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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
I have 11 years of experience and work with IBM. My domain is Travel, Hospitality and Banking - both sectors process lots of data. The way the projects were set up and the mentors' explanation was... Read More
The project orientation is very much unique and it helps to understand the real time scenarios most of the industries are dealing with. And there is no limit, one can go through as many projects... Read More
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 came to the platform with no experience and now I am knowledgeable in Machine Learning with Python. No easy thing I must say, the sessions are challenging and go to the depths. I looked at graduate... Read More
I have worked for more than 15 years in Java and J2EE and have recently developed an interest in Big Data technologies and Machine learning due to a big need at my workspace. I was referred here by a... Read More
According to Wikipedia, Statistics is a branch of mathematics dealing with data collection, organization, analysis, interpretation and presentation. It is about building from collected data, a model that can enable humans to describe, analyze and infer event happening around. Statistics is in itself a conduit to the field of Machine Learning and AI.
In this Hackerday, we will go through the basis of statistics and see how Spark enables us to perform statistical operations like descriptive and inferential statistics over the very large dataset.
No knowledge of statistics is assumed in this session. Every concept will be discussed ground up and put to practice on the airline on-time performance dataset. We will conclude the session by introducing a number of machine learning algorithms available in MLlib.
In this Deep Learning Project on Image Segmentation Python, you will learn how to implement the Mask R-CNN model for early fire detection.
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.
In this time series project, you will forecast Walmart sales over time using the powerful, fast, and flexible time series forecasting library Greykite that helps automate time series problems.
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.
Use the Zillow dataset to follow a test-driven approach and build a regression machine learning model to predict the price of the house based on other variables.
In this machine learning resume parser example we use the popular Spacy NLP python library for OCR and text classification.
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 NLP AI application, we build the core conversational engine for a chatbot. We use the popular NLTK text classification library to achieve this.
Learn to design Hadoop Architecture and understand how to store data using data acquisition tools in Hadoop.
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.