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Machine Learning - Python Programming: From Beginner to Intermediate

Video Description

Dive into this popular and powerful programming language!This is a Python course for absolute beginners. You will learn to write Python programs, perform text processing, apply simple machine learning concepts, and so much more! By the time you’re finished with this intensive video training, you will have gone from zero experience to a fairly serious, early intermediate level. Supplemental Material included! Length: 10 hrs 30 min. Python Programming: From Beginner to Intermediate is an essential training course for anyone who wants to begin learning Python. Using a Python IDE (integrated development environment) called iPython from Anaconda, the expert instructors in this course will lead you step-by-step through topics such as: functional language constructs, automated reports, website scraping, and natural language processing. Get started with Python and how it is used in Machine Learning and Artificial Intelligence today!

Table of Contents

  1. WHAT IS CODING? - IT'S A LOT LIKE COOKING!
    1. Introduction 00:02:52
    2. Coding is Like Cooking 00:07:36
    3. Anaconda & Pip 00:09:00
    4. Variables are Like Containers 00:11:01
  2. DON'T JUMP THROUGH HOOPS - USE DICTIONARIES, LISTS AND LOOPS
    1. A List is a List 00:09:17
    2. Fun with Lists! 00:08:45
    3. Dictionaries & If-Else 00:06:19
    4. Don't Jump through Hoops - Use Loops 00:04:27
    5. Doing Stuff with Loops 00:05:29
    6. Everything in Life is a List - Strings as Lists 00:07:08
  3. OUR FIRST SERIOUS PROGRAM
    1. Modules are Cool for Code-Reuse 00:02:32
    2. Our First Serious Program - Downloading a Webpage 00:17:49
    3. A Few Details - Conditionals 00:07:49
    4. A Few Details - Exception Handling in Python 00:07:48
  4. DOING STUFF WITH FILES
    1. A File is Like a Barrel 00:11:22
    2. Auto-Generating Spreadsheets with Python 00:09:10
    3. Auto-Generating Spreadsheets - Download & Unzip 00:17:14
    4. Auto-Generating Spreadsheets - Parcing CSV Files 00:18:37
    5. Auto-Generating Spreadsheets with XLSXwriter 00:05:27
  5. FUNCTIONS ARE LIKE FOOD PROCESSORS
    1. Functions are Like Food Processors 00:10:58
    2. Argument Passing in Functions 00:16:31
    3. Writing Your First Function 00:12:54
    4. Recursion 00:16:59
    5. Recursion in Action 00:05:41
  6. DATABASES - DATA IN ROWS AND COLUMNS
    1. How Would You Implement a Bank ATM? 00:17:39
    2. Things You Can Do with Databases - I 00:20:06
    3. Things You Can Do with Databases - II 00:08:12
    4. Interfacing with Databases from Python 00:06:46
    5. SQLite Works Right out of the Box 00:06:27
    6. Manually Downloading the ZIP Files Required 00:01:00
    7. Build a Database of Stock Movements - I 00:15:02
    8. Build a Database of Stock Movements - II 00:13:48
    9. Build a Database of Stock Movements - III 00:13:23
  7. AN OBJECT-ORIENTED STATE OF MIND
    1. Objects are Like Puppies! 00:03:46
    2. A Class is a Type of Variable 00:17:31
    3. An Interface Drives Behavior 00:13:40
  8. NATURAL LANGUAGE PROCESSING AND PYTHON
    1. Natural Language Processing with NLTK 00:07:26
    2. Natural Language Processing with NLTK - See It in Action 00:14:14
    3. Web Scraping with BeautifulSoup 00:18:09
    4. A Serious NLP Application: Text Auto-Summarization Using Python 00:11:34
    5. Auto-Summarize News Articles - I 00:18:34
    6. Auto-Summarize News Articles - II 00:11:28
    7. Auto-Summarize News Articles - III 00:10:24
  9. MACHINE LEARNING AND PYTHON
    1. Machine Learning - Jump on the Bandwagon 00:16:32
    2. Plunging In - Machine Learning Approaches to Spam Detection 00:17:01
    3. Spam Detection with Machine Learning - Continued 00:19:05
    4. News Article Classification Using K-Nearest Neighbors 00:19:29
    5. News Article Classification Using Naive Bayes 00:19:25
    6. Code Along - Scraping News Websites 00:18:51
    7. Code Along - Feature Extraction from News Articles 00:15:46
    8. Code Along - Classification with K-Nearest Neighbors 00:04:15
    9. Code Along - Classification with Naïve Bayes 00:08:08
    10. Document Distance Using TF-IDF 00:11:04
    11. News Article Clustering with K-Means & TF-IDF 00:14:32
    12. Code Along - Clustering with K-Means 00:08:32