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98-381 exam Dumps Source : Introduction to Programming Using Python

Test Code : 98-381
Test designation : Introduction to Programming Using Python
Vendor designation : Microsoft
: 40 real Questions

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    Introduction to Programming Using Python

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    5 Free Courses to Learn Python in 2019 | killexams.com real questions and Pass4sure dumps

    Free Python Courses for Programmers and Developers

    Hello guys, If you are a beginner looking for some Free Python resources to start your programming journey in 2019 then you hold foster to the birthright place.

    Earlier, I hold shared a brace of free Python Programming eBooks sand today I’ll share a brace of expedient Python programming courses which are absolutely FREE!! You can recall these online courses to learn Python at your own pace, at your own time, and at your place.

    This is a powerful advantage of online learning, the flexibility it provides is just awesome. You just necessity a laptop or a smartphone with an internet connection and you can learn anything.

    Btw, before to start with the list of courses to learn Python programming I want to congratulate you on making the birthright determination to start your programming journey with Python. Many beginners, students and people starting with programming examine this question to me every day. Should I start with Python or Java? Even though I am a Java developer, I examine people to start with Python because of its awesome and multi-purpose features.

    Python is effortless to learn, easier compared to even Java. You can too write wee Python scripts to quickly automate things you normally consequence manually and that provides a powerful value to beginners.

    Python is too powerful, feature rich and multi-purpose. For example, you can utilize Python for web development, you can utilize it to create scripts, and you can even utilize it in the space of Data Science and Machine learning.

    This seriously makes learning Python an distinguished skill which will pay you throughout your career. I hold always advised sum my readers and students sail along with SQL and UNIX, you should too learn Python. One of the powerful programming skill every programmer should have. That’s the intuition I hold listed is one of the essential tools for programmers.

    5 Free Courses to Learn Python Programming

    Now that you know that learning Python is powerful for your programming career its time to actually learn Python. Whenever I start with a current technology I usually ensue my 3 point model love link an online course, buy a book, and consequence a project. This way I hold mastered several current technologies sum by myself without going into expensive coaching classes or boot camps and nothing beats free resources to start with. In the past I hold shared free books to learn Python and today I will share some of the best online courses you recall to learn Python Programming for free. The project Part is something which you can consequence yourself once you learn Python by going through these courses and books.

    1. Introduction To Python Programming

    If you necessity a quick brush-up or learning Python for the first time then this is the flawless course for you.

    This is quite improbable that the instructor himself is a 17-year-old student and this Python course hold more than 130K students enrolled in Udemy, which speaks volume about the course.  Here is the link to link the course: Introduction To Python Programming This course is a one-stop-shop for everything you’ll necessity to know to score started with Python, along with a few incentives.

    You will start with the basics of Python, learning about strings, variables, and getting to know the data types. You will then learn other essential programming constructs e.g. loops and conditions in Python.

    The course too teaches you file manipulation and functions. In short, a Quick and effortless Intro to Python Programming.

    Introduction To Python Programming 2. Python for Absolute Beginners

    This is another powerful introductory course on Python for people who want to learn Python as well as Programming. It doesn’t matter if you’ve had no programming sustain whatsoever. This course starts Python programming from scratch, showing you everything from how to set up a Python IDE to how to utilize list comprehensions.

    Here is the link to link the course for FREE: Python for Absolute Beginners

    The best Part of the course is that every current concept is taught with source code slides and exercise problems for you to toil through. You will too be provided with downloadable solutions to the exercise problems. This learn-by-doing approach is powerful for beginners who can quickly learn both Programming and Python by following this course.

    Python for Absolute Beginners 3. Programming with Python: Hands-On Introduction for Beginners

    This is another awesome course to learn Python online. The course has been specifically designed for beginners who hold been looking to obtain a hands-on learning sustain with Python. It teaches you programming concepts birthright from the basics of Python. Since Python is effortless to learn, it too makes it effortless to learn to programme.

    Here is the link to link the course: Programming with Python: Hands-On Introduction for Beginners

    The course too provides a lot of exercise problems, programming lectures, PDF notes for a complete sustain and everything are free of cost. In short, a powerful Python course for beginners who are passionate to learn to programme even if they haven’t written a sole line of code in their entire life and wants to start with Python.

    Programming with Python: Hands-On Introduction for Beginners 4. Python Programming for Absolute Beginners

    This is a powerful practical Python course for beginners with replete of examples and exercises, particularly useful for people coming from a different background with no programming experience. This course is ideal for those who haven’t programmed before, but it should too toil for experienced programmers who want to learn Python as long as they don’t score offended by a few extra explanations.

    Here is the link to link the course for FREE: Python Programming for Absolute Beginners You will start from scratch and by the discontinuance of the course you will know how to write Python programs and learn basics about lists, dictionary, and other essential Python data structures.

    You will too learn about functions, modules and lambda expression in Python.

    Overall a nice beginner course with 2.5 hours of content for free. Talking about convivial proof the course has got on tolerable 4.3 ratings from near to 7000 students.

    Python Programming for Absolute Beginners 5. Learn Python 3 From Scratch

    If you want to learn Object-oriented programming with Python then this is the flawless course for you.

    This course will discipline you how to structure your Python code by making utilize of Classes and Objects. You will too learn how to achieve object-oriented programming in Python by learning how to bundle attributes and methods within a class and instantiating them through an object.

    Here is the link to link the course: Learn Python 3 From Scratch

    At the discontinuance of this course, you will be able to write your own Python 3 programs in Python!

    Learn Python 3 From Scratch

    That’s on this list of free Python Programming courses for beginners. As I hold said before, Python is an awesome, multipurpose programming language and every programmer should learn it.

    You can automate things using Python by writing scripts, can consequence object-oriented programming and can even explore the world of web development, data science and machine learning using awesome Python libraries and modules.  Other Free Books and Courses for Programmers 5 Online Courses to learn Core Java for Free 5 Free Courses to Learn Maven and Jenkins 5 Courses to learn Android for Java Developers 5 Free Courses to Learn Data Structures and Algorithms 5 Free Spring Framework Courses for Java Developers 5 Online training courses to learn Angular for Free 5 Node JS courses for Java and Web Developers 5 Courses to Learn colossal Data and Apache Spark 5 Free Courses to Learn Git and Github Thanks for reading this article so far. If you love these free Python courses then please share with your friends and colleagues. If you hold any questions or feedback then please drop a note.

    P.S.- Though these courses are expedient they are not comprehensive enough to cover everything a Programmer needs to know about Python. If you feel so and interested to learn more, I imply you link The Complete Python 3 Bootcamp. It’s not free but it’s completely worth your money.

    This narrative is published in The Startup, Medium’s largest entrepreneurship publication followed by +415,678 people. Subscribe to receive their top stories here.

    Going deep: Lab employees score an introduction to world of machine learning, neural networks | killexams.com real questions and Pass4sure dumps

    Deep learning is one of the most celebrated and widely used machine learning methods due to its success with autonomous vehicles, speech recognition and image classification, to designation a few emergent technologies. But what exactly is abysmal learning, and how can it best be applied to Lab projects?

    Machine learning scientist Brenda Ng taught a “Deep Learning 101” course, which introduced the basics of neural networks and abysmal learning to anyone with a basic erudition of programming in Python. Photos by Julie Russell/LLNL

    Lawrence Livermore National Lab (LLNL) employees discovered the answers during a recent “Deep Learning 101” course, which introduced the basics of neural networks and machine learning to anyone with a basic erudition of programming in Python. The five-part series, which concluded Jan. 31, included lectures, demonstrations and labs, where attendees performed hands-on toil in coding and training their own neural networks. The teaching staff included CED machine learning scientist Brenda Ng, GS-CAD data scientist Luke Jaffe and CED computer vision scientist Nathan Mundhenk, with advocate from teaching assistant and CED machine learning engineer Sam Nguyen.

    “There are many efforts around the Lab that are doing abysmal learning, but they want to acquire it more accessible to everybody,” Ng said. “Not everybody has a machine learning background, and there’s just so much information out there, in terms of tutorials and blogs, and you’re not confident which ones to even leer at. The goal is to give more practical erudition to their scientists here, so they’re not just reading about it, but they will feel empowered to try some of these models and tools in their work.”

    The learning chain consisted of five 75-minute classes. Each class started with a lecture by Ng that covered the abysmal learning topic of the day. The lecture was followed by a programming lab taught by Jaffe that explained the actual code (in the Python machine learning library PyTorch) to implement the model covered in the lecture.

    “We wanted to expose how effortless abysmal learning can be if you utilize the birthright tools,” Jaffe said. “We wanted to remove the stigma that abysmal learning is a complicated black box that can only be done with supercomputers. The labs are designed to expose that employees can score tangible improvements to their work, running on a laptop, starting birthright now.”

    The five-part series, which concluded Jan. 31, included lectures, demonstrations and labs, where attendees performed hands-on toil in coding and training their own neural networks.

    Session topics included convolutional neural networks, generative adversarial networks, autoencoders and recurrent networks. The last class covered a practical tutorial on using Livermore Computing resources to train abysmal learning models, taught by Mundhenk. The course material was adopted from abysmal learning workshops and open-source code examples, interweaving the staff’s personal abysmal learning experiences to afford Lab employees with a property learning experience.

    In all, 91 people signed up for the course, which was originally conceived as a machine learning chain for the Engineering postdocs and was fully funded by the Engineering postdoc program. However, due to overwhelming demand, the chain was opened to anyone who was interested and had some familiarity with Python programming and library installations, as well as basic calculus and linear algebra.

    “The Engineering postdoc program is pleased to be able to advocate this benign of skill expansion and mentorship at the Laboratory,” said Engineering postdoc program coordinator Nathan Barton. “Building abilities to toil across disciplines is a powerful way to invest in workforce development. And people’s sustained interest in expanding their erudition helps to acquire the Lab a powerful set to work.”

    Wenqin Li, a current postdoc in the energy utilization and delivery group in CED Engineering, attended the entire chain and said it Inspired her to find current ways to succor decipher energy problems.

    “DL101 is a powerful class for researchers who are a stout believer of applying machine learning to succor decipher their engineering problems,” Li said. “Brenda did a powerful job by explaining the most tangled terms in the simplest way. Luke too designed very titillating lab demos too, which provides us powerful hands-on experience.”

    MED postdoc Eric Elton said he wanted to recall the course because had no erudition of machine learning or AI but thought that getting a better grasp of the fundamentals would succor him be better able to toil with others who program machine learning methods.

    “I hold a much better grasp of how ML works now, not just from a programming perspective but too from a fundamental perspective,” Elton said. “The course has really taken the mystery out of what exactly the computers are doing when someone says, ‘we used AI to consequence x.’ I’m too much more cognizant of the limits of machine learning now than I was before the course, and why I can’t just examine the computer to decipher some equations in the context of this data, or something love that.”

    Another abysmal Learning 101 attendee, Farid Dowla, said: “This has been really an excellent class. It’s been a solid discussion on theory, techniques, tools and literature. It will benefit many at LLNL and certainly myself. I hold been in this zone for more than 20 years, and I can remark how things hold changed recently. I highly recommend the staff in this class. They necessity to hold more of these.”

    Source: LLNL

    Thread Carefully: An Introduction To Concurrent Python | killexams.com real questions and Pass4sure dumps

    The competence to execute code in parallel is crucial in a wide variety of scenarios. Concurrent programming is a key asset for web servers, producer/consumer models, batch number-crunching and pretty much any time an application is bottlenecked by a resource.

    It’s sadly the case that writing property concurrent code can be a real headache, but this article aims to demonstrate how effortless it is to score started writing threaded programs in Python. Due to the big number of modules available in the yardstick library which are there to succor out with this benign of thing, it’s often the case that simple concurrent tasks are surprisingly quick to implement.

    We’ll walk through the contrast between threads and processes in a Python context, before reviewing some of the different approaches you can recall and what they’re best suited for.

    (Python 3 is used for the duration of the article.)

    The Global Interpreter Lock

    It’s impossible to talk about concurrent programming in Python without mentioning the Global Interpreter Lock, or GIL. This is because of the big impact it has on which approach you select when writing asynchronous Python. The most distinguished thing to note is that it is only a feature of CPython (the widely used “reference” Python implementation), it’s not a feature of the language. Jython and IronPython, among other implementations, hold no GIL.

    The GIL is controversial because it only allows one thread at a time to access the Python interpreter. This means that it’s often not viable for threads to recall advantage of multi-core systems. Note that if there are blocking operations which betide outside Python, long-wait tasks love I/O for instance, then the GIL is not a bottleneck and writing a threaded program will noiseless be a benefit. However, if the blocking operations are largely crunching through CPython bytecode, then the GIL becomes a bottleneck.

    Why was the GIL introduced at all? It makes memory management much simpler with no possibility of simultaneous access or race conditions, and it makes C extensions easier to write and easier to wrap.

    The upshot of sum this is that if you necessity valid parallelism and necessity to leverage multi-core CPUs, threads won’t slit it and you necessity to utilize processes. A sunder process means a sunder interpreter with sunder memory, its own GIL, and valid parallelism. This lead will give examples of both thread and process architectures.

    The concurrent.futures module

    The concurrent.futures module is a well-kept secret in Python, but provides a uniquely simple way to implement threads and processes. For many basic applications, the effortless to utilize Pool interface offered here is sufficient.

    Here’s an case where they want to download some webpages, which will be much quicker if done in parallel.

    """Download webpages in threads.""" import requests from concurrent.futures import ThreadPoolExecutor download_list = [ {'name': 'google', 'url': "http://google.com"}, {'name': 'reddit', 'url': "http://reddit.com"}, {'name': 'ebay', 'url': "http://ebay.com"}, {'name': 'bbc', 'url': "http://bbc.co.uk"} ] def download_page(page_info): """Download and deliver webpage.""" r = requests.get(page_info['url']) with open(page_info['name'] + '.html', 'w') as save_file: save_file.write(r.text) if __name__ == '__main__': pool = ThreadPoolExecutor(max_workers=10) for download in download_list: pool.submit(download_page, download)

    Most of the code is just setting up their downloader example; it’s only the last obstruct which contains the threading-specific code. Note how effortless it is to create a dynamic pool of workers using ThreadPoolExecutor and submit a task. They could even simplify the last two lines to one using map:

    pool.map(download_page, download_list)

    Using threads works well in this case since the blocking operation that benefits from concurrency is the act of fetching the webpage. This means that the GIL is not an issue and threading is an ideal solution. However, if the operation in question was something which was CPU intensive within Python, processes would likely be more preempt because of the restrictions of the GIL. In that case, they could hold simply switched out ThreadPoolExecutor with  ProcessPoolExecutor.

    The threading module

    Whilst the concurrent.futures module offers a powerful way to score off the ground quickly, sometimes more control is needed over different threads, which is where the ubiquitous threading module comes in.

    Let’s re-implement the website downloader they made above, this time using the threading module.

    """Download webpages in threads, using `threading`.""" import requests import time import threading download_list = [ {'name': 'google', 'url': "http://google.com"}, {'name': 'reddit', 'url': "http://reddit.com"}, {'name': 'ebay', 'url': "http://ebay.com"}, {'name': 'bbc', 'url': "http://bbc.co.uk"} ] def status_update(): """Print 'Still downloading' at regular intervals.""" while True: print("Still downloading") time.sleep(0.1) def download_page(page_info): """Download and deliver webpage.""" r = requests.get(page_info['url']) with open(page_info['name'] + '.html', 'w') as save_file: save_file.write(r.text) if __name__ == '__main__': for download in download_list: downloader = threading.Thread(target=download_page, args=(download,)) downloader.start() status = threading.Thread(target=status_update) status.start()

    For each thread they want to create, they acquire an instance of the threading.Thread class, specifying what they would love their worker role to be, and the arguments required.

    Note that we’ve too added a status update thread. The purpose of this is to repeatedly print “Still downloading” until we’ve finished fetching sum the web pages. Unfortunately, since Python waits for sum threads to finish executing before it exits, the program will never exit and the status updater thread will never desist printing.

    This is an case of when the threading module’s host of options could be useful: they can price the updater thread as a daemon thread, which means that Python will exit when only daemon threads are left running.

    status = threading.Thread(target=status_update) status.daemon = True status.start()

    The program now successfully stops printing and exits when sum downloader threads are finished.

    Daemon threads are generally most useful for background tasks and repetitive functions which are only required when the main program is running, since a daemon can be killed at any moment, causing data loss.

    The combination of threading and queues

    So far we’ve only looked at cases where they know exactly what they want the threads to be working on when they start them. However, it’s often the case that they necessity to start a group of worker threads, then feed them tasks as they arrive.

    The best data structure for dealing with these tasks is, of course, a queue, and Python provides a queue module which is especially geared towards threading applications. FIFO, LIFO and priority queues are available.

    Using a queue.Queue object to add, get, and price tasks as done is as simple as this:

    from queue import Queue # maxsize=0 means illimitable size limit tasks = Queue(maxsize=0) tasks.put("a task") tasks.put("another task") while not tasks.empty(): print(tasks.get()) # execute task tasks.task_done()

    Ok, that’s pretty basic so far. Now let’s utilize it to create a tasks queue for their website downloader. We’ll create a group of worker threads which can sum access the queue and wait for tasks to foster in.

    """Download webpages in threads, using `threading` and `queue`.""" import requests import threading from queue import Queue NUM_WORKER_THREADS = 3 def download_page(page_info): """Download and deliver webpage.""" r = requests.get(page_info['url']) with open(page_info['name'] + '.html', 'w') as save_file: save_file.write(r.text) def handle_tasks(tasks_queue): """Monitor tasks queue and execute tasks as appropriate.""" while True: download_page(tasks_queue.get()) tasks_queue.task_done() if __name__ == '__main__': tasks = Queue(maxsize=0) # Create and start worker threads for i in range(NUM_WORKER_THREADS): worker = threading.Thread(target=handle_tasks, args=(tasks,)) worker.daemon = True worker.start() # Add some tasks to the queue tasks.put({'name': 'google', 'url': "http://google.com"}) tasks.put({'name': 'reddit', 'url': "http://reddit.com"}) tasks.put({'name': 'ebay', 'url': "http://ebay.com"}) tasks.put({'name': 'bbc', 'url': "http://bbc.co.uk"}) tasks.join()

    Note that in this case sum the tasks were added in one sail for the sake of brevity, but in a real application the tasks could trickle in at any rate. Here they exit the program when the tasks queue has been fully completed, using the .join() method.

    The multiprocessing module

    The threading module is powerful for circumstantial control of threads, but what if they want this finer flush of control for processes? You might mediate that this would be more challenging since once a process is launched, it’s completely sunder and independent – harder to control than a current thread which remains within the current interpreter and memory space.

    Fortunately for us, the Python developers worked arduous to create a multiprocessing module which has an interface that is almost identical to the threading module. This means that launching processes follows the exact very syntax as their examples above. Their simple downloader would become this:

    """Download webpages in threads, using `multiprocessing`.""" import requests import time import multiprocessing download_list = [ {'name': 'google', 'url': "http://google.com"}, {'name': 'reddit', 'url': "http://reddit.com"}, {'name': 'ebay', 'url': "http://ebay.com"}, {'name': 'bbc', 'url': "http://bbc.co.uk"} ] def status_update(): """Print 'Still downloading' at regular intervals.""" while True: print("Still downloading") time.sleep(0.1) def download_page(page_info): """Download and deliver webpage.""" r = requests.get(page_info['url']) with open(page_info['name'] + '.html', 'w') as save_file: save_file.write(r.text) if __name__ == '__main__': for download in download_list: downloader = multiprocessing.Process(target=download_page, args=(download,)) downloader.start() status = multiprocessing.Process(target=status_update) status.daemon = True status.start()

    We mediate it’s awesome that Python manages to sustain the same syntax between the threading and multiprocessing modules, when the action taking set under the hood is so different.

    When it comes to distributing data between processes, the queue.Queue that they used for threading will not toil between processes. This is because a queue.Queue is fundamentally just a data structure within the current process – albeit one which is cleverly locked and mutexed. Thankfully there exists a multiprocessing.Queue, which is specifically designed for inter-process communication. Behind the scenes, this will serialize your data and route it through a pipe between processes – a very convenient abstraction.


    Writing concurrent code in Python can be a lot of fun due to the inbuilt language features that abstract away a lot of problems. This doesn’t weigh that a circumstantial flush of control cannot be achieved either, but rather that the barrier to getting started with simple tasks is lowered. So when you’re stuck waiting for one process to finish before starting the next, give one of these techniques a try.

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