Last updated 2/2019MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 3.22 GB | Duration: 9h 13m
Learn to code in Python from scratch with hands-on projects What you'll learn Implement the List and Dictionary data types to take text as input and produce a word count Work with Python Modules to create your first web-scraping app in Python Handle files using your Python code to build your own Python-based text editor Programming in Python using a modular approach Developing apps using object-oriented Python programming Build powerful Graphical User Interfaces (GUIs) Speed up your code with natively Python idioms Requirements This course doesn't assume any knowledge of Python or Python programming experience. Description Python is an open-source community-supported, a general-purpose programming language that, over the years, has also become one of the bastions of data science. Thanks to its flexibility and vast popularity that data analysis, visualization, and machine learning can be easily carried out with Python.With this application development course with Python 3, you'll first learn about variables, control flow statements & much more make use of them in Python programs. Then you will learn to use Python's advanced data structures such as lists and dictionaries. Then you will get a hands-on project building such as build a game that consists of a deck of playing cards, Dice-Rolling Simulator in Python, Building Architectural Marvels & much more. Moving further, you'll learn to troubleshoot your python application where you can quickly detect which lines of code are causing problems, and fix them quickly without going through 300 pages of unnecessary detail.Contents and OverviewThis training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, B Python Programming in 7 Days will get you started by setting up your environment and the tools you need to start programming in Python. You will be learning about variables and operators and how to make use of them in Python programs. You will learn all about control flow statements and loops in Python and you will be using them in your programs to solve your coding problems. Then you will learn to use Python's advanced data structures such as lists and dictionaries. You will be able to organize in functions and save coding by writing code that can be reused. Then, you will learn about Python modules and how to make use of them. On the last day, you will start interacting with files using Python code. The course will give you a strong entry point into programming in general and programming in Python in particular.The second course, Python By Example explores Python basics, data structures, and algorithms. We'll build a die rolling simulator to see how to use Python dictionaries, loops, functions, and control statements. Then, we will see how we can develop dictionaries that contain other dictionaries to build complex data structures. Next, we will use a modular approach to build a game that consists of a deck of playing cards. We will use object-oriented (OOP) Python classes to do so. We will display the playing cards both in a textual form, which we create, as well as via image files. This will lead to our displaying card images in a graphical form using Python's built-in Tkinter package. In the next part, we will use multiple inheritances with OOP classes. While the Java and C# programming languages are limited to only single inheritance, Python classes can inherit from multiple classes. You will learn how to use multiple inheritances with Python. You will also build Graphical User Interfaces (GUIs). We will use Python's built-in Tkinter package and delve more deeply into GUI programming. By the end of this video tutorial, you will have built some useful utilities using Python. Python is very strong at searching directory folders, replacing words within modules, and much more. You will find these utilities useful in your everyday work as a developer.The third course, Troubleshooting Python Application Development takes you through a structured journey of performance problems that your application is likely to encounter, and presents both the intuition and the solution to these issues. You'll get things done, without a lengthy detour into how Python is implemented or computational theory. Quickly detect which lines of code are causing problems, and fix them quickly without going through 300 pages of unnecessary detail.About the Authors:Colibri Digital is a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help its clients navigate the rapidly chag and complex world of emeg technologies, with deep expertise in areas such as big data, data science, machine learning, and cloud computing. Over the past few years, they have worked with some of the world's largest and most prestigious companies, including a tier 1 investment bank, a leading management consultancy group, and one of the World's most popular soft drinks companies, helping each of them to make better sense of its data, and process it in more intelligent ways. The company lives by its motto: Data -> Intelligence -> Action.Rudy Lai is the founder of QuantCopy, a sales acceleration startup using AI to write sales emails to prospects. By taking in leads from your pipelines, QuantCopy researches them online and generates sales emails from that data. It also has a suite of email automation tools to schedule, send, and track email performance—key analytics that all feed back into how our AI generates content. Prior to founding QuantCopy, Rudy ran HighDimension.IO, a machine learning consultancy, where he experienced first-hand the frustrations of outbound sales and prospecting. As a founding partner, he helped startups and enterprises with High Dimension, IO's Machine-Learning-as-a-Service, allowing them to scale up data expertise in the blink of an eye.In the first part of his career, Rudy spent 5+ years in quantitative trading at leading investment banks such as Morgan Stanley. This valuable experience allowed him to witness the power of data, but also the pitfalls of automation using data science and machine learning. Quantitative trading was also a great platform to learn deeply about reinforcement learning and supervised learning topics in a commercial setting. Rudy holds a Computer Science degree from Imperial College London, where he was part of the Dean's List, and received awards such as the Deutsche Bank Artificial Intelligence prize.Burkhard is a professional software test automation designer, developer, and analyst. He has more than 18 years' professional experience working for several software companies in California, USA. He currently works as an independent Python consultant from New York. He is the author of the Python GUI Programming Recipes using PyQt5 Packt video course. He is the author of Python GUI Programming Cookbook, First and Second Edition. This book is also available as a Packt video course. He is also the author of the Python Projects Packt video course. In his professional career, he has developed advanced in-house testing frameworks written in Python 3. He has also created advanced test automation GUIs in Python, which highly increased the productivity of the software development testing team. When not dreaming in Python code, he reads programming books about design, likes to go for walks, and reads classical poetry. Overview Section 1: B Python Programming in 7 Days Lecture 1 The Course Overview Lecture 2 Installing Python and Code Editor Lecture 3 Getting Familiar with Command Line Lecture 4 Writing and Running Your First Python Program Lecture 5 Understanding Basic Syntax Lecture 6 Assignment Day 1 Lecture 7 Understanding Python Variables Lecture 8 Using Variables in Code Lecture 9 Understanding Python Operators Lecture 10 Usage of Python Operators Lecture 11 Assignment Day 2 Lecture 12 Introducing Control Statements Lecture 13 Usage of Control Statements Lecture 14 Understand Loops Lecture 15 Use Loops in Your Python Code Lecture 16 Assignment Day 3 Lecture 17 Introducing Python Lists Lecture 18 List Operators, Functions, and Methods Lecture 19 Introducing the Dictionary Data Type Lecture 20 Dictionary Operators, Functions, and Methods Lecture 21 Assignment Day 4 Lecture 22 Introducing Functions Lecture 23 Usage of Functions in Your Code Lecture 24 Understanding Scope of Variables Lecture 25 Example Code for a Scope of Variable Demonstration Lecture 26 Assignment Day 5 Lecture 27 Python Modules Lecture 28 Using Third-Party Python Modules Lecture 29 Compiling Python Files Lecture 30 Using Python Packages Lecture 31 Assignment Day 6 Lecture 32 Reading Text from a File Lecture 33 Writing Text to a File Lecture 34 Handling Exceptions Lecture 35 Assignment Day 7 Section 2: Python By Example Lecture 36 The Course Overview Lecture 37 Dice Rolling Simulator Lecture 38 Nesting Python Dictionaries Lecture 39 Using Python Generators Lecture 40 Iterating over Lecture 41 Deck of Cards Game Using Textual Cards Lecture 42 Deck of Cards Game Using Graphical Cards Lecture 43 Correctly Sizing Image Files Lecture 44 Playing the Game Lecture 45 Laying the Foundation Lecture 46 Blue Prints of Architectural Design Lecture 47 Building Our First Building Lecture 48 The Greatness of Software Applied Lecture 49 Building a Graphical User Interface Lecture 50 Adding Many Widgets Lecture 51 Using Several Layered Notebooks Lecture 52 Making Our GUI Pretty Lecture 53 Searching Directories Lecture 54 Replacing Words Within Modules Lecture 55 Administration Tasks Lecture 56 Making Life Easy with Automation Section 3: Troubleshooting Python Application Development Lecture 57 The Course Overview Lecture 58 Measuring Between Two Lines of Code with it Lecture 59 Figuring out Where Is Spent with the Profile Module Lecture 60 More Precise Tracking with cProfile Lecture 61 Looking at Memory Consumption with memory_profiler Lecture 62 Reduce Execution and Memory Consumption with __slots__ Lecture 63 Use Tuples Instead of Lists When Your Data Does Not Change Lecture 64 Save on Memory Consumption with Generators Instead of Lists Lecture 65 When to Use Lists Instead of Generators Lecture 66 Leveraging Itertools to Create Generator Pipelines Lecture 67 The Problem with Using Lists to Perform Vector Calculations Lecture 68 Using NumPy’s Arrays for More Powerful Vector Representations Lecture 69 Rewriting Our Problem with NumPy to Speed It up 40x Lecture 70 Fast MapReduce with NumPy Broadcasting Lecture 71 Optimize All Calculations in One Go with numexpr Lecture 72 The Problem of Serially Executing Web Scraping Calls Lecture 73 Simple Asynchronous Programming with coroutines and gevent Lecture 74 Event-Driven Concurrency with Tornado Lecture 75 Concurrency and Futures with asyncio Lecture 76 Getting Started with Parallel Programming Lecture 77 Doubling the Speed of Your List Processing with Tuples Lecture 78 Easily Speed up a Group of Processes with Pool Lecture 79 Stop Processes from Interfering with Each Other with Locks Lecture 80 Logging What Happens When You Have Many Processes Lecture 81 Stop Modifying the Wrong Object Instance with Correct Object Cloning Lecture 82 Speed Up Your OOP with namedtuples Lecture 83 Reduce Getters and Setters with Static Methods and Properties Lecture 84 Comparing Two Different Objects Lecture 85 Improve Readability with Abstract Base Classes in Python This course is for Python developers, who would like to learn the Python programming language in a hands-on way & tackle application performance problems to speed up your apps. 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