For those with some machine learning experience, this course provides a deeper dive into deep learning with TensorFlow. It covers advanced topics like building custom neural networks, and working with text and sequence data.
This course covers advanced techniques for creating more accurate predictive models using ensembles and metamodeling.
This intermediate-level course explores advanced prompting techniques such as chain-of-thought, tree-of-thought, and directional stimulus prompting to get more out of large language models.
For those with a basic understanding of SQL, this course delves into more advanced querying techniques that can be used for in-depth data exploration.
This course delves into more advanced techniques for time series forecasting, going beyond the basics to cover more complex models and scenarios.
This course provides guidance on balancing AI innovation with risk mitigation, covering frameworks for AI governance, Safety by Design strategies, risk assessment methods, red teaming approaches, and regulatory compliance essentials. It is designed for professionals in Trust & Safety, AI engineering, policy, and brand management.
Explore how to make powerful, accurate predictions with ensemble learners, one of the most common classes of machine learning algorithms.
This course focuses on cleaning, normalizing, and creating features to improve the performance of machine learning models.
This course shows you how to apply supervised learning techniques to real-world problems, focusing on both classification and regression tasks. You'll start with basic models and advance to more complex algorithms like decision trees and XGBoost.
A LinkedIn Learning course that explores the intersection of causal inference and machine learning, teaching how to build more robust and interpretable models.
Learn how to identify and fix common data quality issues in R, including missing values, outliers, and inconsistent data.
This course offers a beginner-friendly introduction to the core concepts of computer vision using Python. You will learn to manipulate images, detect features like faces and eyes, and perform object recognition with popular libraries like OpenCV and Dlib.
This course provides a comprehensive overview of data cleaning techniques in Python, from identifying and handling missing data to dealing with inconsistent data formats.
This beginner-level course teaches how to develop curation files to document information about datasets and related business processes. It is a foundational course for anyone looking to get into data curation.
This LinkedIn Learning course provides a practical introduction to hypothesis testing for data science. You will learn about the different types of hypothesis tests and how to apply them to real-world data. The course includes hands-on exercises using Python.
A concise, beginner-friendly course that provides a quick dive into the essentials of crafting effective prompts for various generative AI systems. It's packed with insights despite its short duration.
This course provides a foundational understanding of what data analytics is and the role of a data analyst. It covers topics like thinking like an analyst and gathering useful data.
This course focuses on supervised learning specifically with neural networks, covering deep neural networks, convolutional networks, and sequence classifiers.
This course provides a foundational understanding of linear regression, one of the most important algorithms in machine learning and AI. It covers the theory and practical implementation of linear regression.
Learn about the modeling techniques and experimental designs that allow you to establish causal inference, and gain insights into causal inference from observational studies.
This course provides a practical introduction to machine learning using Python. It covers the entire machine learning workflow, from data preparation and feature engineering to model building and evaluation.
A learning path that provides a comprehensive overview of how people analytics and AI are transforming human resources. The courses cover topics such as using data and AI to make better hiring decisions, improve employee performance, and reduce turnover. It touches upon how AI can be used for talent assessment and understanding workforce dynamics.
This course provides a step-by-step guide to using Python for data analysis, including data cleaning, manipulation, and visualization for EDA.
This course focuses on the data preparation phase of machine learning projects, including techniques for cleaning and transforming data using Python.
A course on LinkedIn Learning covering the fundamentals of time series analysis using Python, including data preparation, modeling, and forecasting.
Learn how to create a variety of visualizations in Python using Matplotlib and Seaborn to effectively explore and present your data.
This is the first in a series of courses on statistics foundations from LinkedIn Learning. This course covers the basics of descriptive statistics, including measures of central tendency and variability. The course is designed for beginners.
This intermediate-level course explains how to create one of the most common types of machine learning: supervised learning models.
A collection of courses on LinkedIn Learning focused on optimization techniques for machine learning and data science.
A specialized course that explores how artificial intelligence can enhance UX design processes. It covers practical tools, ethical considerations, and strategies to create inclusive and effective AI-driven designs.
This course explores how AI tools can enhance the UX design workflow, covering ideation, prototyping, and user testing, with a focus on balancing AI assistance with human creativity.
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