IBM Journey to AI blog

DECEMBER 20, 2023

Machine learning types Machine learning algorithms fall into five broad categories: supervised learning, unsupervised learning, semi-supervised learning, self-supervised and reinforcement learning. the target or outcome variable is known). temperature, salary).

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Pickl AI

JULY 12, 2024

Machine Learning algorithms are trained on large amounts of data, and they can then use that data to make predictions or decisions about new data. There are three main types of Machine Learning: supervised learning, unsupervised learning, and reinforcement learning.

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Pickl AI

NOVEMBER 18, 2024

The main types are supervised, unsupervised, and reinforcement learning, each with its techniques and applications. Supervised Learning In Supervised Learning , the algorithm learns from labelled data, where the input data is paired with the correct output. spam email detection) and regression (e.g.,

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Pickl AI

AUGUST 1, 2023

Unlike structured data, which resides in databases and spreadsheets, unstructured data poses challenges due to its complexity and lack of standardization. Machine Learning algorithms, including Naive Bayes, Support Vector Machines (SVM), and deep learning models, are commonly used for text classification.

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Pickl AI

FEBRUARY 7, 2025

Types of Machine Learning Machine Learning is divided into three main types based on how the algorithm learns from the data: Supervised Learning In supervised learning , the algorithm is trained on labelled data. Deep Learning, however, thrives on large volumes of data.

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Pickl AI

AUGUST 9, 2024

Variety It encompasses the different types of data, including structured data (like databases), semi-structured data (like XML), and unstructured formats (such as text, images, and videos). Students should learn about Spark’s core concepts, including RDDs (Resilient Distributed Datasets) and DataFrames.

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Pickl AI

SEPTEMBER 12, 2024

Key Components of Data Science Data Science consists of several key components that work together to extract meaningful insights from data: Data Collection: This involves gathering relevant data from various sources, such as databases, APIs, and web scraping. Data Cleaning: Raw data often contains errors, inconsistencies, and missing values.

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