Repository for coursera specialization Applied Data Science with Python by University of Michigan

MIT Introduction to Deep Learning (6.S191) Instructors: Alexander Amini and Ava Soleimany Course Information Summary Prerequisites Schedule Lectures Labs, Final Projects, Grading, and Prizes Software labs Gather.Town lab + Office Hour sessions Final project Paper Review Project Proposal Presentation Project Proposal Grading Rubric Past Project Proposal Ideas Awards + Categories Important Links and Emails Course Information Summary MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and a panel of industry sponsors. Prerequisites We expect basic knowledge of calculus (e.g., taking derivatives), linear algebra (e.g., matrix multiplication), and probability (e.g., Bayes theorem) -- we'll try to explain everything else along the way! Experience in Python is helpful but not necessary. This class is taught during MIT's IAP term by current MIT PhD researchers. Listeners are welcome! Schedule Monday Jan 18, 2021 Lecture: Introduction to Deep Learning and NNs Lab: Lab 1A Tensorflow and building NNs from scratch Tuesday Jan 19, 2021 Lecture: Deep Sequence Modelling Lab: Lab 1B Music Generation using RNNs Wednesday Jan 20, 2021 Lecture: Deep Computer Vision Lab: Lab 2A Image classification and detection Thursday Jan 21, 2021 Lecture: Deep Generative Modelling Lab: Lab 2B Debiasing facial recognition systems Friday Jan 22, 2021 Lecture: Deep Reinforcement Learning Lab: Lab 3 pixel-to-control planning Monday Jan 25, 2021 Lecture: Limitations and New Frontiers Lab: Lab 3 continued Tuesday Jan 26, 2021 Lecture (part 1): Evidential Deep Learning Lecture (part 2): Bias and Fairness Lab: Work on final assignments Lab competition entries due at 11:59pm ET on Canvas! Lab 1, Lab 2, and Lab 3 Wednesday Jan 27, 2021 Lecture (part 1): Nigel Duffy, Ernst & Young Lecture (part 2): Kate Saenko, Boston University and MIT-IBM Watson AI Lab Lab: Work on final assignments Assignments due: Sign up for Final Project Competition Thursday Jan 28, 2021 Lecture (part 1): Sanja Fidler, U. Toronto, Vector Institute, and NVIDIA Lecture (part 2): Katherine Chou, Google Lab: Work on final assignments Assignments due: 1 page paper review (if applicable) Friday Jan 29, 2021 Lecture: Student project pitch competition Lab: Awards ceremony and prize giveaway Assignments due: Project proposals (if applicable) Lectures Lectures will be held starting at 1:00pm ET from Jan 18 - Jan 29 2021, Monday through Friday, virtually through Zoom. Current MIT students, faculty, postdocs, researchers, staff, etc. will be able to access the lectures during this two week period, synchronously or asynchronously, via the MIT Canvas course webpage (MIT internal only). Lecture recordings will be uploaded to the Canvas as soon as possible; students are not required to attend any lectures synchronously. Please see the Canvas for details on Zoom links. The public edition of the course will only be made available after completion of the MIT course. Labs, Final Projects, Grading, and Prizes Course will be graded during MIT IAP for 6 units under P/D/F grading. Receiving a passing grade requires completion of each software lab project (through honor code, with submission required to enter lab competitions), a final project proposal/presentation or written review of a deep learning paper (submission required), and attendance/lecture viewing (through honor code). Submission of a written report or presentation of a project proposal will ensure a passing grade. MIT students will be eligible for prizes and awards as part of the class competitions. There will be two parts to the competitions: (1) software labs and (2) final projects. More information is provided below. Winners will be announced on the last day of class, with thousands of dollars of prizes being given away! Software labs There are three TensorFlow software lab exercises for the course, designed as iPython notebooks hosted in Google Colab. Software labs can be found on GitHub: https://github.com/aamini/introtodeeplearning. These are self-paced exercises and are designed to help you gain practical experience implementing neural networks in TensorFlow. For registered MIT students, submission of lab materials is not necessary to get credit for the course or to pass the course. At the end of each software lab there will be task-associated materials to submit (along with instructions) for entry into the competitions, open to MIT students and affiliates during the IAP offering. This includes MIT students/affiliates who are taking the class as listeners -- you are eligible! These instructions are provided at the end of each of the labs. Completing these tasks and submitting your materials to Canvas will enter you into a per-lab competition. MIT students and affiliates will be eligible for prizes during the IAP offering; at the end of the course, prize-winners will be awarded with their prizes. All competition submissions are due on January 26 at 11:59pm ET to Canvas. For the software lab competitions, submissions will be judged on the basis of the following criteria: Strength and quality of final results (lab dependent) Soundness of implementation and approach Thoroughness and quality of provided descriptions and figures Gather.Town lab + Office Hour sessions After each day’s lecture, there will be open Office Hours in the class GatherTown, up until 3pm ET. An MIT email is required to log in and join the GatherTown. During these sessions, there will not be a walk through or dictation of the labs; the labs are designed to be self-paced and to be worked on on your own time. The GatherTown sessions will be hosted by course staff and are held so you can: Ask questions on course lectures, labs, logistics, project, or anything else; Work on the labs in the presence of classmates/TAs/instructors; Meet classmates to find groups for the final project; Group work time for the final project; Bring the class community together. Final project To satisfy the final project requirement for this course, students will have two options: (1) write a 1 page paper review (single-spaced) on a recent deep learning paper of your choice or (2) participate and present in the project proposal pitch competition. The 1 page paper review option is straightforward, we propose some papers within this document to help you get started, and you can satisfy a passing grade with this option -- you will not be eligible for the grand prizes. On the other hand, participation in the project proposal pitch competition will equivalently satisfy your course requirements but additionally make you eligible for the grand prizes. See the section below for more details and requirements for each of these options. Paper Review Students may satisfy the final project requirement by reading and reviewing a recent deep learning paper of their choosing. In the written review, students should provide both: 1) a description of the problem, technical approach, and results of the paper; 2) critical analysis and exposition of the limitations of the work and opportunities for future work. Reviews should be submitted on Canvas by Thursday Jan 28, 2021, 11:59:59pm Eastern Time (ET). Just a few paper options to consider... https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf https://papers.nips.cc/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf https://papers.nips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf https://science.sciencemag.org/content/362/6419/1140 https://papers.nips.cc/paper/2018/file/0e64a7b00c83e3d22ce6b3acf2c582b6-Paper.pdf https://arxiv.org/pdf/1906.11829.pdf https://www.nature.com/articles/s42256-020-00237-3 https://pubmed.ncbi.nlm.nih.gov/32084340/ Project Proposal Presentation Keyword: proposal This is a 2 week course so we do not require results or working implementations! However, to win the top prizes, nice, clear results and implementations will demonstrate feasibility of your proposal which is something we look for! Logistics -- please read! You must sign up to present before 11:59:59pm Eastern Time (ET) on Wednesday Jan 27, 2021 Slides must be in a Google Slide before 11:59:59pm Eastern Time (ET) on Thursday Jan 28, 2021 Project groups can be between 1 and 5 people Listeners welcome To be eligible for a prize you must have at least 1 registered MIT student in your group Each participant will only be allowed to be in one group and present one project pitch Synchronous attendance on 1/29/21 is required to make the project pitch! 3 min presentation on your idea (we will be very strict with the time limits) Prizes! (see below) Sign up to Present here: by 11:59pm ET on Wednesday Jan 27 Once you sign up, make your slide in the following Google Slides; submit by midnight on Thursday Jan 28. Please specify the project group # on your slides!!! Things to Consider This doesn’t have to be a new deep learning method. It can just be an interesting application that you apply some existing deep learning method to. What problem are you solving? Are there use cases/applications? Why do you think deep learning methods might be suited to this task? How have people done it before? Is it a new task? If so, what are similar tasks that people have worked on? In what aspects have they succeeded or failed? What is your method of solving this problem? What type of model + architecture would you use? Why? What is the data for this task? Do you need to make a dataset or is there one publicly available? What are the characteristics of the data? Is it sparse, messy, imbalanced? How would you deal with that? Project Proposal Grading Rubric Project proposals will be evaluated by a panel of judges on the basis of the following three criteria: 1) novelty and impact; 2) technical soundness, feasibility, and organization, including quality of any presented results; 3) clarity and presentation. Each judge will award a score from 1 (lowest) to 5 (highest) for each of the criteria; the average score from each judge across these criteria will then be averaged with that of the other judges to provide the final score. The proposals with the highest final scores will be selected for prizes. Here are the guidelines for the criteria: Novelty and impact: encompasses the potential impact of the project idea, its novelty with respect to existing approaches. Why does the proposed work matter? What problem(s) does it solve? Why are these problems important? Technical soundness, feasibility, and organization: encompasses all technical aspects of the proposal. Do the proposed methodology and architecture make sense? Is the architecture the best suited for the proposed problem? Is deep learning the best approach for the problem? How realistic is it to implement the idea? Was there any implementation of the method? If results and data are presented, we will evaluate the strength of the results/data. Clarity and presentation: encompasses the delivery and quality of the presentation itself. Is the talk well organized? Are the slides aesthetically compelling? Is there a clear, well-delivered narrative? Are the problem and proposed method clearly presented? Past Project Proposal Ideas Recipe Generation with RNNs Can we compress videos with CNN + RNN? Music Generation with RNNs Style Transfer Applied to X GAN’s on a new modality Summarizing text/news articles Combining news articles about similar events Code or spec generation Multimodal speech → handwriting Generate handwriting based on keywords (i.e. cursive, slanted, neat) Predicting stock market trends Show language learners articles or videos at their level Transfer of writing style Chemical Synthesis with Recurrent Neural networks Transfer learning to learn something in a domain for which it’s hard or risky to gather data or do training RNNs to model some type of time series data Computer vision to coach sports players Computer vision system for safety brakes or warnings Use IBM Watson API to get the sentiment of your Facebook newsfeed Deep learning webcam to give wifi-access to friends or improve video chat in some way Domain-specific chatbot to help you perform a specific task Detect whether a signature is fraudulent Awards + Categories Final Project Awards: 1x NVIDIA RTX 3080 4x Google Home Max 3x Display Monitors Software Lab Awards: Bose headphones (Lab 1) Display monitor (Lab 2) Bebop drone (Lab 3) Important Links and Emails Course website: http://introtodeeplearning.com Course staff: introtodeeplearning-staff@mit.edu Piazza forum (MIT only): https://piazza.com/mit/spring2021/6s191 Canvas (MIT only): https://canvas.mit.edu/courses/8291 Software lab repository: https://github.com/aamini/introtodeeplearning Lab/office hour sessions (MIT only): https://gather.town/app/56toTnlBrsKCyFgj/MITDeepLearning

Applied Geospatial Data Science with Python, published by Packt

Course - 5; Specialization: Applied Data Science with Python; University Of Michigan

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.

Repo for the first course of the Applied Data Science with Python Specialization taught by University of Michigan hosted by Coursera

Data Science has been ranked as one of the hottest professions and the demand for data practitioners is booming. This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Data Science or Machine Learning. This program consists of 9 courses providing you with latest job-ready skills and techniques covering a wide array of data science topics including: open source tools and libraries, methodologies, Python, databases, SQL, data visualization, data analysis, and machine learning. You will practice hands-on in the IBM Cloud using real data science tools and real-world data sets. It is a myth that to become a data scientist you need a Ph.D. This Professional Certificate is suitable for anyone who has some computer skills and a passion for self-learning. No prior computer science or programming knowledge is necessary. We start small, re-enforce applied learning, and build up to more complex topics. Upon successfully completing these courses you will have done several hands-on assignments and built a portfolio of data science projects to provide you with the confidence to plunge into an exciting profession in Data Science. In addition to earning a Professional Certificate from Coursera, you will also receive a digital Badge from IBM recognizing your proficiency in Data Science.

Applied Data Science with Python Specialization: Course 4 (University of Michigan)

My solutions to the 'Applied Data Science with Python' specialization held by University of Michigan on Coursera.

This project contains all the assignment's solution of university of Michigan.

Repository for the Applied Data Science with Python Specialization from University of Michigan on Coursera

Use powerful industry-standard tools to unlock new, actionable insights from your data

Applied Data Science with Python Specialization: Course 2 (University of Michigan)

This repository contains my work while completing the specialization created by University of Michigan on Coursera. 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.

All the work done by me as part of IBM's CongnitiveClass "Applied Data Science with Python 🐍" Learning Path.

No description available

Applied Data Science with Python Specialization: University of Michigan

Assignments, notes, quizes and course materials. University of MIchigan.

✅This repository contains solutions to the 5 courses under the Specialization - Applied Data Science with Python by University of Michigan on Coursera.

Repo for coursera specialization Big Data by UC San Diego

This repository contains all the projects and assignments about the specialization of Applied Data Science with Python

Applied Machine Learning & Data Science with Python, R and SQL

Maps are everywhere around us: in our cars, on our phones, and driving public health initiatives. Geospatial skills and knowledge are increasingly sought after in industry, and will continue to prove vital to Data Science. You will learn how to create maps and analyze spatial data using Python and SQL, how spatial data are applied in a variety of domains, and have hands-on experiences with real data. Together, we will answer questions such as: (1) what are maps, (2) how can we create maps from data, (3) and how do we quantify and analyze maps. Applied geospatial projects will include: autonomous vehicles, public health, supply chain, and more.

This repository contains the materials to "Applied Data Science with Python", a specialization provided by University of Michigan through Coursera.

A cryptocurrency public ledger is a record-keeping system. It maintains participants' identities anonymously, their respective cryptocurrency balances, and record all the genuine transactions executed between network participants. Cryptocurrency is an encrypted decentralized digital currency. SQL is not the proper database to store the information of transactions because cryptocurrency is encrypted and decentralized. This project is to make the ledger (Similar to Bank Management System). We can make Transactions of tokens (as a user), create an account, delete it, view the public ledger ( details of sender and receiver (only address of the wallet and tokens transacted)). Scaling and security concerns are one the challenge. Future advancements are shifting to the blockchain database. SQL (Structured Query Language) is a standardized programming language that's used to manage relational databases and perform various operations on the data in them. Python is an interpreted high-level general-purpose programming language. Its design philosophy emphasizes code readability with its use of significant indentation. The transaction's details in the bank's records can be queried and verified by the two parties between whom the transaction took place. Public ledgers work the same way as bank records, although with a few differences. Similar to the bank records, the transaction details on a cryptocurrency public ledger can be verified and queried by the two transacting participants. However, no central authority or network participants can know the identity of the participants. Transactions are allowed and recorded only after suitable verification of the sender’s liquidity; otherwise, they are discarded. The objective of this project is to let the students apply the programming knowledge to a real-world situation/problem and exposed the students to how programming skills help in developing good software. This is also to educate the students on future technologies and make them aware of what's happening in the technological world. Students will demonstrate a breadth of knowledge in computer science, as exemplified in the areas of systems, theory and software development. Students will demonstrate the ability to conduct research or applied Computer Science projects, requiring writing and presentation skills that exemplify scholarly style in computer science. Students will learn about the basic principle, how cryptocurrency works and will develop a curiosity to dive deep into readings blogs (computer science research papers).

Content for the class "Applied Data Science with Python"

Applied Data Science with Python Specialization: Course 1 (University of Michigan)

Sri Venkateshwara University (SVU) strives to create professionals who are not only adept in academics but also in application for the benefit of humanity. We foster a culture of learning by doing. We believe in nurturing students who are at the forefront of innovation by offering an environment of research & development to make us Best University in Uttar Pradesh (UP). SVU believes in experiential learning. To facilitate this, we have an ultra-modern infrastructure that motivates students to experiment & excel in their area of interest. The Best University of Moradabad has laboratories & workshops that signify our commitment to core research, thus enabling innovation. SVU is the only institution to have set up labs in collaboration with the industry. This way we can train our students on the latest skills & make them employable. Students sharpen their practical skills under the watch full eyes of trainers & become competent professionals. For the overall development of the students, we organize cultural programs. Students take part in these programs & exhibit their talent to become confident professionals. The annual fest attracts students from all over the country & showcase their talent to make us the Top University in India. We equipped the computing labs with the latest software & hardware to augment the technical skills of the students. SVU’s library is an epitome of knowledge. It has over 3000 books & journals that ensure the students are never short on intellectual input. The team of industry trainers educate them on the key skills so crucial for employment & make us the Best University in Gajraula. The specially created engineering labs assist engineers to refine their technical acumen so much needed for the country. The Chairman Dr. Sudhir Giri believes in removing all the economic & social barriers that can hinder education. Hence, SVU provides many scholarships & grants to meritorious students. Up till now, the college has enabled over 500000 students to attain their academic desires to make us the Best Private University in Uttar Pradesh (UP). The group is running a dozen educational institutions that include medical colleges in India & abroad. Our commitment towards education & healthcare has enabled Dr Sudhir Giri to win the International Glory Man of the year Award 2021. The Best Private University in Moradabad is on the Delhi Moradabad highway, well connected with rail & road. The green surroundings provide peace of mind that enables research based learning. The carefully recruited faculty is the pride of the university. They have years of industrial & academic experience so vital for the students. They transfer key skills & make us the Best Private University in Gajraula. The faculty encourages students to undertake research & sharpen their skills that will enable them to get jobs. Majority of the faculty members are doctorates who educate the students to become competent professionals. The faculty takes part in FDP in order to develop a culture of research. The specialty of SVU is the internship. We have partnered with leading industries for providing internship to the students. We believe that education without applicability is incomplete. Students gain hands on exposure through internship & become job ready. We place most of the students during internship to make us the Top University in India. SVU, the Best University in Uttar Pradesh (UP), adopts a futuristic teaching pedagogy. We strive for experiential learning of our students through role plays, projects & presentation. The students take part in the learning activity & imbibe concepts that enable their placements. The AC seminar & conference halls allow knowledge dispersion for the development of the students. The University is running over 150 undergraduate (UG), postgraduate (PG) courses, (Ph.D.), diploma and certificate courses in various fields of Applied Sciences, Medical Science, Humanities & Social Sciences. We also run courses in Languages, Design, Agriculture, Engineering & Technology, Nursing, Pharmacy, Paramedical, Commerce & Management, Law, Library & information Sciences, Mass Comm. & Journalism to enhance the employability of the youth. SVU has a culture of project based learning. Students do projects in each semester under the guidance of faculty. They complete these projects in earmarked industries to garner hands-on skills. Through these projects, we train students on the hot skills so crucial for employment to make us the Best University in Moradabad. SVU’s Research & Development (R&D) wing encourages students to work on research areas important for the country. We have partnered with leading research institutions to undertake research. The breath-taking infrastructure of the best university in Gajraula motivates researchers to achieve their goals for research. Owing to our dedication, SVU has received grants from GOI for research on areas of national importance. The faculty members provide guidance to the scholars until they achieve their aim. We have set up the incubation center to provide fillip to new ideas that foster entrepreneurship. We want to be an institution that supports the ‘Make in India’ vision of the government. The center supports new ideas that enable the young entrepreneurs to create startups & become successful. Under the strong leadership of Dr. Sudhir Giri, till date we have successfully incubated 150 start-ups. This speaks of our exemplary education & make us the Best Private University in Uttar Pradesh (UP). These startups are not only creating wealth but also providing employment to the needy. The industrialists have lamented that the epicenter for entrepreneurship will be the educational institutions. We need to provide them with the support & infrastructure for this. The annual hackathon attracts individuals who showcase their business acumen to make us the Best Private University in Moradabad. SVU has a dedicated International Research & collaboration Cell (IRCC) that collaborates with universities abroad. Faculty & students who want to pursue studies abroad the IRCC starts admission formalities for them. We have partnered with reputed institutions for providing excellent research collaborations. Those who wish to do P. HD abroad the IRCC help them gain admission & make us the Top University in India. A lot of our faculty members are pursuing their research internationally & contributing to the welfare of humanity. SVU strives to make our students feel comfortable at the campus. Separate hostel for boys & girls with 24 hour security is available at SVU. The cafeteria serves nutritious food to the students. Gym, recreation hall & the sports ground help to relax our students & make us the Best University in Uttar Pradesh (UP). The campus has an in house ATM & convenience store for the benefit of the students. SVU enables placement through exemplary training. We train on communication & interpersonal skills in order to refine the personality of the students. We make them practice mock interviews & group discussion that help to clear placement tests. Ninety percent of the students get placed before their last semester to make us the best university in Moradabad. We have hired industrial trainers in order to provide training on block chain, machine learning, artificial intelligence (AI), and python & data science. These trainers have years of experience that enables them in training the students. The students gain key insights on these technologies & sharpen their acumen to make us the Best University in Gajraula.

Applied Data Science with Python specialization from the University of Michigan

Repository for Coursera specialization Applied Data Science with Python by University of Michigan