Python programming in Boston and Iris data set

Hey, I also worked on those two datasets when I was a beginner. I can do it. We can discuss the details over chat, feel free to message me and ask any question. Let me talk about my experience now. I have been working as a Data Scientist & Machine Learning Engineer for more than one year now. Experience with: - Deep Learning: Deep Neural Networks, Convolution Neural Networks, Recurrent Neural Networks etc. - Gradient Boosting algorithms - Natural Language Processing - Unsupervised learning Some of the projects on which I worked are: - Bird Detection: Prevent collision between Birds and Wind Turbine. - Movie Recommendation website using Machine Learning + Web Development Knowledge - Machine Learning Bot that plays Tic Tac Toe at 10x10 board size. The tree size of 10x10 board is more than the number of atoms in the universe. - Age, Gender prediction by looking at the face of a person - Hair Segmentation - Image Segmentation - Facial Recognition - Potential Customer prediction - Predict if a comment is toxic or not - Predict how likely a windows machine is to be hit by malware.

Hi! I am an expert in Computer Vision and Machine learning python stack, sickit-learn, numpy,pandas,matplotlib, OpenCv and tensorflow. I did my bachelors with majors in Computer Vision and Deep Learning. I am dedicated and passionate, and will do a perfect job. I would love to work for you. I hope you will consider me for this project. Regards, Asad

I have experience in implementing ML and DL models using tenserflow, keras and scikit-learn. I have completed many projects. Some of these are as: 1. Human Activity Recognition (HAR) :- I have tried three models Support Vector Machines (SVM), K-Nearest Neighbors (k-NN) and Convolutional Neural Network (CNN) to classify six activities(Walking ,Walking Upstairs, Walking Downstairs, Sitting, Standing, Laying). The most accurate model tried is SVM with accuracy of 98.47%. 2. Brain Computer Interface(BCI) with the P300 Speller which aims for helping patients incapable to activate muscles to spell words by methods for their brain signal activities. The first classification is to identify the presence of a P300 in the electroencephalogram(EEG). The second classification is the combination of various P300 response for deciding the correct character to spell. 3. Handwritten Digit Recognition using CNN :- Implemented a CNN model using tenserflow to identify handwritten digits from the MNIST dataset. I will first discuss problem statement and your requirement properly and then I will propose an approach and estimated outcomes with you and if you will agree with that then I will code that and complete it. Your requirement and statisfaction will be my first priority. Looking forward to hearing from you. Thank You.

I am a Python Data Analytics Professional and trainer , I've gone through the Boston data a couple of time. You can proceed with further details in the chat box