Machine Learning Foundations: A Case Study Approach
This course provides practical experience with machine learning through case studies, concentrating on applying approaches across domains and laying the groundwork for deeper understanding of models and algorithms.
Description for Machine Learning Foundations: A Case Study Approach
Practical Case Analyses: Acquire practical experience through a series of case studies, including forecasting real estate values and suggesting items, to use machine learning methodologies across many sectors.
Machine Learning as a Black Box: Regard machine learning as a black box to concentrate on comprehending relevant problems, aligning them with suitable machine learning techniques, and assessing the quality of outcomes.
Overview of Machine Learning Tasks: Acquire knowledge on aligning certain machine learning tasks, such as sentiment analysis and document retrieval, with appropriate tools and methodologies.
Foundation for Future Learning: Establish a basis for subsequent courses, wherein participants will explore machine learning models, algorithms, and the elements of the machine learning pipeline in greater depth.
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 22
Offered by: On Coursera provided by Google Cloud
Duration: 3 weeks at 6 hours a week
Schedule: Flexible
Pricing for Machine Learning Foundations: A Case Study Approach
Use Cases for Machine Learning Foundations: A Case Study Approach
FAQs for Machine Learning Foundations: A Case Study Approach
Reviews for Machine Learning Foundations: A Case Study Approach
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Machine Learning Foundations: A Case Study Approach
The AI tool empowers non-programmers to construct and deploy AI, featuring data transformation, insights generation, identification of critical drivers, and prediction and forecasting functionalities to enhance business decision-making and planning processes.
Utilize generative AI to advance in the field of data science. Develop hands-on generative AI skills that are in high demand to accelerate your data science career in under one month.
Learn to leverage Generative AI for automation, software development, and optimizing outputs with Prompt Engineering.
Learn machine learning with Google Cloud. End-to-end machine learning experimentation in the real world
Welcome to the 'Gen AI for Code Generation for Python' course, where you will begin a journey to hone and expand your abilities in the field of code generation using Generative AI.
Utilize Generative AI to optimize marketing creativity. Explore the potential of Generative AI to revolutionize and influence your marketing organization.
Learn to use the latest LLM APIs, the LangChain Expression Language (LCEL), and develop a conversational agent.
Master coding basics and create a Hangman game using generative AI tools like Google Bard in a beginner-friendly, 1.5-hour guided project.
Learn the significance, use cases, history, and pros and cons of generative AI in a business context, with a focus on its relationship to machine learning and services at Amazon.
Learn about AI principles and platforms like IBM Watson and Hugging Face, integrate RAG technology for chatbot intelligence, create web apps using Python libraries, and develop interfaces with generative AI models and Python frameworks.
Featured Tools
Learn to use Databricks and MLlib for creating and advancing machine learning models with Spark.
Learn to develop a text processing pipeline and understand LSTM and Recurrent Neural Networks to train and assess deep learning models.
Learn fundamental machine learning principles, including K nearest neighbor, linear regression, and model analysis, with prerequisites of Python programming and basic mathematics.
Students will acquire practical experience in AI development by integrating technical skills with hands-on project development for real-world applications.
Learn to develop a text preprocessing pipeline, understand the theory behind Naive Bayes classifiers, and evaluate their effectiveness after training.
Compare & Review AI Tool Directory AI Course Marketplace
AI Tool Categories
AI Productivity ToolsAI Image GeneratorsAI Text GeneratorsAI Video GeneratorsAI Audio GeneratorsAI Art GeneratorsMisc AI ToolsAI Code GeneratorsFree AI ToolsAI Functions
Marketing AI ToolsAI Tools for EducationAI Tools for ResearchAI Tools for FinanceAI Project Management ToolsLegal AI ToolsAI Tools for E-commerceAI Agents For AutomationAI Tools for BusinessAI Tools for Real EstateAI Course Categories
AI & Machine Learning CoursesGenerative AI CoursesData Science CoursesComputer Science CoursesAI Courses for BeginnersContact Us: info@groupify.ai
Aimed Labs