Python for beginners using sample projects.
This tutorial teaches Machine learning with Python from scratch using project based approach.
Python for beginners using sample projects. free download
This tutorial teaches Machine learning with Python from scratch using 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.
