Perceptron:

Perceptron is a single layer neural network and a multi-layer perceptron is called Neural Networks. Perceptron is usually used to classify the data into two parts. Therefore, it is also known as a Linear Binary Classifier.

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  1. Initialize the weights to 0 or small random numbers.
  2. For each training sample x(i):
  • Calculate the output value.(All the inputs x are multiplied with their weights w.Add all the multiplied values and call them Weighted Sum.Apply that weighted sum to the correct Activation Function).
  • Update the weights.

Ensemble methods is a machine learning technique that combines several base models in order to produce one optimal predictive model.

The fundamental principle of the ensemble model is that a group of weak learners come together to form a strong learner, which increases the accuracy of the model.

  1. Parallel Ensemble Learning(bagging)

2. Sequential Ensembling Learning(boosting):

Parallel Ensemble Learning(bagging)

Algorithms : Random Forest, Bagged Decision Trees, Extra Trees

a. Max Voting :

The max voting method is generally used for classification problems. In this technique, multiple models are used to make predictions for each data point. The predictions by each model…


Source code: https://github.com/Uttam580/chatterbot_chatbot

What are chatbot

A chatbot is an artificial intelligence-powered piece of software in a device (Siri, Alexa, Google Assistant etc), application, website or other networks that try to gauge consumer’s needs and then assist them to perform a particular task like a commercial transaction, hotel booking, form submission etc .

Today almost every company has a chatbot deployed to engage with the users. Some of the ways in which companies are using chatbots are:

* To deliver flight information

* to connect customers and their finances

* As customer support. The possibilities are (almost) limitless.

quick demo:

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chatterbot chatbot


Git repo: https://github.com/Uttam580/Honey_Bees_Classifier

Objective:

In this project, the objective is to predict strength and health of honey bees. frequent check-ups on the hive are time-consuming and disruptive to the bees’ workflow and hive in general. By understanding the bees we can understand hive itself.

* How can we improve our understanding of a hive through images of bees?

* How can we expedite the hive checkup process?

* How can bee image data help us recognize problems earlier?

* How can bee image data help us save our bees?

quick demo

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honey bee classifier

Datasset:

Dataset with adnotated images of bees from various…


Git : https://github.com/Uttam580/diabetes_prediction_api

In this project, the objective is to predict whether the person has Diabetes or not based on various features like Glucose level, Insulin, Age, BMI. We will use the Pima Indians dataset from the UCI Machine learning repository.

we can predict diabetes from two ways.

  1. User will fill the data after that prediction will be displayed over UI.

2. User can upload csv file with required features and then get downloadable predicted csv file.

quick demo:

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Description of variables in the dataset:

Pregnancies: Number of times pregnant

Glucose: Plasma glucose concentration a 2 hours in an oral…


Git Link : https://github.com/Uttam580/Traffic_Signal_Detection

Traffic Signal Detection UI to detect traffic signal and integrating with Flask.

Data has downloded form kaggle .Use Below link to download the dataset.

Dataset Link: GTSRB Data

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It conatains three files.

  1. Meta data : Meta data conatins diff- diff type of signal images used in Training the model. It has 43 types of traffic signal data so almot we have 86k images to train.
  2. Train
  3. Test data.

Technical Aspect

  1. Training a deep learning model using tensorflow. I trained model on local system using NVIDIA GEFORCE GTX 1650 for batch size 32 , epoch 20 and I had…

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