ML in Accounting with Python
For the purpose of accounting data analytics, the course educates students on the application and optimization of machine learning models in Python.
Description for ML in Accounting with Python
Machine Learning Algorithms: Comprehend the various machine learning models and their applications in order to address accounting-related issues.
Practical Application with Python:** Learn how to apply machine learning models to datasets using Python in Jupyter Notebook through practical application.
Model Evaluation: Acquire the ability to assess the accuracy and efficacy of machine learning models.
Model Optimization: Investigate methods for optimizing machine learning models to enhance their efficacy.
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 22
Offered by: On Coursera provided by University of Illinois Urbana-Champaign
Duration: 64 hours (approximately)
Schedule: Flexible
Pricing for ML in Accounting with Python
Use Cases for ML in Accounting with Python
FAQs for ML in Accounting with Python
Reviews for ML in Accounting with Python
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for ML in Accounting with Python
Learn to leverage Generative AI for automation, software development, and optimizing outputs with Prompt Engineering.
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.
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 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.
Acquire practical skills to build a generative AI application by constructing a retrieval augmented generation (RAG) system using data, Qdrant, and LLMs.
Gain expertise in deploying and managing LLMs on Azure, optimizing interactions with Semantic Kernel, and applying Retrieval Augmented Generation (RAG) techniques.
Gain expertise in deploying and managing LLMs on Azure, optimizing interactions with Semantic Kernel, and applying Retrieval Augmented Generation (RAG) techniques.
Master Python programming for software development and data science, including core logic, Jupyter Notebooks, libraries like NumPy and Pandas, and web data gathering with Beautiful Soup and APIs.
Acquire practical skills in fundamental machine learning models and their applications using PyTorch, as utilized by leading tech companies.
Featured Tools
Master the AI and machine learning toolkit. Mathematics for Machine Learning and Data Science is a Specialization that is accessible to beginners. In this program, you will acquire the basic mathematics tools of machine learning, including calculus, linear algebra, statistics, and probability.
The course provides learners with the necessary skills to transform B2B marketing by implementing AI-driven strategies and machine learning.
Modern and Practical Statistical Thinking for All. Utilize Python for statistical visualization, inference, and modeling.
Gain the skills needed for a machine learning engineering role and prepare for the Google Cloud Professional Machine Learning Engineer certification exam by learning to design, build, and productize ML models using Google Cloud technologies.
Construct and train neural networks and tree ensemble methods using TensorFlow, while applying effective machine learning practices for real-world data generalization.