Each project comes with 2-5 hours of micro-videos explaining the solution.
Get access to 102+ solved projects with iPython notebooks and datasets.
Add project experience to your Linkedin/Github profiles.
I have had a very positive experience. The platform is very rich in resources, and the expert was thoroughly knowledgeable on the subject matter - real world hands-on experience. I wish I had this... 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
Initially, I was unaware of how this would cater to my career needs. But when I stumbled through the reviews given on the website. I went through many of them and found them all positive. I would... 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
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
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
Start here if...
you’re new to computer vision. This data science project is the perfect introduction to techniques like neural networks using a classic dataset including pre-extracted features.
MNIST ("Modified National Institute of Standards and Technology") is the de facto “hello world” dataset of computer vision. Since its release in 1999, this classic dataset of handwritten images has served as the basis for benchmarking classification algorithms. As new machine learning techniques emerge, MNIST remains a reliable resource for researchers and learners alike.
In this data science project, our goal is to correctly identify digits from a dataset of tens of thousands of handwritten images. We encourage you to experiment with different algorithms to learn first-hand what works well and how techniques compare.
Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255, inclusive.
The training data set, (train.csv), has 785 columns. The first column, called "label", is the digit that was drawn by the user. The rest of the columns contain the pixel-values of the associated image.
Each pixel column in the training set has a name like pixelx, where x is an integer between 0 and 783, inclusive. To locate this pixel on the image, suppose that we have decomposed x as x = i * 28 + j, where i and j are integers between 0 and 27, inclusive. Then pixelx is located on row i and column j of a 28 x 28 matrix, (indexing by zero).
More details about the dataset, including algorithms that have been tried on it and their levels of success, can be found at http://yann.lecun.com/exdb/mnist/index.html. The dataset is made available under a Creative Commons Attribution-Share Alike 3.0 license.
Hive Project -Learn to write a Hive program to find the first unique URL, given 'n' number of URL's.
In this Databricks Azure tutorial project, you will use Spark Sql to analyse the movielens dataset to provide movie recommendations. As part of this you will deploy Azure data factory, data pipelines and visualise the analysis.
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.
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 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 build your own face recognition system in Python using OpenCV and FaceNet by extracting features from an image of a person's face.
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.
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 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.