IBM Journey to AI blog

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. However, the growing influence of ML isn’t without complications.

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Pickl AI

OCTOBER 18, 2023

How to Scale Your Data Quality Operations with AI and ML: In the fast-paced digital landscape of today, data has become the cornerstone of success for organizations across the globe. Every day, companies generate and collect vast amounts of data, ranging from customer information to market trends.

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IBM Journey to AI blog

JUNE 5, 2024

Given the volume of SaaS apps on the market (more than 30,000 SaaS developers were operating in 2023) and the volume of data a single app can generate (with each enterprise businesses using roughly 470 SaaS apps), SaaS leaves businesses with loads of structured and unstructured data to parse. What are application analytics?

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Dataconomy

AUGUST 9, 2023

By scrutinizing data packets that constitute network traffic, NTA aims to establish baselines of normal behavior, detect deviations, and take appropriate actions. This is where the power of machine learning (ML) comes into play. One of the primary applications of ML in network traffic analysis is anomaly detection.

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Towards AI

APRIL 4, 2024

Machine learning (ML) has proven that it is here with us for the long haul, everyone who had their doubts by calling it a phase should by now realize how wrong they are, ML has being used in various sector’s of society such as medicine, geospatial data, finance, statistics and robotics.

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Mlearning.ai

FEBRUARY 15, 2024

Data Science Project — Predictive Modeling on Biological Data Part III — A step-by-step guide on how to design a ML modeling pipeline with scikit-learn Functions. Photo by Unsplash Earlier we saw how to collect the data and how to perform exploratory data analysis. Now comes the exciting part ….

Data Science 52

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Pickl AI

MAY 10, 2023

Being an important component of Data Science, the use of statistical methods are crucial in training algorithms in order to make classification. Certainly, these predictions and classification help in uncovering valuable insights in data mining projects. Consequently, each brand of the decision tree will yield a distinct result.

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Dataconomy

MAY 25, 2023

Here are some ways AI enhances IoT devices: Advanced data analysis AI algorithms can process and analyze vast volumes of IoT-generated data. By leveraging techniques like machine learning and deep learning, IoT devices can identify trends, anomalies, and patterns within the data.

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Mlearning.ai

FEBRUARY 2, 2023

Mind Map: Mistakes in ML model training This blog highlights some important mistakes that one can make while training a machine learning model. Machine Learning model training is the process of teaching a model how to recognize patterns in data. What can go wrong in ML model training?

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Defined.ai blog

AUGUST 10, 2023

Businesses must understand how to implement AI in their analysis to reap the full benefits of this technology. In the following sections, we will explore how AI shapes the world of financial data analysis and address potential challenges and solutions.

Business Intelligence 52

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ODSC - Open Data Science

AUGUST 15, 2023

Big Data Analysis with PySpark Bharti Motwani | Associate Professor | University of Maryland, USA Ideal for business analysts, this session will provide practical examples of how to use PySpark to solve business problems. Finally, you’ll discuss a stack that offers an improved UX that frees up time for tasks that matter.

Machine Learning 52

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Pickl AI

FEBRUARY 7, 2025

ML works with structured data, while DL processes complex, unstructured data. ML requires less computing power, whereas DL excels with large datasets. Introduction In todays world of AI, both Machine Learning (ML) and Deep Learning (DL) are transforming industries, yet many confuse the two.

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Pickl AI

APRIL 3, 2023

Accordingly, Machine Learning allows computers to learn and act like humans by providing data. Apparently, ML algorithms ensure to train of the data enabling the new data input to make compelling predictions and deliver accurate results. Significantly, Supervised Learning uses offline analysis.

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Pickl AI

NOVEMBER 28, 2024

Understanding Machine Learning algorithms and effective data handling are also critical for success in the field. Introduction Machine Learning ( ML ) is revolutionising industries, from healthcare and finance to retail and manufacturing. Fundamental Programming Skills Strong programming skills are essential for success in ML.

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Pickl AI

JULY 12, 2024

Scikit-learn: A simple and efficient tool for data mining and data analysis, particularly for building and evaluating machine learning models. Here are a few of the key concepts that you should know: Machine Learning (ML) This is a type of AI that allows computers to learn without being explicitly programmed.

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ODSC - Open Data Science

JANUARY 5, 2024

Without this library, data analysis wouldn’t be the same without pandas, which reign supreme with its powerful data structures and manipulation tools. Pandas provides a fast and efficient way to work with tabular data. It is widely used in data science, finance, and other fields where data analysis is essential.

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Pickl AI

DECEMBER 4, 2024

This explosive growth is driven by the increasing volume of data generated daily, with estimates suggesting that by 2025, there will be around 181 zettabytes of data created globally. Understand data structures and explore data warehousing concepts to efficiently manage and retrieve large datasets.

Data Science 52

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DagsHub

APRIL 21, 2024

A traditional machine learning (ML) pipeline is a collection of various stages that include data collection, data preparation, model training and evaluation, hyperparameter tuning (if needed), model deployment and scaling, monitoring, security and compliance, and CI/CD. What is MLOps?

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IBM Journey to AI blog

JULY 6, 2023

Data science solves a business problem by understanding the problem, knowing the data that’s required, and analyzing the data to help solve the real-world problem. Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on learning from what the data science comes up with.

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Heartbeat

SEPTEMBER 28, 2023

To address this challenge, data scientists harness the power of machine learning to predict customer churn and develop strategies for customer retention. Continuous Experiment Tracking with Comet ML Comet ML is a versatile tool that helps data scientists optimize machine learning experiments.

Machine Learning 52

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Ocean Protocol

MARCH 11, 2024

METAR, Miami International Airport (KMIA) on March 9, 2024, at 15:00 UTC In the recently concluded data challenge hosted on Desights.ai , participants used exploratory data analysis (EDA) and advanced artificial intelligence (AI) techniques to enhance aviation weather forecasting accuracy.

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Pickl AI

JANUARY 3, 2024

Feature engineering in machine learning is a pivotal process that transforms raw data into a format comprehensible to algorithms. Through Exploratory Data Analysis , imputation, and outlier handling, robust models are crafted. Hence, it is important to discuss the impact of feature engineering in Machine Learning.

Machine Learning 52

Machine Learning Machine Learning Exploratory Data Analysis Cross Validation 52

Mlearning.ai

MAY 23, 2023

The following Venn diagram depicts the difference between data science and data analytics clearly: 3. Data analysis can not be done on a whole volume of data at a time especially when it involves larger datasets. Overfitting: The model performs well only for the sample training data.

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Pickl AI

SEPTEMBER 23, 2024

Summary: The blog explores the synergy between Artificial Intelligence (AI) and Data Science, highlighting their complementary roles in Data Analysis and intelligent decision-making. These components solve complex problems and drive decision-making in various industries.

Artificial Intelligence 52

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Pickl AI

NOVEMBER 5, 2024

AI encompasses various subfields, including Machine Learning (ML), Natural Language Processing (NLP), robotics, and computer vision. Together, Data Science and AI enable organisations to analyse vast amounts of data efficiently and make informed decisions based on predictive analytics.

Data Science 52

Data Science Artificial Intelligence Artificial Intelligence Machine Learning 52

Pickl AI

APRIL 28, 2023

Here is the tabular representation of the same: Technical Skills Non-technical Skills Programming Languages: Python, SQL, R Good written and oral communication Data Analysis: Pandas, Matplotlib, Numpy, Seaborn Ability to work in a team ML Algorithms: Regression Classification, Decision Trees, Regression Analysis Problem-solving capability Big Data: (..)

Data Scientist 52

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Mlearning.ai

FEBRUARY 15, 2023

Some important things that were considered during these selections were: Random Forest : The ultimate feature importance in a Random forest is the average of all decision tree feature importance. A random forest is an ensemble classifier that makes predictions using a variety of decision trees.

Cross Validation 52

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phData

SEPTEMBER 10, 2024

In this blog, we’ll look at how to apply Generative AI on top of predictive ML models to enhance explainability. Using Large Language Models (LLMs) on Snowflake AI Data Cloud , we’ll extract detailed natural-language descriptions to help business associates understand complex quantitative predictions.

Machine Learning 52

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Pickl AI

SEPTEMBER 3, 2023

On the other hand, 48% use ML and AI for gaining insights into the prospects and customers. An ensemble of decision trees is trained on both normal and anomalous data. In 2023, the expected reach of the AI market is supposed to reach the $500 billion mark and in 2030 it is supposed to reach $1,597.1

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Algorithm 52

Heartbeat

FEBRUARY 8, 2023

R is frequently used for statistical software development, data analysis, and data visualisation because it can handle large data sets with ease. This programming language offers a variety of methods for model training and evaluation, making it perfect for machine learning projects that need a lot of data processing.

Natural Language Processing 52

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Heartbeat

MAY 29, 2023

It is also essential to evaluate the quality of the dataset by conducting exploratory data analysis (EDA), which involves analyzing the dataset’s distribution, frequency, and diversity of text. The ML process is cyclical — find a workflow that matches. Check out our expert solutions for overcoming common ML team problems.

Machine Learning 78

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Mlearning.ai

JANUARY 29, 2024

Data Science Project — Build a Decision Tree Model with Healthcare Data Using Decision Trees to Categorize Adverse Drug Reactions from Mild to Severe Photo by Maksim Goncharenok Decision trees are a powerful and popular machine learning technique for classification tasks.

Decision Trees 52

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DagsHub

SEPTEMBER 16, 2024

Model Visualization provides insights into the decision-making process of a model, especially for complex models like neural networks. By visually interpreting the performance metrics, it helps in the efficient evaluation of the ML models. For using Comet, you will need the API Key which you need to create on the Comel ML platform.

Machine Learning 52

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Dataconomy

SEPTEMBER 1, 2023

Heart disease stands as one of the foremost global causes of mortality today, presenting a critical challenge in clinical data analysis. Leveraging hybrid machine learning techniques, a field highly effective at processing vast healthcare data volumes is increasingly promising in effective heart disease prediction.

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