Machine Learning & Data Science from Caltech

This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to adaptively improve their performance with experience accumulated from the observed data. ML has become one of the hottest fields of study today, taken up by undergraduate and graduate students from more than 20 different majors at Caltech. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects.

⏰ Free Online Course
🎬 18 Video lessons
📁 downloadable resources
🏃‍♂️ Self paced
Offered by: Caltech

🔗 Course link

#Machine #Leaning #machinelearning #data_science
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Data Science Projects - Data Analysis & Machine Learning

Learn how to build Data Science Projects with Python. Classification, Time-Series and NLP Projects. Not for beginners.

Rating ⭐️: 4.8 out 5
Students 👨‍🎓 : 5,201
Duration ⏰ : 1hr 44min on-demand video
Created by 👨‍🏫: Onur Baltacı

🔗 Course Link


#Projects #Data_Analysis #Machine_Learning #Data_Science
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Python Data Science Handbook

Python Data Science Handbook: full text in Jupyter Notebooks. This repository contains the entire Python Data Science Handbook, in the form of (free!) Jupyter notebooks.

Creator: Jake Vanderplas
Stars⭐️: 39k
Fork: 17.1K
Repo: https://github.com/jakevdp/PythonDataScienceHandbook

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NOC:Python for Data Science, IIT Madras

🆓 Free Online Course
💻 40 Lecture Videos
⏰ 5 Module
🏃‍♂️ Self paced
Teacher 👨‍🏫 : Prof. Ragunathan Rengasamy

🔗 https://nptel.ac.in/courses/106106212


#Data_Science #IIT
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Applied Data Science
by Daniel Krasner


📄 141 pages

🔗 Book link

#BigData  #DataScience  #MachineLearning  #Statistics
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Visualisation: visual representations of data and information

Modern society is often referred to as 'the information society' - but how can we make sense of all the information we are bombarded with? In this free course, Visualisation: visual representations of data and information, you will learn how to interpret, and in some cases create, visual representations of data and information that help us to see things in a different way.

⏰ Free Online Course
⏰ 9 Module
⏰ Duration : 8 hours
🏃‍♂️ Self paced
Offered by: openlearn

🔗 Course link

#Data #Visualization #data_science
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data-science-ipython-notebooks

Creator: Donne Martin
Stars ⭐️: 22.6k
Forked By: 7k
GithubRepo: https://github.com/donnemartin/data-science-ipython-notebooks
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Data Science vs ML vs Data Analytics vs Math

Visualization created by our team.


#datascience
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Artificial Neural Network for Regression

Rating ⭐️: 4.6 out of 5
Duration ⏰: 1hr 11min on-demand video
Students 👨‍🏫: 49,827
Created by: Hadelin de Ponteves, SuperDataScience Team, Ligency Team

🔗 Course link


#ai #ml #neural_networks #machine_learning #data_science #regression
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Data Science Pipeline

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📊 Data Scientists vs Software Engineers 🖥

🔍 Ever wondered what sets apart Data Scientists from Software Engineers? Let's dive into the key differences!

📈 Data Scientists:

💡 Their role revolves around analyzing complex data to extract valuable insights.
🔍 They focus on data analysis, modeling, and visualization to uncover patterns and trends.
🧠 Skills include statistics, machine learning, and data mining.
🔧 Tools they commonly use are Python, R, SQL, and Jupyter Notebooks.
📋 Responsibilities include data cleaning, preprocessing, and transformation.
🌐 They often possess a strong domain knowledge in a specific industry or business area.
🎯 Their goal is to extract actionable insights from data to drive decision-making.
🔄 Workflow follows CRISP-DM, a standard process for data mining.
💼 Project examples include predictive modeling and recommendation systems.
🚀 Deployment involves integrating models and insights into existing systems or presenting them in reports.
🎯 Performance evaluation focuses on metrics like accuracy, precision, recall, and F1 score.
🤝 Collaboration involves working with cross-functional teams including domain experts and stakeholders.

💻 Software Engineers:

💡 Their role centers around designing, developing, and maintaining software systems.
🔍 They focus on software design, coding, and testing to create functional and reliable solutions.
🧠 Skills include programming languages, algorithms, and databases.
🔧 Tools they commonly use are Java, C++, JavaScript, IDEs, and version control systems.
📋 Responsibilities include developing scalable software applications.
🌐 They possess general knowledge of software engineering principles.
🎯 Their goal is to develop software that meets user needs and operates flawlessly.
🔄 Workflow follows agile or waterfall software development methodologies.
💼 Project examples include web or mobile app development and system integration.
🚀 Deployment involves delivering software for end-users to interact with directly.
🎯 Performance evaluation focuses on code efficiency, reliability, and scalability.
🤝 Collaboration involves working with other software engineers and project managers.

🚀 Whether extracting insights from data or building robust software systems, both Data Scientists and Software Engineers play essential roles in the digital landscape!

🔥 Let's celebrate their unique skills and contributions to the world of technology! 💪💻


#DataScience #SoftwareEngineering #TechComparison #DigitalWorld #DataAnalysis #SoftwareDevelopment
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