Python for beginners using sample projects.

अभी खरीदें

कीमत: $19.99

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. हैप्पी लर्निंग.

अध्याय 1:- Installing Python framework and Pycharm IDE.

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

अध्याय 3:- Python is case-sensitive

अध्याय 4:- चर, data types, inferrence & प्रकार()

अध्याय 5:- Python is a dynamic language

अध्याय 6:- Comments in python

अध्याय 7:- Creating function, whitespaces & indentation

अध्याय 8:- Importance of new line

अध्याय 9:- List in python, अनुक्रमणिका, Range & Negative Indexing

अध्याय 10:- For loops and IF conditions

अध्याय 11:- जोश, जोश 8, Python enhancement proposal

अध्याय 12:- ELSE and ELSE IF

अध्याय 13:- Array vs Python

अध्याय 14:- Reading text files in Python

अध्याय 15:- Casting and Loss of Data

अध्याय 16:- Referencing external libararies

अध्याय 17:- Applying linear regression using sklearn

अध्याय 18:- Creatiing classes and objects.

अध्याय 19:- What is Machine learning?

अध्याय 20:- Algoritham and Training data.

अध्याय 21:- वैक्टर.

अध्याय 22:- Models in Machine Learning.

अध्याय 23:- Features and Labels.

अध्याय 24:- Bag of words.

अध्याय 25:- Implementing BOW using SKLearn.

अध्याय 26:- The fit Method.

अध्याय 27:- StopWords.

अध्याय 28:- The transform Method.

अध्याय 29:- Zip and Unzip.

अध्याय 30:- Project Article Auto tagging.

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

अध्याय 32 :- Planning the code of the project.

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

अध्याय 34 :- Reading the file in the document collection

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

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

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

अध्याय 38 :- Count and Vocab structure complexity

अध्याय 39 :- Converting CSR matrix to COO matrix

अध्याय 40 :- Creating the BOW text file.

अध्याय 41 :- Restricting Stop words.

अध्याय 42 :- Array vs List revisited

अध्याय 43 :- Referencing Numpy and Pandas

अध्याय 44 :- Creating a numpy array

अध्याय 45 :- Numpy Array vs Normal Python array

अध्याय 46 :- Why do we need Pandas ?

अध्याय 47 :- Revising Arrays vs Numpy Array vs Pandas

अध्याय 47 :- Corupus / क्यूए और टीम के सदस्यों की भूमिकाएं और जिम्मेदारियां, Document and Terms.

अध्याय 48 :- Understanding TF

अध्याय 49 :- Understanding IDF

अध्याय 50 :- TF IDF.

अध्याय 51 :- Performing calculations of TF IDF.

अध्याय 52 :- Implementing TF IDF using SkLearn

अध्याय 53 :- IDF calculation in SkLearn.