ProjectPro

JUNE 6, 2025

Cognitive Engines: Deep learning and machine learning models that enable decision-making, reasoning, and strategy development. Memory Systems: Persistent data structures that allow agents to learn from past interactions. What skills are necessary to develop and implement Agentic AI systems? Avoid repeating errors.

Medical 64

Medical Healthcare Deep Learning Hospitality 64

ProjectPro

JUNE 6, 2025

Nucleus Research has found that business intelligence with data visualization capabilities will offer a return on investment of $13.01 With how much expenditure is made and time spent on data collection and processing, getting the most out of data is naturally a well-in-demand skill. for every dollar spent.

Coding 67

Coding Project Machine Learning Datasets 67

ProjectPro

JUNE 6, 2025

TensorFlow) Strong communication and presentation skills Data Scientist Salary According to the Payscale, Data Scientists earn an average of $97,680. Experience is one of the most significant factors that determine the data scientist salary. They also help data science professionals to execute projects on time.

Data Science 56

Data Science Data Mining Data Architect BI 56

ProjectPro

JUNE 6, 2025

Deep learning models have been used recently for bioactivity and synthesis prediction for drugs and vaccines in addition to molecular design. Zomato uses ML and AI to boost their business growth, with the massive amount of data collected over the years from food orders and user consumption patterns.

Data Science 67

Data Science Project Food Pharmaceutical 67

RandomTrees

NOVEMBER 25, 2024

Generative AI employs ML and deep learning techniques in data analysis on larger datasets, resulting in produced content that has a creative touch but is also relevant. In the telecom sector, this technology is assisting with operations, customer satisfaction as well as business development.

Telecommunication 52

Telecommunication IT Unstructured Data Data Mining 52

ProjectPro

JUNE 6, 2025

FAQs How to Start an AI Project: The Prerequisites Implementing AI systems requires a solid understanding of its various subsets, such as Data Analysis , Machine Learning (ML) , Deep Learning (DL) , and Natural Language Processing (NLP). 1) Data High-quality data is the foundation of most AI projects.

Project 40

Project Deep Learning Datasets Machine Learning 40

ProjectPro

JUNE 6, 2025

This blog explores synthetic data generation, highlighting its importance for overcoming data scarcity. It covers various methods, from theoretical to deep learning approaches, and provides practical Python examples. FAQs What is Synthetic Data Generation?

Healthcare 40

Healthcare Medical Datasets Machine Learning 40

AltexSoft

MAY 12, 2022

Aiming at understanding sound data, it applies a range of technologies, including state-of-the-art deep learning algorithms. Audio data transformation basics to know. It also comes with pretrained machine learning and deep learning models that can be used for speech analysis and sound recognition.

Machine Learning 122

Machine Learning Building Deep Learning Healthcare 122

Knowledge Hut

JANUARY 18, 2024

It is an interdisciplinary science with multiple approaches, and advancements in Machine Learning and deep learning are creating a paradigm shift in many sectors of the IT industry across the globe. SQL for data migration 2. Python libraries such as pandas, NumPy, plotly, etc.

Data Science 98

Data Science Deep Learning Business Analyst Data Mining 98

ProjectPro

JUNE 6, 2025

Data preparation for machine learning algorithms is usually the first step in any data science project. It involves various steps like data collection, data quality check, data exploration, data merging, etc. Imagine yourself as someone who is learning Jazz dance form.

Data Preparation 45

Data Preparation Machine Learning Project IT 45

ProjectPro

JUNE 6, 2025

Deep Learning Frameworks- TensorFlow , PyTorch, and Keras streamline model development of many deep learning and machine learning models. Step 2: Choosing the Right AI Tools and Frameworks Selecting the appropriate AI tools and frameworks is crucial for building an effective generative AI model.

Building 52

Building Banking SQL Deep Learning 52

ProjectPro

JUNE 6, 2025

Data Engineers, Data Scientists, Data Architects have become significant job titles in the market, and the opportunities keep soaring. Hence, having diverse types of machine learning projects for your resume helps recruiters understand your problem-solving approach to various business problems.

Machine Learning 52

Machine Learning Coding Project Deep Learning 52

Edureka

MARCH 26, 2025

Data Collection and Preprocessing: DeepBrain AI begins by putting together big sets of data that include speech patterns, text, and other useful information. Cleansing and cleaning this data makes sure that it can be used to train machine learning models. Let’s break it down.

Education 45

Education Media Deep Learning Healthcare 45

ProjectPro

JUNE 6, 2025

Build and deploy ETL/ELT data pipelines that can begin with data ingestion and complete various data-related tasks. Handle and source data from different sources according to business requirements.

Data Engineering 58

Data Engineering Data Engineer Engineering Amazon Web Services 58

ProjectPro

JUNE 6, 2025

Become a Job-Ready Data Engineer with Complete Project-Based Data Engineering Course ! Big data is often characterized by the seven V's: Volume , Variety , Velocity, Variability, Veracity, Visualization, and Value of data. specialist to learn big data. 2) Can you learn big data for free?

Big Data 45

Big Data Big Data Skills Scala Hadoop 45

Knowledge Hut

DECEMBER 29, 2023

Professionals from a variety of disciplines use data in their day-to-day operations and feel the need to understand cutting-edge technology to get maximum insights from the data, therefore contributing to the growth of the organization. Engineering and problem-solving abilities based on Big Data solutions may also be taught.

Data Science 98

Data Science Data Mining Deep Learning Programming Language 98

Knowledge Hut

MAY 31, 2023

Artificial intelligence (AI) projects are software-based initiatives that utilize machine learning, deep learning, natural language processing, computer vision, and other AI technologies to develop intelligent programs capable of performing various tasks with minimal human intervention. Let us get started!

Project 96

Project Transportation Healthcare Deep Learning 96

Cloudera

NOVEMBER 19, 2020

Analyzing historical data is an important strategy for anomaly detection. The modeling process begins with data collection. Here, Cloudera Data Flow is leveraged to build a streaming pipeline which enables the collection, movement, curation, and augmentation of raw data feeds.

Government 119

Government Machine Learning Algorithm Raw Data 119

Confluent

FEBRUARY 6, 2019

Uber expanded Michelangelo “to serve any kind of Python model from any source to support other Machine Learning and Deep Learning frameworks like PyTorch and TensorFlow [instead of just using Spark for everything].”. Data scientists love Python, period. These standards have pros and cons.

Machine Learning 107

Machine Learning Python Kafka Java 107

ProjectPro

JUNE 6, 2025

These diverse applications highlight AI's field of impact, and we are about to look at more such use cases that demonstrate how AI is reshaping data analytics in even more specific ways. It can also automate data analysis tasks like data wrangling , error correction, and standardization, which usually take significant time.

Data Analytics 40

Data Analytics Healthcare Machine Learning Datasets 40

ProjectPro

JUNE 6, 2025

We also have a few tips and guidelines for beginner-level and senior data engineers on how they can build an impressive resume. 180 zettabytes- the amount of data we will likely generate by 2025! This is what data engineering does. Data engineering entails creating and developing data collection, storage, and analysis systems.

Data Engineering 40

Data Engineering Data Engineer Recruitment Building 40

ProjectPro

JUNE 6, 2025

Model training frameworks TensorFlow: Developed by Google, TensorFlow is an open-source machine learning framework that provides a comprehensive ecosystem for constructing and training machine learning models. It supports deep learning algorithms and distributed computing. What are the stages of ML lifecycle?

Machine Learning 40

Machine Learning Data Preparation Data Science Algorithm 40

ProjectPro

JUNE 6, 2025

AWS offers a comprehensive set of services and tools for data storage, processing, and analysis, and a Data Scientist specializing in AWS utilizes these services to extract valuable information from data.

AWS 40

AWS Amazon Web Services Cloud Computing Machine Learning 40

Knowledge Hut

JULY 4, 2023

It involves extracting meaningful features from the data and using them to make informed decisions or predictions. Data Collection and Pre-processing The first step is to collect the relevant data that contains the patterns of interest. The steps involved in it can be summarized as follows: 1.

Machine Learning 98

Machine Learning Medical Algorithm Deep Learning 98

ProjectPro

JUNE 6, 2025

PyTorch for fine-tuning and transfer learning. Solution Approach Step 1: Data Collection The FAO dataset provides historical pesticide usage trends (1990–2021) across different regions and crops. Remote sensing (satellite data) will provide macro-level soil monitoring insights.

Project 40

Project Building Google Cloud AWS 40

ProjectPro

JUNE 6, 2025

At its core, a machine learning system leverages the power of data to refine and boost performance over time iteratively. It further preprocesses the raw data by performing tasks like handling missing values, converting time formats, and normalizing user behavior- all of which are crucial for meaningful insights.

Machine Learning 40

Machine Learning Process Entertainment Algorithm 40

ProjectPro

JUNE 6, 2025

Data science is the study of large volumes of data to deduce unknown patterns. Data Science involves leveraging machine learning algorithms, deep learning algorithms, Natural Language Processing methods, etc. Data Science field primarily focuses on extracting meaningful insights from the given dataset.

Software Engineering 52

Software Engineering Software Engineer Engineering Programming Language 52

Knowledge Hut

MAY 2, 2024

It means computers learn and there are many concepts, methods, algorithms and processes involved in making this happen. Let us try to understand some of the more important machine learning terms. Three concepts – artificial intelligence, machine learning and deep learning – are often thought to be synonymous.

Machine Learning 98

Machine Learning IT Deep Learning Algorithm 98

ProjectPro

JUNE 6, 2025

Hybrid Approach combines rule-based logic with machine learning (ML) techniques and deep learning models, often fine-tuned within LLMs, to improve adaptability and handle a broader range of user inputs with better accuracy. Though effective for basic sentiment tasks, this method may miss context and sarcasm. joy, anger, fear).

Datasets 40

Datasets Deep Learning Media Python 40

ProjectPro

JUNE 6, 2025

Base Model: A foundation model is initially trained on a vast amount of text data, learning general language patterns and structures. Domain-Specific Data: Collect a dataset containing detailed information about return policies, including FAQs, policy documents, and previous customer interactions related to the customer.

Project 40

Project Electronics Medical Datasets 40

ProjectPro

JUNE 6, 2025

Step 2: Collecting and Preparing Data Next, turn your attention to data collection and high quality data preparation, the backbone of any AI model. Data can be sourced from public datasets, internal company databases, or even through web scraping. How much does it cost to Build an AI model?

Building 40

Building Datasets Programming Language Data Preparation 40

AltexSoft

MAY 27, 2022

MIMIC standing for Medical Information Mart for Intensive Care is a freely available database of medical data collected from patients in intensive care units (ICU). There are numerous studies describing experiments with deep learning models trained to predict LOS. MIMIC database. several others.

Hospitality 98

Hospitality Medical Healthcare Algorithm 98

ProjectPro

JUNE 6, 2025

Big Data Engineers are professionals who handle large volumes of structured and unstructured data effectively. They are responsible for changing the design, development, and management of data pipelines while also managing the data sources for effective data collection.

Big Data 40

Big Data Data Engineering Data Engineer Engineering 40

ProjectPro

JUNE 6, 2025

Without data, you're just another person with an opinion. Edwards Deming But what if your data could think for itself? In this blog, we’ll talk about why AI is becoming a must-have in data analytics, what it means when we say “AI in data analytics,” and how companies use it to make smarter, faster decisions.

Data Analytics 40

Data Analytics Unstructured Data Datasets BI 40

Knowledge Hut

MAY 2, 2024

Analysis of data includes Condensation, Summarization, Conclusion etc., The Interpretation step includes drawing conclusions from the data collected as the figures don’t speak for themselves. Statistics used in Machine Learning is broadly divided into two categories, based on the type of analyses they perform on the data.

Machine Learning 98

Machine Learning Algorithm Datasets Deep Learning 98

Knowledge Hut

DECEMBER 26, 2023

They collect and extract data from warehouses using querying techniques, analyze this data and create summary reports of the company's current standings. They suggest recommendations to management to increase the efficiency of the business and develop new analytical models to standardize data collection.

Data Science 97

Data Science BI Data Mining Business Intelligence 97

Knowledge Hut

DECEMBER 26, 2023

However, as we progressed, data became complicated, more unstructured, or, in most cases, semi-structured. This mainly happened because data that is collected in recent times is vast and the source of collection of such data is varied, for example, data collected from text files, financial documents, multimedia data, sensors, etc.

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98