BINNING | Convert Numerical variable to Categorical using Python

This video covers step by step approach of converting numerical variables to categorical using a method called binning i.e. creating the bins or perform binning using Python and more specifically using the Pandas library to perform mathematical operations to create bins. By creating the bins the efficiency of the machine learning model might increase drastically.

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Subtitles available in: Hindi, English, French

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#Binning #NumericaltoCategorical #DataScience

[TheAIUniversity]

Let me know what is the most challenging task you feel exists in Data Science Lifecycle.

[abrahammathew]

Hello Sir, When to convert continuous variable to bins and when not not to?What is the intuition for it?

[alexiarose]

thank you sir this is jst what i needed ❤ keep it up

[abhishekgadekar]

why is 5.1 classified in the bin [4-5), clearly 5.1>5!
"5.1" should be classified in [5-6) bin ryt?

[beautifulanimals]

Sir Kindly make videos on these modules which are most demanding???



Module 5
Computer vision using OpenCV and Python

Module Contents:
• Installing and configuring OpenCV
• Data types and structures
• Image types
• Image pre-processing
• Image denoising
• Manipulating pixels
• Scaling and rotating images
• Using video inputs
• Creating custom interfaces
• Thresholding
• Object detection
• Face and feature detection
• Template matching
• CNN
• Transfer Learning
• Object Detection
• Image Classification

Module 6
Natural language processing using NLTK and Python

Module Contents:
• Basic text analysis with NLTK
• Text pre-processing
• Stopword removal
• Stemming and lemmatization
• Parts of speech tagging
• Chunking
• Named entity recognition
• Wordnet with NLTK
• Text classification
• Converting word to features
• Classifying text documents using NLTK
• Integration with scikit-learn classifiers
• Gensim
• Word2vec
• Investigating data biasness using NLTK
• Twitter sentiment analysis using NLTK

Module 7
Building AI systems through Deep Learning and NVIDIA GPUs: Keras and
Tensorflow



Module Contents:
• What is Deep Learning and what are Neural Networks?
• Artificial Neural Networks (ANN) Intuition
• Building an ANN
• Evaluating Performance of an ANN
• Hands-On Exercise
• Introduction to Keras and TenserFlow
• Convolutional Neural Networks (CNN) Intuition
• Building a CNN
• Evaluating Performance of a CNN
• Hands-On Exercise
• Recurrent Neural Networks (RNN) Intuition
• Building a RNN
• Evaluating Performance of a RNN
• Hands-On Exercise
• Image Classification with DIGITS
• Object Detection with DIGITS
• Neutral Network Deployment with DIGITS and TensorRT



Module 8
Automatic speech recognition

Module Contents:
• How Speech Recognition Works – An Overview
• Picking a Python Speech Recognition Package
• Installing SpeechRecognition
• The Recognizer Class
• Working With Audio Files
• Supported File Types
• Using record() to Capture Data From a File
• Capturing Segments With offset and duration
• The Effect of Noise on Speech Recognition
• Working With Microphones
• Installing PyAudio
• The Microphone Class
• Using listen() to Capture Microphone Input
• Handling Unrecognizable Speech
• Putting It all Together: A “Guess the Word” Game