ML Theory & Hands-on: Python Specialization
Learn to develop, assess, and enhance machine learning models using Python libraries, covering introductory deep learning, supervised, and unsupervised learning algorithms.
Unsupervised Learning,Python Programming,Deep Learning,hyperparameter tuning,Supervised Learning
Description for ML Theory & Hands-on: Python Specialization
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 21
Offered by: On Coursera provided by University of Colorado Boulder
Duration: 3 months at 10 hours a week
Schedule: Flexible
Pricing for ML Theory & Hands-on: Python Specialization
Use Cases for ML Theory & Hands-on: Python Specialization
FAQs for ML Theory & Hands-on: Python Specialization
Reviews for ML Theory & Hands-on: Python Specialization
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for ML Theory & Hands-on: Python Specialization
Learn to leverage Generative AI for automation, software development, and optimizing outputs with Prompt Engineering.
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.
Use AI skills to advance your engineering career. Acquire practical knowledge regarding deep learning methodologies for computer vision.
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
Acquire the knowledge necessary to direct your AI voyage. In this program, a CEO will instruct you on how to become a hands-on, AI-powered leader who is prepared to unlock the transformative potential of GenAI for your organization.
Begin your professional journey as an AI Product Manager. Develop generative AI and product management skills that are in high demand to be job-ready in six months or less.
Achieve a professional status as an AI Engineer. Acquire the knowledge necessary to develop next-generation applications that are propelled by generative AI, a skill that is indispensable for startups, agencies, and large corporations.
This learning path provides a thorough overview of generative AI. This specialization delves into the ethical considerations that are essential for the responsible development and deployment of AI, as well as the foundations of large language models (LLMs) and their diverse applications.
Delve into the historical evolution of Generative AI and AI, exploring diverse models and their applications in business contexts for optimized decision-making and innovation.