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
Discover AI terminology, ethical norms, and protocols for responsibly utilizing and citing Generative AI.
Understand foundational knowledge of AI and RegTech, their societal implications, and the discourse around their future integration and obstacles.
Gain extensive knowledge in AI technologies relevant to digital marketing, involving precise data analysis, content creation, and tools for optimizing social media and consumer segmentation.
Learn the skills necessary to operate, optimize, and implement large language models through practical experience with state-of-the-art LLM architectures and open-source resources.
Develop expertise in the exposure and deployment of large language models via application programming interfaces (APIs), configure server environments, and incorporate natural language processing (NLP) functionalities into applications.
Learn proficiency in the construction, deployment, and safeguarding of large language models at scale, utilizing Rust, Amazon Web Services (AWS), and established DevOps best practices.
This course is dedicated to the setting up of GPU-based environments, the deployment of local large language models (LLMs), and their integration into Python applications utilizing open-source tools.
Gain an extensive understanding of TinyML applications, fundamental principles, and the ethical development of artificial intelligence.
Gain proficiency in the automation of software development processes through the utilization of generative artificial intelligence, AI-assisted programming, MLOps, and Amazon Web Services.
Examine how to improve learning and preserve integrity by incorporating morally sound and useful AI tools into evaluation procedures.
Featured Tools
Explore the topic of AI-powered personalization by acquiring the skills necessary to utilize LangChain and ChatGPT.
Examine how to improve learning and preserve integrity by incorporating morally sound and useful AI tools into evaluation procedures.
Discover AI terminology, ethical norms, and protocols for responsibly utilizing and citing Generative AI.
The material equips data engineers to incorporate machine learning models into pipelines while adhering to best practices in collaboration, version control, and artifact management.
Learn the fundamental techniques of supervised and unsupervised learning and apply them to real-world problems to unlock the potential of machine learning.