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11 Ways to Learn More Data Science

#artificialintelligence

I've been a teacher at many grade levels, and I own a tutoring center that serves kids from age 4 to 18. I've tutored hundreds of students myself over 10 years. I've spent a lot of time trying to teach concepts, to students, peers, friends, direct reports, you name it. I say this because there is one thing that I beg you to listen to, and it's the number one issue I've seen in students at all levels: We just don't know what we don't know. People aren't great at seeing where their own understanding has small gaps. For any topic, we have a few lines of knowledge that we can spout, but we just aren't aware of the edge cases that exist until we see them. We don't have all the knowledge of how every topic intersects with every related one, and many times, those answers are not easy to figure out. Therein lies why experience is valuable. There is so much about even the basic Data Science topics that we haven't yet come across.


100 Best + Free Udemy Courses Online

#artificialintelligence

Are you looking for the Best Free Udemy Courses 2021? This Online Courses list contains the Best Udemy Certifications, and Tutorial for you.


10 Resources for Data Science Self-Study

#artificialintelligence

Learning from a textbook provides a more refined and in-depth knowledge beyond what you get from online courses. This book provides a great introduction to data science and machine learning, with code included: "Python Machine Learning", by Sebastian Raschka.


VLEngagement: A Dataset of Scientific Video Lectures for Evaluating Population-based Engagement

arXiv.org Machine Learning

With the emergence of e-learning and personalised education, the production and distribution of digital educational resources have boomed. Video lectures have now become one of the primary modalities to impart knowledge to masses in the current digital age. The rapid creation of video lecture content challenges the currently established human-centred moderation and quality assurance pipeline, demanding for more efficient, scalable and automatic solutions for managing learning resources. Although a few datasets related to engagement with educational videos exist, there is still an important need for data and research aimed at understanding learner engagement with scientific video lectures. This paper introduces VLEngagement, a novel dataset that consists of content-based and video-specific features extracted from publicly available scientific video lectures and several metrics related to user engagement. We introduce several novel tasks related to predicting and understanding context-agnostic engagement in video lectures, providing preliminary baselines. This is the largest and most diverse publicly available dataset to our knowledge that deals with such tasks. The extraction of Wikipedia topic-based features also allows associating more sophisticated Wikipedia based features to the dataset to improve the performance in these tasks. The dataset, helper tools and example code snippets are available publicly at https://github.com/sahanbull/context-agnostic-engagement


Why Machine Learning Is A Great Career Jump For Physicists

#artificialintelligence

The demand for data science and machine learning jobs is rapidly rising and the gap between this demand and the number of data scientists available is still very wide. Now, people from an engineering background and pure sciences are shifting their careers in the field of ML. Physics is one such background which falls into this category because of the high level of logic and mathematics required in an ML job. Physics research requires dealing with a lot of data, just like ML. Physicists are also proficient in at least one programming language -- most likely Python, as it is popular in the Physics community as well. There are many physicists today who are data scientists.


Applied Data Science with Python Coursera

#artificialintelligence

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.


My Journey into Data Science – Towards Data Science

@machinelearnbot

The main motivation for making this blog is that I will soon be starting the Fast AI Deep Learning course. Let me first start by giving you a quick background of my journey into data science. About a year ago I started writing my master thesis for the study Business Administration. Next to this master I had started a second master in Marketing Communication. At this point I had finished all courses and finally had to start writing these two master theses that I had consistently delayed.


Applied Data Science with Python Coursera

@machinelearnbot

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have basic a python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.