Python Fundamentals LiveLessons Part I

Python Fundamentals LiveLessons Part I
Python Fundamentals LiveLessons Part I
English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 7h 42m | 3.80 GB

The professional programmer’s Deitel® video guide to Python development with the powerful IPython and Jupyter Notebooks platforms.

Python Fundamentals LiveLessons with Paul Deitel is a code-oriented presentation of Python–one of the world’s most popular and fastest growing languages. In the context of scores of real-world code examples ranging from individual snippets to complete scripts, Paul will demonstrate coding with the interactive IPython interpreter and Jupyter Notebooks. You’ll quickly become familiar with the Python language, its popular programming idioms, key Python Standard Library modules and several popular open-source libraries. In the Intro to Data Science videos, Paul lays the groundwork for later lessons in which he’ll introduce some of today’s most compelling, leading-edge computing technologies, including natural language processing, data mining Twitter® for sentiment analysis, cognitive computing with IBM® Watson™, supervised machine learning with classification and regression, unsupervised machine learning with clustering, computer vision through deep learning and convolutional neural networks, sentiment analysis through deep learning with recurrent neural networks, big data with Hadoop®, Spark™ streaming, NoSQL databases and the Internet of Things.

What you Will Learn

  • Before You Begin–Configure your system for Python, obtain the code examples, Python package managers, Paul’s contact info
  • Lesson 1–Test-Drives: Using IPython and Jupyter Notebooks–Work with snippets and scripts in the context of IPython and Jupyter Notebooks
  • Lesson 2–Intro to Python Programming–Variables, types, operators, strings, I/O, decisions, objects and dynamic typing
  • Lesson 3–Control Statements–if, if…else, if…elif…else, for, while, break, continue, augmented assignments, boolean operators, intro to lists
  • Lesson 4–Functions–Custom function definitions, importing libraries, simulation with random-number generation, scope, default parameter values, keyword arguments, arbitrary argument lists, methods, intro to tuples, intro to functional-style programming
Table of Contents

1 Introduction to Python Fundamentals LiveLessons – Part 1
2 Lesson overview
3 Getting the code
4 Structure of the examples folder
5 Installing Anaconda
6 Updating Anaconda
7 Package managers
8 Installing jupyter-matplotlib
9 Twitter developer account
10 Getting your questions answered
11 Lesson overview
12 Using IPython Interactive Mode as a Calculator
13 Executing a Python Program Using the IPython Interpreter
14 Writing and Executing Code in a Jupyter Notebook
15 Lesson overview
16 Variables and Assignment Statements
17 Self Check
18 Arithmetic
19 Self Check
20 Function print and an Intro to Single- and Double-Quoted Strings
21 Self Check
22 Triple-Quoted Strings
23 Self Check
24 Getting Input from the User
25 Self Check
26 Decision Making – The if Statement and Comparison Operators
27 Self Check
28 Objects and Dynamic Typing
29 Self Check
30 Intro to Data Science – Basic Descriptive Statistics
31 Self Check
32 Lesson overview
33 if Statement
34 Self Check
35 if…else and if…elif…else Statements
36 Self Check
37 while Statement
38 Self Check
39 for Statement; Iterables, Lists and Iterators; Built-in range Function
40 Self Check
41 Augmented Assignments
42 Self Check
43 Sequence-Controlled Iteration
44 Self Check
45 Sentinel-Controlled Iteration
46 Built-In Function range – A Deeper Look
47 Self Check
48 Using Type Decimal for Monetary Amounts
49 Self Check
50 break and continue Statements
51 Boolean Operators and, or and not
52 Self Check
53 Intro to Data Science РMeasures of Central Tendency—Mean, Median and Mode
54 Self Check
55 Lesson overview
56 Defining Functions
57 Self Check
58 Functions with Multiple Parameters
59 Self Check
60 Random-Number Generation
61 Self Check
62 Case Study – A Game of Chance
63 Self Check
64 math Module Functions
65 Default Parameter Values
66 Keyword Arguments
67 Arbitrary Argument Lists
68 Self Check
69 Methods – Functions That Belong to Objects
70 Scope Rules
71 import – A Deeper Look
72 Self Check
73 Passing Arguments to Functions – A Deeper Look
74 Self Check
75 Functional-Style Programming
76 Intro to Data Science – Measures of Dispersion