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This is one of the best of investments you can make with regards to career progression and growth in technological knowledge. I was pointed in this direction by a mentor in the IT world who I highly... Read More
I have extensive experience in data management and data processing. Over the past few years I saw the data management technology transition into the Big Data ecosystem and I needed to follow suit. I... Read More
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This was great. The use of Jupyter was great. Prior to learning Python I was a self taught SQL user with advanced skills. I hold a Bachelors in Finance and have 5 years of business experience.. I... Read More
The objective of this machine learning project is to use binary leaf images and extracted features, including shape, margin, and texture, to accurately identify 99 species of plants. Leaves, due to their volume, prevalence, and unique characteristics, are an effective means of differentiating plant species. They also provide a fun introduction to applying techniques that involve image-based features. We are going to apply different classification techniques to benchmark the relevance of classifiers in image classification problem.
In this machine learning resume parser example we use the popular Spacy NLP python library for OCR and text classification.
Use the RACE dataset to extract a dominant topic from each document and perform LDA topic modeling in python.
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.
Given big data at taxi service (ride-hailing) i.e. OLA, you will learn multi-step time series forecasting and clustering with Mini-Batch K-means Algorithm on geospatial data to predict future ride requests for a particular region at a given time.
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.
Estimating churners before they discontinue using a product or service is extremely important. In this ML project, you will develop a churn prediction model in telecom to predict customers who are most likely subject to churn.
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.
Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.
In this Deep Learning Project on Image Segmentation Python, you will learn how to implement the Mask R-CNN model for early fire detection.
Hive Project -Learn to write a Hive program to find the first unique URL, given 'n' number of URL's.