Which of the following statements are true? Check all that apply. This statement is wrong. There we have to break the symmetry. That's why we initialize all the weights randomly. But no need to do it in Gradient descent. I have provided solutions to all the problems for all weeks. Thanks for the solutions but can you help with some details on how you came up with these answers. Videos are coursera are not that straight forward or some links that we can go through. Appreciate your help.
Honestly, I think you can get most of the answers through Coursera theory lectures only. If you have doubt for any particular question. You can ask here as well. I have also provided reasons for the selected answers for some of the quizzes.
Recent Posts. Click here to see solutions for all Machine Learning Coursera Assignments.
Coursera: Machine Learning (Week 4) Quiz - Neural Networks: Representation| Andrew NG
Feel free to ask doubts in the comment section. I will try my best to answer it.
If you find this helpful by any mean like, comment and share the post. This is the simplest way to encourage me to keep doing such work. Share This Facebook Twitter. Nival Kolambage 9 November at Unknown 16 March at Densil 27 March at Subscribe to: Post Comments Atom.
Total Pageviews. YouTube Channel. Popular Posts. Created By ThemeXpose.This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment Jupyter Notebook for Pythonrather than in Excel and the Visual Basic Editor.
These concepts are taught within the context of one or more accounting data domains e. The first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks.
We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software. The second half of the course focuses on assembling data for machine learning purposes.
We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data. Execute Python code for wrangling data from different structures into a Pandas dataframe structure. Run and interpret fundamental data analytic tasks in Python including descriptive statistics, data visualizations, and regression.
Use relational databases and know how to manipulate such databases directly through the command line, and indirectly through a Python script. The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni.
Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs. In this module, you will become familiar with the course, your instructor and your classmates, and our learning environment.
This orientation module will also help you obtain the technical skills required to navigate and be successful in this course. This module serves as the introduction to the course content and the course Jupyter server, where you will run your analytics scripts. First, you will read about specific examples of how analytics is being employed by Accounting firms. Next, you will learn about the capabilities of the course Jupyter server, and how to create, edit, and run notebooks on the course server.
After this, you will learn how to write Markdown formatted documents, which is an easy way to quickly write formatted text, including descriptive text inside a course notebook. This module focuses on the basic features in the Python programming language that underlie most data analytics programs or scripts. First, you will read about why accounting students should learn to write computer programs.
In the first lesson, you will also learn the basic concepts of the Python programming language, including how to create variables, basic data types and mathematical operators, and how to document your programs with comments.
Next, you will learn about Boolean and logical operators in Python and how they can be used to control the flow of a Python program by using conditional statements. Finally, you will learn about functions and how they can simplify developing and maintaining programs. You will also learn how to create and call functions in Python.
In this module you will learn about working with fundamental data structures in Python: strings, tuples, lists, and dictionaries. You will also learn about how to write loops for performing repetitive tasks. In this module you will learn about creating and using modules, which is a group of functions. You will then learn about two of the most important modules for data analytics: NumPy and Pandas.This course aims to teach everyone the basics of programming computers using Python.
We cover the basics of how one constructs a program from a series of simple instructions in Python. The course has no pre-requisites and avoids all but the simplest mathematics.
Anyone with moderate computer experience should be able to master the materials in this course. Once a student completes this course, they will be ready to take more advanced programming courses. This course covers Python 3. The professor's lecture is so awesome, however, the content is adjust to the new programming learners.
I heared extended part of the course is charged, you can purchased it if you want deeper learning. After trying tutorial after tutorial and exploring many different resources to learn Python, I have finally found one that works! This is a very fun course, and the free textbook is simply incredible. These are the course-wide materials as well as the first part of Chapter One where we explore what it means to write programs.
We finish Chapter One and have the quiz and first assignment in the third week of the class. Throughout the course you may want to come back and look at these materials. This section should not take you an entire week. Loupe Copy. Programming for Everybody Getting Started with Python.
Course 1 of 5 in the Python for Everybody Specialization. Enroll for Free. From the lesson. Taught By. Charles Russell Severance Clinical Professor. Try the Course for Free. Explore our Catalog Join for free and get personalized recommendations, updates and offers.
All rights reserved.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. 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.
After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Jupyter Notebook Python. Jupyter Notebook Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again.
Latest commit. Latest commit d May 3, You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Nov 24, Data and Figure Update. Jul 14, May 3, Assignments, Slides, Certificate and Readme. Oct 4, Original Data.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. This repository is aimed to help Coursera and edX learners who have difficulties in their learning process. The quiz and programming homework is belong to coursera and edx and solutions to me.
Johns Hopkins University - R Programming.
Coursera: Machine Learning (Week 1) Quiz - Linear Regression with One Variable | Andrew NG
Johns Hopkins University - Ruby on Rails. University of London - Responsive Web Design. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up.
1.3 - Python as a Language
Racket Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Latest commit 10d7dc1 Apr 7, Coursera and edX Assignments This repository is aimed to help Coursera and edX learners who have difficulties in their learning process.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. This repository is aimed to help Coursera learners who have difficulties in their learning process. The quiz and programming homework is belong to coursera. Please Do Not use them for any other purposes.
Please feel free to contact me if you have any problem,my email is wcshen Learn to Program: Crafting Quality Code. Specialization Applied Data Science with Python. Specialization Deep Learning. Specialization Functional Programming in Scala. Specialization Meachine Learning-University of Washington. Specialization Recommender System-University of Minnesota. Specialization Statistics with R-Duke University. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
Sign up. Jupyter Notebook Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again.
Latest commit.This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively.
By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course was fast paced but the material was interesting and not to complex. I can only recommend this course to anyone interested in Data Science and who already has a basic knowledge of Python. I thought this was course was good, and was fairly challenging for an online-only course. I thought the lectures could have been a little longer to ensure proper coverage of materials and functions.
In this week you'll get an introduction to the field of data science, review common Python functionality and features which data scientists use, and be introduced to the Coursera Jupyter Notebook for the lectures. All of the course information on grading, prerequisites, and expectations are on the course syllabus, and you can find more information about the Jupyter Notebooks on our Course Resources page. Loupe Copy.
Introduction to Data Science in Python. Enroll for Free. From the lesson. Introduction to Specialization Data Science The Coursera Jupyter Notebook System Python Functions Python Types and Sequences Python More on Strings Python Dates and Times Advanced Python Objects, map