Python Programming for Beginners in Data Science
What you’ll learn
Learn just enough Python Programming to do Data Science, Machine Learning and Deep Learning
Have a good understanding of the core concepts of Python Programming
Form a good programming base to be able to apply it to Machine Learning Algorithms
Just enough Object Oriented Python as well
Requirements
None in general – This is a beginner’s course
A PC or Mac with good internet connections.
All required software (like Python executable, IDE etc) can be downloaded
Enough enthusiasm to learn the course through its quizzes and exercises.
Description
Data Science, Machine Learning, Deep Learning & AI are hot areas right now. But to learn these, for some of us programming is a bit of a problem. Not all of us are from a programming background. Or some come from a Java background and might not know Python. These days, Python is the de-facto ( almost ) programming language for Data Science. So, to fill that gap, we have created a course that covers just enough Python for you to start up and running with any of you the Machine learning algorithms you are interested in. Python Programming – Python programming is one of the core skills required for any Data Scientist. However, not all wanna-be data scientists have the required programming background let alone Python skills. This Python online training program is designed to let you start all the way from the basics. It teaches you the basic skills in python. Here are some of the topics we will discuss in the course. You don’t have to understand these topics just yet. The listing is to just give a good inventory of the topics that we will be covering in this Python course. variables, type conversions, flow control, operators & Expressions. Loops – for & while loops , nested loops, for else loopsStrings, built-in and user defined functionsData Structures – Lists, Dictionaries, Tuples, SetsObject Oriented PythonI/O, exceptionsStandard library – date/time, file I/O, math, statistics & random numbers.For any data scientist, these are the absolute essentials of python. What about Data Science & Machine Learning ? This course does NOT teach you data science or machine learning. Python is a broad purpose programming langauge. It can be used for a variety of purposes like building websites, process automation, devops, Data science etc. However, this Python programming course is designed specifically to cater to the needs of the Machine Learning or Data Science learner. By the end of this course, you will be in a good position to apply your python skills to apply to any of the Machine Learning or Data Science algorithms in Python. Who this course is not for ?Although most newbies or experienced folks will benefit from this course, it is not suitable for those experienced in Python already. those who already have some Python programming experience, but wish to learn more about its application in Data Science or Machine learning. Free PreviewWe have deliberately kept quite a number of videos for free preview. Hopefully, this will enable you to judge our Python Programming course before you take it. Either way, Udemy’s 30 day return program will hopefully help you with a refund in case you don’t like the course. However, we are absolutely positive you will like the course.
Overview
Section 1: Day 0 – Python Setup
Lecture 1 Why Python
Lecture 2 About the Course
Lecture 3 Python Setup
Lecture 4 Hello World in Python
Lecture 5 Python IDE Setup
Section 2: Day 1 – Python Basics
Lecture 6 What are Variables
Lecture 7 Variables – Types of Numbers
Lecture 8 Variables – Strings, Boolean & Reserved Keywords
Lecture 9 Variables – Quiz
Lecture 10 Variables – Recap
Lecture 11 Variables – Challenge – Discussion
Lecture 12 Type Conversion
Lecture 13 Type Conversion Quiz Discussion
Lecture 14 Arithmetic Operators
Lecture 15 Comparision Operators
Lecture 16 Operator Precedence
Lecture 17 Logical Operators
Section 3: Day 1 (contd) – Flow Control
Lecture 18 if statement
Lecture 19 python blocks
Lecture 20 nested if statement
Lecture 21 elif statement
Lecture 22 else statement
Lecture 23 flow control quiz – discussion
Lecture 24 flow control challenges – discussion
Section 4: Day 2 – Loops
Lecture 25 for loop
Lecture 26 While loop
Lecture 27 Challenge Discussion – 1
Lecture 28 Challenge Discussion – 2
Lecture 29 Challenge Discussion – 3
Lecture 30 for vs while loop
Lecture 31 Break Statement – Theory
Lecture 32 Break Statement – Program
Lecture 33 for-else statement
Lecture 34 Nested loops
Section 5: Day 3 – Strings & Functions
Lecture 35 What are Strings
Lecture 36 Sub-strings
Lecture 37 Split strings
Lecture 38 Strip strings
Lecture 39 Other String Functions
Lecture 40 Cheatsheet
Lecture 41 Challenges
Lecture 42 Python Functions
Lecture 43 Create your own Function
Lecture 44 doc string
Lecture 45 function arguments
Lecture 46 Python functions – Summary
Lecture 47 Python Built-in Functions
Lecture 48 Python Built-in functions Summary
Section 6: Day 4 – Data Structures – Lists
Lecture 49 What are Lists
Lecture 50 Challenge
Lecture 51 List Indexing and Merging
Lecture 52 List Manipulation
Lecture 53 Challenge – Average Grades v3
Lecture 54 Challenge contd.
Lecture 55 Challenge contd.
Lecture 56 Nested Lists
Lecture 57 Enumerate Lists
Lecture 58 Merge and Sort Lists
Lecture 59 List Slicing
Lecture 60 Python Dictionary
Lecture 61 get-vs-index
Lecture 62 Challenge – Vowels
Lecture 63 Dictionary access
Lecture 64 Dictionary – Key & Value objects
Lecture 65 Challenge – 1
Lecture 66 Challenge – 2
Lecture 67 Challenge – 2 ( contd)
Lecture 68 Dictionary – Deletion
Section 7: Day 5 – Data Structures (contd.)
Lecture 69 Python Tuples
Lecture 70 Python Tuples ( contd. )
Lecture 71 Python Sets
Lecture 72 Set Operations (Union, Intersection, Difference etc )
Lecture 73 Python Sets – (contd)
Lecture 74 Python Sets – Summary
Section 8: Day 6 – Object Oriented Python
Lecture 75 What is Object Oriented Python
Lecture 76 Write your first Python Class
Lecture 77 Attributes & Methods in a class
Section 9: Day 7 – I/O & Exceptions
Lecture 78 I/O – Input / Output
Lecture 79 I/O – contd.
Lecture 80 Exceptions
Section 10: Day 8 – Python Standard Library
Lecture 81 Date Object
Lecture 82 Quiz Discussion
Lecture 83 Time delta
Lecture 84 Time
Lecture 85 Date time
Lecture 86 File Operations – Read files
Lecture 87 File operations – Write & Append files
Lecture 88 File Operations – Exception Handling
Lecture 89 Math Module
Section 11: NumPy – Numeric Python
Lecture 90 What is NumPy
Lecture 91 What makes NumPy faster
Lecture 92 How to Create Arrays in NumPy
Lecture 93 How to Reshape NumPy Arrays
Lecture 94 Element wise operations in NumPy
Lecture 95 Aggregate Operations in NumPy
Lecture 96 Array Indexing in NumPy
Lecture 97 Array Slicing in NumPy
Lecture 98 Append rows/columns in NumPy
Lecture 99 Insert rows/columns in NumPy
Lecture 100 Array Manipulation in NumPy
Section 12: Pandas
Lecture 101 What is Pandas
Lecture 102 Pandas Installation & Sample file creation
Lecture 103 Dataframe Creation methods
Section 13: Frequently Asked Questions
Lecture 104 Course Completion Certificate
Section 14: Troubleshooting
Lecture 105 Jupyter command not recognized
Non-Programmers interested to learn Python as their first language,Non-Python Programmers interested in learning Python for Machine Learning and Data Science
Course Information:
Udemy | English | 14h 7m | 4.79 GB
Created by: Ajay Tech
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