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
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
This course equips students with the necessary business leadership skills and technical knowledge to propel the success of ML.
The course highlights the curriculum focused on statistics and machine learning, covering descriptive statistics, data clustering, predictive model development, and analysis capability development.
Gain a comprehensive understanding of NLP, machine learning, deep learning (including TensorFlow, CNNs, RNNs, and LSTMs), and deep learning to facilitate the development of models and data analysis.
Leverage Python programming skills to develop and analyze comprehensive clustering procedures, thereby mastering the fundamental concepts and operations of data clustering, with a particular emphasis on the K-means algorithm.
Gain expertise in leveraging machine learning for marketing transformation, applying unsupervised models like PCA and K-Means, understanding the theory behind k-means clustering and PCA, and determining the optimal number of clusters using the elbow method.