used for data visualization.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

IBM Journey to AI blog

DECEMBER 19, 2023

The main difference being that while KNN makes assumptions based on data points that are closest together, LOF uses the points that are furthest apart to draw its conclusions. Unsupervised learning Unsupervised learning techniques do not require labeled data and can handle more complex data sets.

Machine Learning 65

Machine Learning Machine Learning Supervised Learning K-nearest Neighbors 65

Dataconomy

AUGUST 9, 2023

It works by finding the hyperplane that best separates different classes of data points, enabling it to classify or detect anomalies effectively Deep Learning : Deep neural networks , a subset of ML, are particularly adept at handling complex and unstructured data like network traffic analysis. All too long to do?

Machine Learning 91

Machine Learning Machine Learning ML ML 91

Dataconomy

MAY 16, 2023

They employ statistical and mathematical techniques to uncover patterns, trends, and relationships within the data. Data scientists possess a deep understanding of statistical modeling, data visualization, and exploratory data analysis to derive actionable insights and drive business decisions.

Data Scientist 156

Data Scientist ML ML Machine Learning 156

Towards AI

JUNE 27, 2023

Its comprehensive ecosystem of tools, libraries, and community resources aids developers in constructing robust, scalable machine learning models. Scikit Learn Scikit Learn is a comprehensive machine learning tool designed for data mining and large-scale unstructured data analysis.

Machine Learning 94

Machine Learning Machine Learning ML ML 94

Pickl AI

JULY 25, 2023

Proficient in programming languages like Python or R, data manipulation libraries like Pandas, and machine learning frameworks like TensorFlow and Scikit-learn, data scientists uncover patterns and trends through statistical analysis and data visualization. Big Data Technologies: Hadoop, Spark, etc.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Mlearning.ai

JANUARY 17, 2024

Traditional AI can recognize, classify, and cluster, but not generate the data it is trained on. Their generative sisters, on the other hand, are pre-trained on giant amounts of data from human domains. Deep learning neural network. Let’s play the comparison game. Image credit: Yang, Jingfeng et.

AI 98

AI AI Deep Learning Deep Learning 98

Mlearning.ai

MAY 23, 2023

With the advancement of technology, machine learning, and computer vision techniques can be used to develop automated solutions for leaf disease detection. In this article, we will discuss the development of a Leaf Disease Detection Flask App that uses a deep learning model to automatically detect the presence of leaf diseases.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Python 52

Pickl AI

JULY 18, 2023

Making data-driven decisions: Data science empowers you to make informed decisions by analyzing and interpreting data. Addressing real-world problems: Data science enables you to tackle real-world challenges across diverse domains, such as healthcare, finance, marketing, and social sciences.

Computer Science 52

Computer Science Computer Science Data Science Machine Learning 52

Mlearning.ai

MAY 21, 2023

With the advancement of technology, machine learning, and computer vision techniques can be used to develop automated solutions for leaf disease detection. In this article, we will discuss the development of a Leaf Disease Detection Flask App that uses a deep learning model to automatically detect the presence of leaf diseases.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Python 52

AWS Machine Learning Blog

NOVEMBER 1, 2023

Visual language processing (VLP) is at the forefront of generative AI, driving advancements in multimodal learning that encompasses language intelligence, vision understanding, and processing. He focuses on developing scalable machine learning algorithms.

AWS 87

AWS Clustering Deep Learning Deep Learning 87

Pickl AI

JULY 20, 2023

Descriptive Analytics Projects: These projects focus on summarizing historical data to gain insights into past trends and patterns. Examples include generating reports, dashboards, and data visualizations to understand business performance, customer behavior, or operational efficiency. ImageNet).

Analytics 40

Analytics Analytics Big Data Big Data 40

Mlearning.ai

JUNE 6, 2023

In today’s blog, we will see some very interesting Python Machine Learning projects with source code. This list will consist of Machine learning projects, Deep Learning Projects, Computer Vision Projects , and all other types of interesting projects with source codes also provided. Working Video of our App [link] 12.

Machine Learning 52

Machine Learning Machine Learning Python Deep Learning 52

PyImageSearch

MAY 1, 2023

Redmon and Farhadi (2017) published YOLOv2 at the CVPR Conference and improved the original model by incorporating batch normalization, anchor boxes, and dimension clusters. The Python code for data visualization ( Figure 5 ) and class distribution graph ( Figure 6 ) computation is provided inside the Google Colab Notebook of this tutorial!

Deep Learning 107

Deep Learning Deep Learning Python Algorithm 107

Pickl AI

APRIL 26, 2024

This comprehensive blog outlines vital aspects of Data Analyst interviews, offering insights into technical, behavioural, and industry-specific questions. It covers essential topics such as SQL queries, data visualization, statistical analysis, machine learning concepts, and data manipulation techniques.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

Analytics Vidhya

FEBRUARY 10, 2022

This article was published as a part of the Data Science Blogathon. Introduction Phonepe, Google Pay (Tez) are ubiquitous names in the Indian payment ecosystem and the top two players in the area. According to Phonepe pulse report, it has133 million monthly active users as of July’21. For the Q3-21 quarter, the total transactions were 526.8

Data Science 240

Data Science Analytics Analytics Clustering 240

Mlearning.ai

MAY 23, 2023

Once the exploratory steps are completed, the cleansed data is subjected to various algorithms like predictive analysis, regression, text mining, recognition patterns, etc depending on the requirements. In the final stage, the results are communicated to the business in a visually appealing manner. What is deep learning?

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

ODSC - Open Data Science

JANUARY 29, 2024

Data science methodologies and skills can be leveraged to design these experiments, analyze results, and iteratively improve prompt strategies. Using skills such as statistical analysis and data visualization techniques, prompt engineers can assess the effectiveness of different prompts and understand patterns in the responses.

Data Science 52

Data Science Machine Learning Machine Learning AI 52