Python for beginners using sample projects.

Python for beginners using sample projects., available at $19.99, has an average rating of 3.95, with 5 lectures, based on 24 reviews, and has 1241 subscribers.

You will learn about Python Fundamentals,Python Installation , PyCharm IDE , Running your first program , Data types , Commenting , Functions , Whitespaces and Indentation. List , Arrays , Array vs List , Index , Range , Negative Indexing , For loops , IF conditions ,PEP File IO , Casting , External libraries , classes and Objects Machine Learning basics, Vectors , Features , Labels , BOW ( Bag of words), SkLearn , Stopwords, Transform ,Fit , Zip and UnZip. Numpy,Pandas , Numpy vs Pandas , Corpus , Documents , Terms , TF , IDF , TFIDF , CSR Matrix , COO matrix and Executing Article auto tagging project. This course is ideal for individuals who are Beginners who want to Learn Python using practical project based approach. It is particularly useful for Beginners who want to Learn Python using practical project based approach.

Enroll now: Python for beginners using sample projects.

Summary

Title: Python for beginners using sample projects.

Price: $19.99

Average Rating: 3.95

Number of Lectures: 5

Number of Published Lectures: 5

Number of Curriculum Items: 5

Number of Published Curriculum Objects: 5

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Python Fundamentals,Python Installation , PyCharm IDE , Running your first program , Data types , Commenting , Functions , Whitespaces and Indentation.
List , Arrays , Array vs List , Index , Range , Negative Indexing , For loops , IF conditions ,PEP File IO , Casting , External libraries , classes and Objects
Machine Learning basics, Vectors , Features , Labels , BOW ( Bag of words), SkLearn , Stopwords, Transform ,Fit , Zip and UnZip.
Numpy,Pandas , Numpy vs Pandas , Corpus , Documents , Terms , TF , IDF , TFIDF , CSR Matrix , COO matrix and Executing Article auto tagging project.

Who Should Attend

Beginners who want to Learn Python using practical project based approach.

Target Audiences

Beginners who want to Learn Python using practical project based approach.

What’s the best way to learn any technology , by doing a PROJECT. That’s what exactly this tutorial intends to do. This course teaches Python machine learning using project based approach. Below is the full syllabus for the same. Happy Learning.

Chapter 1:- Installing Python framework and Pycharm IDE.

Chapter 2:- Creating and Running your first Python project.

Chapter 3:- Python is case-sensitive

Chapter 4:- Variables, data types, inferrence & type()

Chapter 5:- Python is a dynamic language

Chapter 6:- Comments in python

Chapter 7:- Creating function, whitespaces & indentation

Chapter 8:- Importance of new line

Chapter 9:- List in python, Index, Range & Negative Indexing

Chapter 10:- For loops and IF conditions

Chapter 11:- PEP, PEP 8, Python enhancement proposal

Chapter 12:- ELSE and ELSE IF

Chapter 13:- Array vs Python

Chapter 14:- Reading text files in Python

Chapter 15:- Casting and Loss of Data

Chapter 16:- Referencing external libararies

Chapter 17:- Applying linear regression using sklearn

Chapter 18:- Creatiing classes and objects.

Chapter 19:- What is Machine learning?

Chapter 20:- Algoritham and Training data.

Chapter 21:- Vectors.

Chapter 22:- Models in Machine Learning.

Chapter 23:- Features and Labels.

Chapter 24:- Bag of words.

Chapter 25:- Implementing BOW using SKLearn.

Chapter 26:- The fit Method.

Chapter 27:- StopWords.

Chapter 28:- The transform Method.

Chapter 29:- Zip and Unzip.

Chapter 30:- Project Article Auto tagging.

Chapter 31 :- Understanding Article auto tagging in more detail.

Chapter 32 :- Planning the code of the project.

Chapter 33 :- Looping through the files of the directory.

Chapter 34 :- Reading the file in the document collection

Chapter 35 :- Understanding Vectorizer , Document and count working.

Chapter 36 :- Calling Fit and Transform to extract Vocab and Count.

Chapter 37 :- Understanding the count and Vocab collection data.

Chapter 38 :- Count and Vocab structure complexity

Chapter 39 :- Converting CSR matrix to COO matrix

Chapter 40 :- Creating the BOW text file.

Chapter 41 :- Restricting Stop words.

Chapter 42 :- Array vs List revisited

Chapter 43 :- Referencing Numpy and Pandas

Chapter 44 :- Creating a numpy array

Chapter 45 :- Numpy Array vs Normal Python array

Chapter 46 :- Why do we need Pandas ?

Chapter 47 :- Revising Arrays vs Numpy Array vs Pandas

Chapter 47 :- Corupus / Documents, Document and Terms.

Chapter 48 :- Understanding TF

Chapter 49 :- Understanding IDF

Chapter 50 :- TF IDF.

Chapter 51 :- Performing calculations of TF IDF.

Chapter 52 :- Implementing TF IDF using SkLearn

Chapter 53 :- IDF calculation in SkLearn.

Course Curriculum

Chapter 1: Lab 1 – Basics of Python

Lecture 1: Lab 1 – Basics of Python

Chapter 2: Lab 2 – Machine Learning, Models and BOW ( Bag of words)

Lecture 1: Lab 2-What is Machine Learning, Models, BOW & other major fundamentals in Python

Chapter 3: Lab 3:- Project 1:- Article Autotagging using Bag of words

Lecture 1: Lab 3:- Project 1:- Article Autotagging using Bag of words

Chapter 4: Lab 4 – Numpy Array & Pandas.

Lecture 1: Lab 4 – Numpy Array & Pandas.

Chapter 5: Lab 5:- Importance of TF-IDF.

Lecture 1: Lab 5:- Importance of TF-IDF.

Instructors

Shivprasad Koirala
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2 stars: 0 votes
3 stars: 6 votes
4 stars: 13 votes
5 stars: 5 votes

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