Applied Data Science with Python Specialization
Learn to perform inferential statistical analysis, assess and improve data visualizations, integrate machine learning into data analysis, and analyze social network connectivity.
Description for Applied Data Science with Python Specialization
Features of Course
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 22
Offered by: On Coursera provided by University of Michigan
Duration: 4 months at 10 hours a week
Schedule: Flexible
Pricing for Applied Data Science with Python Specialization
Use Cases for Applied Data Science with Python Specialization
FAQs for Applied Data Science with Python Specialization
Reviews for Applied Data Science with Python Specialization
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Applied Data Science with 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.
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 a comprehensive understanding of the fundamental business and technical concepts of product management for AI and data science.
Mastering Advanced Statistics for Data Science. Acquire the necessary knowledge and abilities to effectively communicate the choices and interpretations of models.
Become proficient in the programming and analysis of data using Python. Create software that collects, cleans, analyzes, and presents data.
Learn to describe and implement various machine learning algorithms in Python, including classification and regression techniques, and evaluate their performance using appropriate metrics.
Learn to distinguish between different types of machine learning, prepare data for model development, build and evaluate Python-based models for both supervised and unsupervised learning, and choose the right model and metric for a given algorithm.