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Python Crash Course

Get a solid Python background for your career: NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit-Learn, ML, Web Scraping

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4 (20 ratings)
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This course includes

36 h 56 min of video content
Beginner Difficulty
Perpetual Access
Access on mobile and Tablet
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Learning Objectives

Have A Solid Python Background For Your Career.
Be Able To Use Python For Data Science
Be Able To Use Python For Data Analysis
Be Able To Use Python For Business Analysis
Learn Industry Level Ways Of Debugging Python Code
Become an Industry-Level Ready Python Programmer!!
Have The Skills And Understanding Of Python To Confidently Apply For Python Programming Jobs.
Learn Advanced Machine Learning Concepts Such As: Linear Regression, Logistic Regression, KNN, Naive Bayes, Support Vector Machine, etc.
Build and Deploy Machine Learning Models With Flask
Learn From Professionals Who Use Python In The Industry.
Learn To Use Robust Python Libraries And Frameworks Like: NumPy, Pandas, Scikit-Learn, etc.
Learn To Use Robust And Modern Data Visualisation Libraries And Frameworks Like: Seaborn, Matplotlib, Plotly, ggplot, etc.
Learn Modern Web Scraping Frameworks like Selenium, Scrapy, Beautiful Soup, Request, Web drivers, etc
Build PROJECTS And PORTFOLIO With Python
Learn How To Create Your Own Python Programs.
Crack Python Interview Questions With Hands-On Experience.

Table of Contents

295 lectures
36 h 56 min
Introduction
13:57
Install and Write your first Python code
14:19
Getting Started With Anaconda
04:10
Introduction To Jupyter Notebook: Code Vs Markdown Vs Raw
05:00
Introduction To Jupyter Notebook: Working With Text
02:51
Introduction To Jupyter Notebook: Working With Code(Saving and Exporting Notebook)
03:24
Introduction To Google Colab: Overview
04:48
Introduction To Google Colab: Working With Text
04:48
Introduction To Google Colab: Saving and Exporting Notebook
03:00
Python Hands-On: Introduction
01:00
Hands-On With Python: Keywords And Identifiers
12:53
Hands-On Code: Python Comments
07:09
Hands-On Coding: Python Docstring
03:25
Hands-On Coding: Python Variables
09:03
Hands-On Coding: Rules and Naming Conventions for Python Variables
07:39
Hands-On Coding: Output() Function In Python
02:29
Hands-On Coding: Input() Function In Python
07:56
Hands-On Coding: Import() Function In Python
04:52
Hands-On Coding: Python Operators-Arithmetic Operators
02:13
Hands-On Coding: Python Operators: Comparison Operators
01:53
Hands-On Coding: Python Operators: Logical Operators
07:38
Hands-On Coding: Python Operators-Bitwise Operators
07:51
Hands-On Coding: Python Operators-Assignment Operators
03:34
Python Hands-On: Python Operators-Special Operators
02:00
Hands-On Coding: Python Operators-Membership Operators
02:51
If Statement
04:20
If...Else Statement
02:11
ELif Statement
06:02
For loop
03:13
While loop
05:16
Break Statement
03:22
Continue Statement
03:56
User Define Functions
12:46
Arbitrary Arguments
05:13
Function With Loops
02:12
Lambda Function
08:43
Built-In Function
06:40
Python Files
07:58
The Close Method
01:19
The With Statement
02:30
Writing To A File In Python
07:22
Python Modules
06:23
Renaming Modules
01:28
The from...import Statement
02:09
Python Packages and Libraries
04:55
PIP Install Python Libraries
06:35
Lesson 1: Integer and Floating Point Numbers
03:46
Lesson 2: Complex Numbers and Strings
03:50
Lesson 3: LIST
02:38
Lesson 4: Tuple and List Mutability
04:59
Lesson 5: Tuple Immutability
03:25
Lesson 6: Set
02:53
Lesson 7: Dictionary
04:58
LIST
09:45
Working On List
07:09
Splitting Function
10:44
Range In Python
09:06
List Comprehension In Python
06:16
Introduction To Numpy
11:30
Numpy: Creating Multi-Dimensional Arrays
01:54
Numpy: Arange Function
05:53
Numpy: Zeros, Ones and Eye functions
04:46
Numpy: Reshape Function
01:23
Numpy: Linspace
02:23
Numpy: Resize Function
05:23
Numpy:Generating Random Values With random.rand
03:04
Numpy:Generating Random Values With random.randn
02:26
Numpy:Generating Random Values With random.randint
03:40
Numpy: Indexing and Slicing
17:00
Numpy: Broadcasting
01:17
Numpy: How To Create A Copy Dataset
04:28
Numpy: DataFrame Introduction
15:25
Numpy: Creating Matrix
05:39
Pandas: Series 1
19:21
Pandas: Series 2
11:06
Pandas: Loc and iLoc
07:48
Pandas: DataFrame Introduction
04:17
Pandas: Operations On Pandas DataFrame
09:10
Pandas: Selection And Indexing On Pandas DataFrame
03:12
Pandas: Reading A Dataset Into Pandas DataFrame
08:32
Pandas: Adding A Column To Pandas DataFrame
04:33
Pandas: How To Drop Columns And Rows In Pandas DataFrame
11:03
Pandas: How To Reset Index In Pandas Dataframe
03:32
Pandas: How To Rename A Column In Pandas Dataframe
06:29
Pandas: Tail(), Column and Index
02:56
Pandas: How To Check For Missing Values or Null Values(isnull() Vs Isna())
06:16
Pandas: Pandas Describe Function
05:40
Pandas: Conditional Selection With Pandas
09:14
Pandas: How To Deal With Null Values
07:14
Pandas: How To Sort Values In Pandas
03:10
Pandas: Pandas Groupby
00:37
Pandas: Count() and Value_Count()
02:14
Pandas: Concatenate Function
06:48
Pandas: Join and Merge(Creating Dataset)
03:45
Pandas:Join
09:49
Pandas: Merge
07:55
Introduction
04:59
Matplotlib
02:54
Seaborn
02:58
Plotly
03:33
Plotly: Display Output In Google Colab
03:38
Altair
03:57
ggplot
03:55
Bar Graph: Dataset Overview
02:55
Plotting Bar Graphs
04:09
Horizontal Bar Graphs
01:25
Stacked Bar Graphs
02:22
Group Bar Graphs
01:08
Histogram
02:44
Marginal Histogram
01:41
Facet Histogram
02:04
Distplot
03:34
Bivariate and Multivariate Distplot
03:10
Hexplot
01:55
Scatter Plot
02:53
Relplot
02:36
LM Plot
01:40
Tree Plot
04:18
Pie Chart
02:34
Heat Map
04:21
Scatter Matrix
02:22
Sunburst
02:55
Sunburst Interactive Plotting 1
04:59
Sunburst Interactive Plotting 2
02:01
Funnel Plot
02:52
Bubble Plot
04:45
Box Plot
04:37
Violin Plot
02:04
Wind Rose Chart
02:39
Word Cloud: Overview Of Dataset
02:14
Creating WordCloud
04:20
Folium: Overview Of Dataset
02:06
Folium Plot
04:59
Time Series Plot
03:48
Multi-Variable Time Series Plot
04:13
3D Plots With Matplotlib
03:11
3D Plots With Plotly
04:05
3D Plots With Plotly Output
02:09
Animated Plot With Plotly
03:13
Animated Plot With Matplotlib: COVID 19 Dataset
02:29
Animated Plot With Matplotlib: Code Walk Through 1
02:03
Animated Plot With Matplotlib: Code Walk Through 2
03:56
Animated Plot With Matplotlib: Code Walk Through 3
02:03
Animated Plot With Matplotlib: Code Walk Through 4
04:45
Animated Plot With Matplotlib: Final Output
04:49
Python Error Handling Introduction
02:42
Python Syntax Errors
06:05
How To Properly Read and Understand Errors In Python
02:35
Python Common Syntax Errors
03:15
Misspelling, Missing and Misusing Python Keywords
10:20
Missing Parentheses, Brackets and Quotes
04:43
Indentation Error
02:32
Python Runtime Error
04:10
Zero Division Error
03:30
Searching On Stack Overflow
02:50
Operator Errors In Python
07:40
Logical Errors In Python
05:57
Python IndexError
02:14
Python ModuleNotFoundError
01:38
Python KeyError
02:53
Python ImportError
01:50
Python ValueError
01:16
Python KeyboardInterrupt
02:32
Exception Handling: Introduction
04:59
Raising An Exception In Python
04:54
Raising An AssertionError In Python
03:23
Handling Exceptions With Try and Except
10:29
The Else Clause
03:35
The Finally Clause
05:03
Introduction
04:22
How To Extract Day, Month and Year from a given Time
02:14
How to Extract Hours, Minutes, Seconds and Micro-seconds from a given Time
02:03
How To Update current Date
01:44
Working With TimeDelta in Python
06:11
How To Extract Week-Day From A Given Date 1
04:05
How To Extract Week-Day From A Given Date 2
03:06
How To Generate Calendar
02:06
How To Format Date and Time in Python
01:40
Date and Time Formatting Using STRFTIME and STRPTIME
04:48
How To Extract The Year, Month, Day Time Using STRFTIME
01:50
How To Work With Timestamp
04:22
How To Convert Strings To DateTime Using STRPTIME
01:20
How To Handle Different Time Zones
06:58
DataFrame: Get Year, Month and Day from a DataFrame 1
02:38
DataFrame: Get Year, Month and Day from a DataFrame 2
04:55
DataFrame: How to Get The Week and Leap Year from DataFrame
04:57
DataFrame: How to Get Age from Date
02:48
Operations on Date and Time with Dataset 1
03:36
Operations on Date and Time with Dataset 2
04:04
Operations on Date and Time with Dataset 3
02:40
PART 1: Python Tips and Tricks
17:55
PART 2: Python Tips and Tricks
24:15
Introduction
13:20
Data Cleaning
05:05
Drawing Insights From Data
07:53
Time Series Analysis
05:02
Daily Order Analysis
04:52
Monthly Order Analysis
04:09
Linkedin Connection Timeline Analysis with Python
13:19
Linkedin Connections Company Analysis with Python
16:33
Linkedin Connections Position Analysis with Python
09:15
Linkedin Connections Position Analysis(WordCloud) with Python
07:22
Web Scraping Introduction
01:31
Library: Requests
01:55
Library: BeautifulSoup
01:00
Library: Selenium
01:11
Library: Scrapy
01:35
PART 1: Book Store Web Scrapping
06:04
PART 2: Book Store Web Scrapping
22:51
PART 3: Book Store Web Scrapping
10:12
PART 4: Book Store Web Scrapping
16:30
Part 1: Building Amazon Web Scraper
00:54
PART 2 Building Amazon Auto Scraper
14:44
Building Amazon Auto Scraper
27:52
Introduction
26:43
Applications Of A.I/Machine Learning
19:15
Supervised Machine Learning
38:46
Difference between Regression And Correlation
08:43
Linear Regression
26:05
Lab Session 1: Exploratory Data Analysis(EDA)
37:32
Lab Session 2: EDA| Dealing With Categorical And Missing Values
35:15
Lab Session 3: Building Linear Regression Model
37:13
Introduction 1
49:04
Introduction 2
22:52
Lab Session 1: Exploratory Data Analysis(EDA)
23:34
Lab Session 2: Exploratory Data Analysis(EDA)
28:39
Lab Session 3: Building Logistic Regression Model
21:13
Introduction
13:31
KNN Distance Measures
21:06
Lab Session
26:39
Lab Session: Building KNN Model
17:09
Choosing K In K-NN
17:45
Introduction
25:04
Dealing With Linearly Inseparable Points
11:53
Lab Session 1: Exploratory Data Analysis(EDA)
09:44
Lab Session 2: Building a Support Vector Machine (SVM) Model
19:10
What Is K-Means Clustering?
14:32
Lab Session 1: Exploratory Data Analysis(EDA)
44:35
Choosing K in K-Means-The Elbow Method
12:48
Introduction
26:35
Dendrograms And Cophenetic correlation
11:46
Lab Session
42:29
Flask Introduction
02:37
Create Your First Flask App
13:25
Create Your First Flask App: Linking HTML File
18:04
Create Your First Flask App: Linking CSS File
12:08
Flask Introduction
03:09
Introduction to Dataset
07:51
Exploratory Data Analysis (EDA)
47:03
Model Building
23:00
Hands-On With Flask
11:23
Creating The Necessary Folders
23:49
Creating Folder Contents
16:19
Final Deployment
17:58

Description

According to StackOverFow report 2020, Python is the most preferred programming language by industry professionals. The report also emphasis that employers prefer Python programmers and are willing to pay them more than any other programming language professionals

The Harvard Business Review also labelled Data Science and Data Analysis as the Sexiest job of the 21st Century. So imagine if you combine the two(i.e. Python + Data). In my experience at Microsoft working as a Data Scientist, I can testify how software developers, data scientist or data analyst who use Python, become more efficient than others who use other programming languages.

Python is also tagged as the most easiest programming language to learn.

Although, Python has all the attributes of what a good programming language is, if you don't get to learn it the right way, it can be difficult and boring just like the other programming languages.

In this course, you will learn Python from scratch, right from what is Python till you master the concepts as a professional Python Programmer.

You will gain industry level Python skills needed for Data Analysis, Data Science, Business Analysis, Web development or Machine learning.

This course aims to teach you from beginner level to Python Professional level. I have worked in several companies including Microsoft, Deloitte, and Synacor where I use Python to work on Data, so I know what it takes to be good in Python to land a decent job.

There are lots of exercises, assignments and projects to get you hands-on and comfortable in programming in Python.

This course has been designed and structured inn the following way:

  • Get introduced to Python
  • Know the various versions of Python
  • Set up your coding environment to start coding
  • Learn Jupyter Notebook
  • Learn Anaconda
  • Start to get hands-on by coding in Python
  • Learn the various Operators in Python
  • Learn the various Flow Controls in Python
  • Learn how to handle and debug errors in Python
  • Learn Python Modules
  • Learn Python Packages and Libraries
  • Get to know
  • NumPy,
  • Pandas,
  • Seaborn,
  • Matplotlib,
  • Plotly,
  • Scikit-Learn, etc
  • Learn Modern Web Scraping Frameworks like
  • Selenium,
  • Scrapy,
  • Beautiful Soup,
  • Request,
  • Web drivers, etc
  • Build awesome Python Projects
  • Learn more Advanced Machine Learning concepts like
  • Linear Regression,
  • Logistic Regression,
  • KNN,
  • Naive Bayes,
  • Support Vector Machine, etc
  • Build awesome Machine Learning Projects
  • Learn Flask
  • Create Flask API's to deploy Machine Learning models live !!

Feel free to check the course curriculum to verify by yourself.

By the end of this course, you will be much confident in programming in Python like a professionals.

Who this course is for:

  • Beginners who have never programmed before.
  • People Interested In Learning Python For Data Science, Data Analysis, Business Analysis, Machine Learning, Artificial Intelligence, Web Development etc.
  • Programmers switching languages to Python.
  • If you are interested in learning to code in Python from scratch through building fun and useful projects, then this course is for you!.
  • Intermediate Python programmers who want to level up their skills!
  • Featured Reviews

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    Sep 09, 2021

    Simi Dijith

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    The instructor had explained very well, from the intro to the deployment in this course, with some real time projects. It was a good experience.
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    Sep 20, 2021

    Chintamani Modak

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    Very good course. Finding this very interesting as i have started going through this training.