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.
Description for ML Theory & Hands-on: Python Specialization
Features of Course
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.
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.
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.
Develop and evaluate machine learning models using regression, trees, and unsupervised techniques to address various business challenges.
Featured Tools
Explore the transformative impact of generative AI on businesses and careers, and its potential to enhance productivity across various sectors.
The course outlines techniques for establishing a data science environment on Azure and conducting predictive model training and data experimentation.
Generative AI facilitates daily tasks, decision-making, and idea generation, emphasizing responsible use, leveraging prompting techniques, and staying updated on AI advancements.
The course provides comprehensive coverage of AI and ML's increasing integration, structured into three sections focusing on business strategy, fundamental technologies, and hands-on projects, to aid in strategy development and technical planning.
With an emphasis on time series prediction using RNNs and ConvNets, this course educates software developers on how to create scalable AI models using TensorFlow.