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Data scientists looking for their first machine learning or data science project begin by trying the handwritten digit recognition problem. The Digit Recognizer data science project makes use of the popular MNIST database of handwritten digits, taken from American Census Bureau employees. The dataset consists of already pre-processed and formatted 60,000 images of 28x28 pixel handwritten digits. With the use of image recognition techniques and a chosen machine learning algorithm, a program can be built to accurately read the handwritten digits with 95% accuracy. The accuracy rate can be higher based on the chosen machine learning algorithm,
In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.
Hive Project -Learn to write a Hive program to find the first unique URL, given 'n' number of URL's.
In this deep learning project, you will find similar images (lookalikes) using deep learning and locality sensitive hashing to find customers who are most likely to click on an ad.
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.
In this data science project, you will learn how to perform market basket analysis with the application of Apriori and FP growth algorithms based on the concept of association rule learning.
In this NLP AI application, we build the core conversational engine for a chatbot. We use the popular NLTK text classification library to achieve this.
In this deep learning project, you will learn how to build your custom OCR (optical character recognition) from scratch by using Google Tesseract and YOLO to read the text from any images.
In this machine learning resume parser example we use the popular Spacy NLP python library for OCR and text classification.
In this PySpark project, you will simulate a complex real-world data pipeline based on messaging. This project is deployed using the following tech stack - NiFi, PySpark, Hive, HDFS, Kafka, Airflow, Tableau and AWS QuickSight.
In this deep learning project, you will build your own face recognition system in Python using OpenCV and FaceNet by extracting features from an image of a person's face.