Python for beginners using sample projects.
This tutorial teaches Machine learning with Python from scratch using project based approach.
Ratings 4.03 / 5.00
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.
Description
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.
Who Should Attend!
- Beginners who want to Learn Python using practical project based approach.
Tags
- Data Science
- Machine Learning
- Python
Subscribers
1241
Lectures
5
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