Python for beginners using sample projects.

Python for beginners using sample projects.

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.

This tutorial teaches Machine learning with Python from scratch using project based approach.

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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.

Rating: 3.9

Level: Beginner Level

Duration: 2.5 hours

Instructor: Shivprasad Koirala