2020년 가을에 UMASS에서 개설된 Advanced NLP 강의입니다. 슬라이드/동영상 모두 제공됩니다.
강의 제목처럼 기본 NLP내용 외에 최신 내용들을 다루기 때문에 NLP에 대한 사전 지식이 필요한 강의입니다.
동영상 강의는 총 26시간 정도 분량입니다.
[video] https://www.youtube.com/playlist?list=PLWnsVgP6CzadmQX6qevbar3_vDBioWHJL [homepage] https://people.cs.umass.edu/~miyyer/cs685/schedule.html [schedule] Week 1: introduction, language models, representation learning Week 2: neural LMs, RNNs, backpropagation Week 3: Attention mechanisms Week 4: Transformers, transfer learning Week 5: BERT and how to use it for downstream tasks Week 6: further improving transfer learning in NLP Week 7: improving text generation Week 8: data augmentation and collection Week 9: model distillation and retrieval-augmented LMs Week 10: Transformer implementation, vision + language Week 11: Exam week! Week 12: Ethics and probe tasks Week 13: Semantic parsing and commonsense reasoning
특히 3Blue1Brown과 Seeing Theory 컨텐츠는 워낙 쉽게 잘 설명을 해놓았고, visulization이 좋아서 입문자에게 좋습니다.
1. Linear Algebra - 3Blue1Brown 채널 : https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab - MIT 강의 : https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/
2. Probability - Harvard 강의 : https://www.youtube.com/playlist?list=PL2SOU6wwxB0uwwH80KTQ6ht66KWxbzTIo - Seeing Theory : https://seeing-theory.brown.edu/index.html#firstPage
3. Calculus - 3Blue1Brown 채널 : https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr
4. Multivarate Calculus - Coursera 강의 : https://www.coursera.org/learn/multivariate-calculus-machine-learning
딥러닝 기본과 NLP를 익히는데 도움이 될 만한 최신 (2020년 2021년) 동영상 강좌 13종입니다.
하나 하나 직접 들어본 분의 추천이니 관심 있으신 분들은 보시면 좋을 듯 합니다.
1. Deep Learning: CS 182 Spring 2021 Includes a great introduction to deep learning starting with the machine learning basics moving into more core topics like optimization. (by Sergey Levine)
2. Deep Learning (with PyTorch)
This is one of the most recent deep learning courses focusing on hot topics like self-supervised learning, transformers, and energy based models. (by Alfredo Canziani)
3. Deep Learning Crash Course 2021 This course is focused on the popular free book available on the d2l.ai website. If you have been studying the book, this set of lectures will come in handy. (by Alex Smola)
4. Natural Language Processing If you are not too familiar with natural language processing (NLP) concepts, this is a great place to start. It provides short and accessible summaries of some of the most important techniques used to solve NLP problems. (by Machine Learning University)
5. CMU Neural Nets for NLP 2021 This course covers topics related to how neural networks are used in natural language processing (NLP). (by Graham Neubig)
6. CS224N: Natural Language Processing with Deep Learning This has been one of the most popular NLP courses for some time now. It focuses on the use of the latest deep learning techniques applied to NLP problems. (by Chris Manning)
7. fast.ai Code-First Intro to Natural Language Processing The NLP courses above focus heavily on the theory. To get the practical side of NLP, this fast.ai course will be a great place to start. (by Rachel Thomas)
8. CMU Multilingual NLP 2020 Graham Neubig also provides another great course that focuses on multilingual NLP. Topics range from data annotation to code switching to low resource automatic speech recognition. (by Graham Neubig)
9. Deep Learning for Computer Vision 2020 This course focuses heavily on the latest techniques in deep learning for computer vision tasks. From attention mechanism to generative models. (by Justin Johnson)
10. Deep Reinforcement Learning: CS 285 Fall 2020 Focuses on the use of deep learning-based architectures for reinforcement learning problems. (by Sergey Levine)
11. Full Stack Deep Learning 2021 While most of the courses above focus heavily on theory, this course specifically focuses on the ecosystem of tools used to develop and deploy deep learning models. (by Josh Tobin, Pieter Abbeel, Sergey Karayev)
12. Practical Deep Learning for Coders This is another course by fast.ai focusing on a coder-first approach to deep learning. (by Jeremy Howard)
13. Applied ML This is an ongoing course teaching how to build a product grade product through ML techniques and tools. (by Made with ML)
The best machine learning course I have worked on till now is the Andrew Ng's machine learning course in Coursera. You will find the link to the working examples of almost all the machine learning method of his course in this article. It's a free machine learning course. #machinelearning #datascience #python
 This is probably one of the biggest worries of those starting in the area of data science, learning/refreshing math
Image by DataSource.ai
Let’s be honest, most people didn’t do very well in math in school, maybe not even in college, and this is very scary and creates a barrier for those who want to explore this discipline called data science.
A few days ago I published a post inTowards Data Scienceand right here on our blog called“Study Plan for Learning Data Science Over the Next 12 Months”, where I gave some quarterly recommendations and made an emphasis on studying mathematics and statistics for this first quarter, and from which I received many questions about exactly which materials I recommended. Well, this post answers those questions. But before that, I want to give you a context.
Leaving aside the factors or reasons that have led most people to hate math, it is a reality that we need it in data science. For me, one of the biggest shortcomings I found in mathematics was its lack of applicability in the real world, I didn’t see a reason for intermediate and advanced mathematics, such as multivariate calculus. I confess that in school and college I didn’t like them for that reason, but I always did well and got good scores and averages above the majority (especially in statistics). But I still didn’t see how I could use a derivative or a matrix in the real world. I finally ended up as a software engineer and once I entered the world of data science I was able to make the connection between mathematics, statistics, and the real world.
On the other hand, it is important to clarify that we do not need a master’s degree in pure mathematics to do data science projects. As I mentioned in previous posts there is a big debate in the community about how much math we need to do a good job as data scientists.
We could say that data science is divided into two major fields of work: research and production
Byresearch, we mean the part of research and development, which normally takes place within a large company (usually a tech company), or which has focused on cutting-edge technological issues (such as medical research). Or it is also an area that is developed within universities. This sector has very limited job offers.
The great advantage is the deep knowledge of algorithms and their implementations, as well as being a person capable of creating variations of existing algorithms, to improve them. Or even create new machine learning algorithms.
The disadvantage is the unpractical nature of their work. It is a very theoretical work, in which often the only objective is to publish papers and is far from the business use cases in general. For reference on this,I recently read this post on Reddit, I recommend you read it.
Byproduction, we refer to the practical side of this discipline, where you’ll use generally and in your day to day job libraries such as scikit-learn, Tensorflow, Keras, Pytorch, and others. These libraries operate like a black box, where you enter data, you get an output, but you don’t know in detail what happened in the process. This also has its advantages and disadvantages, but it certainly makes life much easier when putting useful models into production. What I don’t recommend is to use them blindly, where you don’t have the minimum bases of mathematics to understand a little of their fundamentals and that is the objective of this post, to guide you and recommend you some valuable resources to have the necessary bases and not to operate blindly those libraries.
So if you decide to focus on Research and Development, you are going to need mathematics and statistics in depth (very in-depth). If you are going to go for the practical part, the libraries will help you deal with most of it, under the hood. It should be noted that most job offers are in the practical side.
Well, after the previous remarks, it is time to define which are the specific topics needed to have an initial basis in mathematics for data science.
Linear Algebra: This subject is important to have the fundamentals of working with data in vector and matrix form, to acquire skills to solve systems of linear algebraic equations, and to find the basic matrix decompositions and the general understanding of their applicability.
Calculus:Here it is important to study functional maps, limits (in case of sequences, functions of one and several variables), differentiation (from a single variable to multiple cases), integration, thus sequentially building a foundation for basic optimization. It is also important here to study gradient descent.
Probability theory: Here you should learn about random variables, i.e. a variable whose values are determined by a random experiment. Random variables are used as a model for the data generation processes we want to study. The properties of the data are deeply linked to the corresponding properties of the random variables, such as expected value, variance, and correlations.
Note: these subjects are much deeper than what I just mentioned, this is simply a guide of the subjects and resources recommended to approach mathematics in the field of data science.
Now that we have a better idea of the path we should take, let’s examine the recommended resources to address this topic. We will divide them into basic, intermediate, and advanced. In the advanced ones, we’ll have resources focused on deep learning
Basics: in this first section of resources we’ll recommend the mathematical basics. Mathematical thinking, algebra, and how to implement math with python.
1- Introduction to mathematical thinking
Price: Free
Image by Coursera
Description: Learn how to think the way mathematicians do — a powerful cognitive process developed over thousands of years.
Mathematical thinking is not the same as doing mathematics — at least not as mathematics is typically presented in our school system. School math typically focuses on learning procedures to solve highly stereotyped problems. Professional mathematicians think a certain way to solve real problems, problems that can arise from the everyday world, or from science, or from within mathematics itself. The key to success in school math is to learn to think inside-the-box. In contrast, a key feature of mathematical thinking is thinking outside-the-box — a valuable ability in today’s world. This course helps to develop that crucial way of thinking.
2- Mathematical Foundation for AI and Machine Learning
Price: $46.99 usd
Image by Packt
Description: Artificial Intelligence has gained importance in the last decade with a lot depending on the development and integration of AI in our daily lives. The progress that AI has already made is astounding with innovations like self-driving cars, medical diagnosis and even beating humans at strategy games like Go and Chess. The future for AI is extremely promising and it isn’t far from when we have our own robotic companions. This has pushed a lot of developers to start writing codes and start developing for AI and ML programs. However, learning to write algorithms for AI and ML isn’t easy and requires extensive programming and mathematical knowledge. Mathematics plays an important role as it builds the foundation for programming for these two streams. And in this course, we’ve covered exactly that. We designed a complete course to help you master the mathematical foundation required for writing programs and algorithms for AI and ML.
Description: InMath for Programmersyou’ll explore important mathematical concepts through hands-on coding. Filled with graphics and more than 300 exercises and mini-projects, this book unlocks the door to interesting–and lucrative!–careers in some of today’s hottest fields. As you tackle the basics of linear algebra, calculus, and machine learning, you’ll master the key Python libraries used to turn them into real-world software applications.
Description: You can learn a lot of math with a bit of coding!
Many people don’t know that Python is a really powerful tool for learning math. Sure, you can use Python as a simple calculator, but did you know that Python can help you learn more advanced topics in algebra, calculus, and matrix analysis? That’s exactly what you’ll learn in this course.
This course is a perfect supplement to your school/university math course, or for your post-school return to mathematics.
Let me guess what you are thinking:
“But I don’t know Python!”That’s okay! This course is aimed at complete beginners; I take you through every step of the code. You don’t need to know anything about Python, although it’s useful if you already have some programming experience.
“But I’m not good at math!”You will be amazed at how much better you can learn math by using Python as a tool to help with your courses or your independent study. And that’s exactly the point of this course: Python programming as a tool to learn mathematics. This course is designed to be the perfect addition to any other math course or textbook that you are going through.
7- Introduction to Linear Models and Matrix Algebra
Price: Free
Image by Edx
Description: Matrix Algebra underlies many of the current tools for experimental design and the analysis of high-dimensional data. In this introductory online course in data analysis, we will use matrix algebra to represent the linear models that commonly used to model differences between experimental units. We perform statistical inference on these differences. Throughout the course we will use the R programming language to perform matrix operations.
Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. You will need to know some basic stats for this course. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.
Description: Python, one of the world’s most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way. These core capabilities help programmers pave the way for building exciting applications in various domains, such as machine learning and data science, using knowledge in the computational mathematics domain.
The book teaches you how to solve problems faced in a wide variety of mathematical fields, including calculus, probability, statistics and data science, graph theory, optimization, and geometry. You’ll start by developing core skills and learning about packages covered in Python’s scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you’ll get to grips with more advanced topics of calculus, probability, and networks (graph theory). After you gain a solid understanding of these topics, you’ll discover Python’s applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code.
By the end of this book, you’ll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.
Description: Behind numerous standard models and constructions in Data Science there is mathematics that makes things work. It is important to understand it to be successful in Data Science. In this specialisation we will cover wide range of mathematical tools and see how they arise in Data Science. We will cover such crucial fields as Discrete Mathematics, Calculus, Linear Algebra and Probability. To make your experience more practical we accompany mathematics with examples and problems arising in Data Science and show how to solve them in Python.
Each course of the specialisation ends with a project that gives an opportunity to see how the material of the course is used in Data Science. Each project is directed at solving practical problem in Data Science. In particular, in your projects you will analyse social graphs, predict estate prices and uncover hidden relations in the data.
Description: Discrete mathematics is a field of math that deals with studying finite and distinct elements. The theories and principles of discrete math are widely used in solving complexities and building algorithms in computer science and computing data in data science. It helps you to understand algorithms, binary, and general mathematics that is commonly used in data-driven tasks.
Learn Discrete Mathematics is a comprehensive introduction for those who are new to the mathematics of countable objects. This book will help you get up-to-speed with implementing discrete math principles to take your programming skills to another level. You’ll learn the discrete math language and methods crucial to studying and describing objects and functions in branches of computer science and machine learning. Complete with real-world examples, the book covers the internal workings of memory and CPUs, analyzes data for useful patterns, and shows you how to solve problems in network routing, encryption, and data science.
By the end of this book, you’ll have a deeper understanding of discrete mathematics and its applications in computer science, and get ready to work on real-world algorithm development and machine learning.
14- Math for Data Science and Machine Learning: University Level
Price: $12.99
Image by Udemy
Description: In this course we will learn math for data science and machine learning. We will also discuss the importance of Math for data science and machine learning in practical word. Moreover, Math for data science and machine learning course is bundle of two courses of linear algebra and probability and statistics. So, students will learn complete contents of probability and statistics and linear algebra. It is not like that you will not complete all the contents in this 7 hours videos course. This is a beautiful course and I have designed this course according to the need of the students.
Linear algebra and probability and statistics is usually offered for the students of data science, machine learning, python and IT students. So, that’s why I have prepared this dual course for different sciences.
I have taught this course multiple times on my universities classes. It is offered usually in two different modes like, it is offered as linear algebra for 100 marks paper and probability and statistics as another 100 marks paper for two different or in a same semesters. I usually focus on the method and examples while teaching this course. Examples clear the concepts of the students in a variety of way like, they can understand the main idea that instructor want to deliver if they feel typical the method of the subject or topics. So, focusing on example makes the course easy and understandable for the students.
Description: Data science courses contain math — no avoiding that! This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus. Data Science Math Skills introduces the core math that data science is built upon, with no extra complexity, introducing unfamiliar ideas and math symbols one-at-a-time.
Learners who complete this course will master the vocabulary, notation, concepts, and algebra rules that all data scientists must know before moving on to more advanced material.
Advanced: in this last section we will focus on the statistical part (probability theory) and the application of mathematics to deep learning algorithms.
Description: Inferential statistics allows us to draw conclusions from data that might not be immediately obvious. This course focuses on enhancing your ability to develop hypotheses and use common tests such as t-tests, ANOVA tests, and regression to validate your claims.
18- Statistical Methods and Applied Mathematics in Data Science
Price: $124.99
Image by Packt
Description: Machine learning and data analysis are the center of attraction for many engineers and scientists. The reason is quite obvious: its vast application in numerous fields and booming career options. And Python is one of the leading open source platforms for data science and numerical computing. IPython, and its associated Jupyter Notebook, provide Python with efficient interfaces to for data analysis and interactive visualization, and they constitute an ideal gateway to the platform. If you are among those seeking to enhance their capabilities in machine learning, then this course is the right choice.
Statistical Methods and Applied Mathematics in Data Science provides many easy-to-follow, ready-to-use, and focused recipes for data analysis and scientific computing. This course tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics. You will apply state-of-the-art methods to various real-world examples, illustrating topics in applied mathematics, scientific modeling, and machine learning. In short, you will be well versed with the standard methods in data science and mathematical modeling.
19- Exploring Math for Programmers and Data Scientists
Price: Free
Image by Manning
Description:Exploring Math for Programmers and Data Scientistsshowcases chapters from three Manning books, chosen by author and master-of-math Paul Orland. You’ll start with a look at the nearest neighbor search problem, common with multidimensional data, and walk through a real-world solution for tackling it. Next, you’ll delve into a set of methods and techniques integral to Principal Component Analysis (PCA), an underlying technique in Latent Semantic Analysis (LSA) for document retrieval. In the last chapter, you’ll work with digital audio data, using mathematical functions in different and interesting ways. Begin sharpening your competitive edge with the fun and fascinating math in this (free!) practical guide!
Description: Most programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models.
You’ll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you’ll explore CNN, recurrent neural network (RNN), and GAN models and their application.
By the end of this book, you’ll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL.
Description:Math and Architectures of Deep Learningsets out the foundations of DL in a way that’s both useful and accessible to working practitioners. Each chapter explores a new fundamental DL concept or architectural pattern, explaining the underpinning mathematics and demonstrating how they work in practice with well-annotated Python code. You’ll start with a primer of basic algebra, calculus, and statistics, working your way up to state-of-the-art DL paradigms taken from the latest research. By the time you’re done, you’ll have a combined theoretical insight and practical skills to identify and implement DL architecture for almost any real-world challenge.
When we have limited time for study, we should select those that we feel best and those that fit our style. For example, you might prefer videos about books, so go ahead and choose what suits you best. This material is sufficient whether you want to take a brief look at the mathematics, or if you want to go deeper into it. I hope you find it useful.
If you have other recommendations for courses, books or videos, please leave them in the comments so that we can all create links of interest.
Note: we are building a private community in Slack of data scientist, if you want to join us you can register here:https://www.datasource.ai/en#slack
I hope you enjoyed this reading! you can follow me ontwitterorlinkedin
Deep Learning - Python & R https://www.udemy.com/course/deep-learning-with-keras-and-tensorflow-in-python-and-r/?ranMID=39197&ranEAID=WIRXHBzeZwo&ranSiteID=WIRXHBzeZwo-NFnZtLOHl0lU6RYjRtpMdA&LSNPUBID=WIRXHBzeZwo&utm_source=aff-campaign&utm_medium=udemyads&couponCode=DECOUP20
Data viz in Excel https://www.udemy.com/course/data-visualization-in-excel-for-business-professionals/?ranMID=39197&ranEAID=WIRXHBzeZwo&ranSiteID=WIRXHBzeZwo-NFnZtLOHl0lU6RYjRtpMdA&LSNPUBID=WIRXHBzeZwo&utm_source=aff-campaign&utm_medium=udemyads&couponCode=DECOUP20
R https://www.udemy.com/course/data-manipulation-with-dplyr-in-r/?ranMID=39197&ranEAID=WIRXHBzeZwo&ranSiteID=WIRXHBzeZwo-NFnZtLOHl0lU6RYjRtpMdA&LSNPUBID=WIRXHBzeZwo&utm_source=aff-campaign&utm_medium=udemyads&couponCode=MATURITY
mySQL https://www.udemy.com/course/the-complete-sql-course-2020-become-a-mysql-master/?couponCode=SQLCOURSE29
Data science is the discipline of making data useful.
YouTube doesn’t need much of an introduction, and we’re sure you all know how much of a popular platform it is among people of all age groups. YouTube is not just a vast repository of entertainment, but it is also an equally important source of education. Undeniably the best thing about learning from YouTube is the lack of any fee or charges for getting access to that vast repertoire of educational videos. Data Science and many other domains like it can seem daunting at first, but with YouTube, you can get easy access to a plethora of educational and instructional videos on a wealth of topics, including Data Science. The goal behind this write-up is to introduce you to a range of informative channels on YouTube that demystify the complex concepts of Data Science so that you can learn at your own pace. But before digging into this, check out Some Interesting Articles for Data Science —
Data Science Books You Must Read in 2020
Have a look, why you should read them?
towardsdatascience.com
Best Data Science Blogs to Follow in 2020
Most trusted and reputed sources to update yourself with the latest happenings in the Data Science world.
towardsdatascience.com
10 Popular Data Science Resources on Github
Some of the top GitHub repositories that will teach you all about Data Science.
towardsdatascience.com
Top Data Science YouTube Channels
The domain of Data Science brings with itself a variety of scientific tools, processes, algorithms, and knowledge extraction systems from structured and unstructured data alike, for identifying meaningful patterns in it.
Everybody loves YouTube, right? But wouldn’t it be fun to gain access to literally thousands of informative videos on Data Science, covering not just the basics but also the latest happenings in the domain? In the age of Massive Online Open Courses(MOOCs), YouTube serves as a powerful platform to find answers to your questions, which otherwise would not have made it to the course video. All that while helping you save some money. We’re guessing by now you’re pretty hyped to know about the channels, fair enough, let’s not waste any more time and move on to the list of YouTube channels focused on Data Science.
3Blue1Brown is a fairly enjoyable channel created by Grant Sandersonin March 2015 that primarily focuses on teaching math in an entertaining way. You might be wondering why we included this channel in our list. Well, there are two reasons for that. First, he addresses a multitude of topics on his channel that are related to the Data Science domain. Some of these topics include Neural Networks, Linear Algebra, Fourier Transformations, Calculus, and many others. Second, the amazing visualizations you see in his videos have been created by an animation engine in Python called manim, which Grant created by himself. The channel 3Blue1Brown has over 100 uploads with a total of 165 million views. Throughout his channel, you’ll realize how much of an important role the visualizations play in Grant’s videos and how beautifully the manim library can be utilized to create some slick visuals.
Created by Quincy Larson in December 2014, freeCodeCamp is a non-profit organization with a mission to empower people to code and help others. freeCodeCamp is more of a course-oriented channel run by a highly knowledgeable group of people with a strong background in programming. Their YouTube channel offers informative videos on a wide range of topics, such as Data Structures, JavaScript, Python, Data Science, Machine Learning, Node.js, and has upwards of 127 million total views so far. freeCodeCamp’s channel has over 1100 videos, and a decent chunk of them are full-on courses with over an hour of content and code sessions at the very least. You can also visit their website to get your hands on more than 6000 tutorials on programming and ethical hacking.
Created by Harrison Kinsley in December 2012, Sentdex covers several programming topics and technologies such as Machine Learning, Natural Language Processing, Data Analysis and Visualization, and some Robotics projects with Raspberry Pi projects. Harrison’s clear and explanatory style of simplifying the various topics puts Sentdex among the best Data Science channels on YouTube. The channel has over 1200 videos and more than 87 million views. Harrison’s love for Python can be seen on his channel, as he has covered a multitude of programming topics in Python. He also runs a website called Python Programming Tutorials, where you can find a healthy collection of Python projects in a very detailed manner and see how things work. If you’re interested in a more advanced topic, say, Neural Networks, Harrison has written a book on it called “Neural Networks from Scratch in Python”. Some Interesting Articles related to Python —
Best Python IDEs and Code Editors You Must Use in 2020
Top Python IDEs and Code Editors with noteworthy features
towardsdatascience.com
10 Cool Python Project Ideas for Python Developers
A list of interesting ideas and projects you can build using Python
Corey Schafer’s YouTube channel revolves mainly around programming tools that are vital for modern programmers and researchers, including the fundamental concepts of programming. The videos on Corey’s channel have garnered over 47 million views and counting. The channel has covered a range of topics, such as the basics of programming, Linux tutorials, SQL tutorials, Django, and much more. For individuals interested in Data Science, Corey has got you covered with video playlists on topics like Pandas, Matplotlib, and a series of videos on getting started Python. Whether you’re a veteran in programming in a professional environment or a beginner learning about the technology, Corey has offered educational content keeping everyone’s skill level in mind.
Started by Tim Ruscica in April 2014, the videos on Tim’s channel are more focused on Python programming in general with some game development usingPyGame, some tutorials on Machine Learning, and JavaScript paired with a few frameworks. With over 32 million total views, Tim’s channel has some cool projects on a couple of topics, such as a Flappy Bird game, a Face Recognition tool, a Slack bot, and more. Tim has also done a handful of long coding live streams ranging anywhere from a manageable period of 2 hours to more grueling 12-hour sessions. You will also find some crucial tips and advice on his channel for new developers as well as some programming project ideas, along with some beginner-friendly tutorials on Golang and Flutter. If you like the projects on Tim’s channel, you can find the codeon his GitHub repo to follow through with some of his videos.
Giles McMullen created this channel in August 2008 to inspire the world about Python and to show his love for the programming language, hence the name Python Programmer. Throughout the years, Giles covered loads of tutorials on his channel for various topics ranging from the more fundamental ones for beginners like the basics of Python programming tomore advanced topics, like Data Science and Machine Learning. You can find a few free courses on Giles’ channel for Data Science and Machine Learning, which can essentially give you a strong idea about the core concepts in these subjects. Apart from covering educational topics, Giles also has videos about popular Python libraries, such as Pandas, NumPy, Scikit-learn, and some handy tips to programmers of all skills on the hows and whats of Python or just programming in general.
What started as mere explanations about the complex statistical techniques to colleagues at work soon turned into Josh’s passion and later into StatQuest. StatQuest takes away the challenges people usually face in understanding those complicated terms and methods that modern statistics and Machine Learning is filled with. Created by Josh Starmer in May 2011, StatQuest has more than 180 videos with a combined total of 17 million views. In the channel, you will find a handful of playlists explaining the various fundamental concepts like Logistic Regression, Linear Regression, Linear Models. You can also visit StatQuest’s website to find study guides filled with detailed information for a better understanding of sub-topics like AdaBoost, Classification Trees, and a few others.
Deep Reinforcement Learning Course (CS285) by Sergey Levine, Assistant Professor at University of California, Berkeley is now has fall 2020 lectures online:
If you are ready to take your career in machine learning to the next level, then these top 10 Machine Learning Courses covering both practical and theoretical work will help you excel.
1) Practical Deep Learning for Coders FAST.AI  Price: Free Taught by: One of the most famous and practical courses on the internet, taught by Jeremy Howard, Research Scientist at the University of San Francisco, the chair of WAMRI, and is Chief Scientist at the platform.ai. He was the President and Chief Scientist of the data science platform Kaggle, where he was the top-ranked participant in international machine learning competitions 2 years running. Course Outcomes: This course is a hands-on introduction to deep learning, where you will dive straight into deep learning via making a state of the art classifier. You will learn a lot of practical aspects of deep learning without knowing the underlying theory.
2) Code-First Introduction to Natural Language Processing by Fast.ai  Price: Free Taught by: Rachel Thomas is an American computer scientist and founding Director of the Center for Applied Data Ethics at the University of San Francisco. Together with Jeremy Howard, she is co-founder of fast.ai. Course Outcomes: This course is a hands-on introduction to NLP, where you will code a practical NLP application first as the name suggests, then slowly start digging inside the underlying theory in it. Applications covered include topic modeling, classification (identifying whether the sentiment of a review is positive or negative), language modeling, and translation. The course teaches a blend of traditional NLP topics (including regex, SVD, naïve Bayes, tokenization) and recent neural network approaches (including RNNs, seq2seq, attention, and the transformer architecture), as well as addressing urgent ethical issues, such as bias and disinformation.
3) Python for Data Science and Machine Learning Bootcamp  Price: $129 (on sale $10-$20) Taught by: Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science and programming. He has publications and patents in various fields such as microfluidics, materials science, and data science technologies. Rating: 4.6* Course Outcomes: This course is a very practical introduction to Machine Learning and data science. It does not assume any previous knowledge, starts from teaching basic Python to Numpy Pandas, then goes to teach Machine Learning via sci-kit learn in Python, then jumps to NLP and Tensorflow, and some big-data via spark. This is definitely one of the best courses out there, as Jose is a really good instructor.
4) DeepLearning.AI TensorFlow Developer Professional Certificate  Price: $49/month Taught by: Laurence Moroney is a Developer Advocate at Google working on Artificial Intelligence with TensorFlow. He is also the author of many books. Rating: 4.7* Course Outcomes: In this hands-on, four-course Professional Certificate program, you’ll learn the necessary tools to build scalable AI-powered applications with TensorFlow. Lawrence will start teaching you the basics of TensorFlow, slowly progressing towards the state of the art applications using Tensorflow.
5) Datacamp Data Science Path  Price: $25/month or $300/year Taught by: Multiple industry professionals Course Outcomes: With no prior coding experience, you will be taught coding from scratch, then moving to advanced libraries and frameworks. Each lesson is accompanied by some exercises or tasks. You will also have access to projects at data camp, which will improve your coding experience as well as your resume.
Theoretical Courses with Less Practical work
1) Machine Learning by Stanford University  Price: $80 Taught by: Andrew Ng is CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist, Baidu, and founding lead of Google Brain. Rating:4.9 Course Outcomes: You will learn all the underlying theory behind famous machine learning algorithms, from Supervised Learning to Unsupervised Learning. You will also get a chance to code them from scratch in MATLAB/Octave.
2) Deep Learning Specialization  Price: $49/month Instructor: Andrew Ng Ratings: 4.8* Course Outcomes: This 5 parts specialization will teach you the underlying theory behind of Deep Learning from Single Layer Network to Multi-Layer Dense Networks, from the basics of CNN to performing object detection with YOLO along with underlying theory, from basics of RNN to Sentiment analysis. This course will also give you an introduction to the basics of Deep Learning frameworks such as Tensorflow or Keras.
3) CS231n by Andrej Karpathy  Price: Free Taught by: Andrej Karpathy, the Sr. Director of AI at Tesla, leads the team responsible for all neural networks on the Autopilot. Previously, he was a Research Scientist at OpenAI working on Deep Learning in Computer Vision, Generative Modeling, and Reinforcement Learning. He received his Ph.D. from Stanford University. Course Outcomes: This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. Students will learn to implement, train, and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. The focus is on teaching how to set up the problem of image recognition, the learning algorithms (e.g., backpropagation), practical engineering tricks for training, and fine-tuning the networks.
4) Stat 451: Introduction to Machine Learning  Price: Free Taught by: Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. Course Outcomes: You will learn all the underlying theory of famous Machine Learning Algorithms from Neural Networks to supervised and Unsupervised Learning. This course is originally taught at the University of Wisconsin-Madison by Dr. Sebastian.
5) MIT Introduction to Deep Learning | 6.S191  Price: Free Taught by: Ava Soleimany is a Ph.D. student in the Harvard Biophysics program and at MIT, where she works with Sangeeta Bhatia at the Koch Institute for Integrative Cancer Research and am supported by the NSF Graduate Research Fellowship. Alexander Amini is a Ph.D. student at MIT, in the Computer Science and Artificial Intelligence Laboratory (CSAIL), with Prof. Daniela Rus. He is an NSF Fellow and completed my Bachelor of Science and Master of Science in Electrical Engineering and Computer Science at MIT, with a minor in Mathematics. Course Outcomes: 6.S191 is MIT’s official 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. Students will also get practical experience in building neural networks in TensorFlow.
 데이터 과학에 관한 작업을 시작했을 뿐이지만 데이터 분석과 빅데이터 분석 전문가(ADP) 라이선스 준비를 위해 전문 서적 외에도 관련 사이트나 블로그로부터 최신 데이터 정보와 지식을 얻어야 합니다. 인터넷에서 30종 인기 블로그나 온라인 사이트를 정리해 공유하였습니다. 참고가 되었으면 좋겠습니다. 실제로 빅데이터 분석 을 할 때 좋은 정보와 데이터가 없는 경우가 많습니다. 오픈 공공데이터 포럼을 정리하고 소개하였습니다. 2 가지 함께 보시면 더 좋을 것 같습니다.
Udacity는 Python、SQL 및 통계 데이터를 통해 인사이트를 통찰하고 중요한 발견을 전하고 데이터 구동형(driven) 솔루션을 구축하기 위해서 힘을 애쓰고 있다는 슬로건이 있습니다. 온라인 비디오 뿐만 아니라, 독자적인 스터디 관리 시스템, 빌트인 (built-in) 프로그래밍 인터페이스 및 포럼도 가지고 있습니다.
Data School는 워싱턴DC에서 데이터 사이언스 교육을 하고 있는 Kevin Markham씨의 채널입니다. 이 채널에서 오픈 소스 툴에 관련 사용방법, Python 및 R 등 다양한 데이터 사이언스에 기반으로 주제에 대해서 소개하는 것입니다. 무료 보기 가능하고, 현재 8만명 이상의 팔로워가 가지고 있습니다.
EdwinChen 씨는 매사추세츠공과대학(MIT)에서 수학과 언어학을 전공했으며, MRS음성인식(speech recognition)，Clarium의 양적 거래, Twitter 광고, Dropbox 분석, Google 데이터 사이언스 등의 일을 하고 있었습니다. AI 인공지능과 데이터에 관한 포스트를 볼 수 있습니다.
Alexis Perrier씨는 기업의 머신러닝을 지원한 적이 있는 데이터 사이언스 강사입니다. 자신의 블로그에서 선형회귀 등에 대한 상세한 연구를 공유했습니다. 러닝을 지원한 적이 있는 데이터 사이언스 강사입니다. 자신의 블로그에서 선형회귀 등에 대한 상세한 연구를 공유했습니다.
Algobeans는 케임브리지대학의 Annalyn 및 스탠퍼드대학의 Kenneth에 의해 설립되었습니다. 데이터 사이언스의 애호가로서 누구나 쉽게 데이터 사이언스를 공부할 수 있도록 사이트를 작성했습니다. 페이스북에서 FineReport Reporting Sofeware채널을 구독하고 더 많은 데이터 시각화 정보를 받으세요!
참고로 아나콘다에서 텐서플로 1.X 또는 2.X 설치하고 가상환경 실행 환경법 부터 "Hello, World" 부터 기본 선형 회귀 분석과 로지스틱 분석, K-Means 및 Nearest Neighbor, Random Forest, GBDT 알고리즘과 Word2Vec 모델을 작성해 볼 수 있습니다.
We decided to make most of our Deep Learning Teaching Materials freely available online: https://lme.tf.fau.de/teaching/free-deep-learning-resources/ I hope this is useful for some of you. Most of it is CC 4.0 BY, so it might also be useful to everybody who teaches her- or himself.
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
Machine Learning From Scratch
About
Python implementations of some of the fundamental Machine Learning models and algorithms from scratch.
The purpose of this project is not to produce as optimized and computationally efficient algorithms as possible but rather to present the inner workings of them in a transparent and accessible way.
Figure: Classification of the digit dataset by a neural network which has been evolutionary evolved.
Genetic Algorithm
$ python mlfromscratch/examples/genetic_algorithm.py +--------+ | GA | +--------+ Description: Implementation of a Genetic Algorithm which aims to produce the user specified target string. This implementation calculates each candidate's fitness based on the alphabetical distance between the candidate and the target. A candidate is selected as a parent with probabilities proportional to the candidate's fitness. Reproduction is implemented as a single-point crossover between pairs of parents. Mutation is done by randomly assigning new characters with uniform probability. Parameters ---------- Target String: 'Genetic Algorithm' Population Size: 100 Mutation Rate: 0.05 [0 Closest Candidate: 'CJqlJguPlqzvpoJmb', Fitness: 0.00] [1 Closest Candidate: 'MCxZxdr nlfiwwGEk', Fitness: 0.01] [2 Closest Candidate: 'MCxZxdm nlfiwwGcx', Fitness: 0.01] [3 Closest Candidate: 'SmdsAklMHn kBIwKn', Fitness: 0.01] [4 Closest Candidate: ' lotneaJOasWfu Z', Fitness: 0.01] ... [292 Closest Candidate: 'GeneticaAlgorithm', Fitness: 1.00] [293 Closest Candidate: 'GeneticaAlgorithm', Fitness: 1.00] [294 Answer: 'Genetic Algorithm']
This course uses pytorch instead of tensorflow.[ [](https://t.co/lahLZspADM?amp=1)https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z](https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z)
