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Applying Data Preprocessing and cleaning to the data and using Machine Algorithms to find the Price of the house in a particular state.
Introducing Pytorch and argument parser to understand RGB pixel orientation and transfer the image to white and Grey format.
Digit recognition on a set of images and working with blur images using image processing. Understanding the concept of CNN and the concept of Neural Networks.
Working with the concept of Web Crawling and Python. Applying text analysis to the data retrieved and working with textblob sentiment analysis.
Working on the detailed EDA and Data preprocessing on the data which specifies the number of Bike sharing status in a particular city.
Understanding the concept of Generative Adversarial Network. Comparing it to CANs, using images of unconditioned Nano Transfer GAN, working to replace a portion of an image by imposing a portion from another image.
Working on detailed EDA and Data preprocessing to understand the data. Finding the attributes regarding forest fire risk and evaluating the possible risk.
Working with Natural Language Processing. Understanding Bidirectional Transformer and working with BERT model. Using tensorflow and BERT, we will work on how the model will ask questions to text based on the dataset.
Using Machine Learning algorithms to find out the quality of Wine from the given data. Applying preprocessing and feature engineering to understand and extract useful features.
This machine learning beginners’ project aims to predict the future price of the stock market based on previous years' data. We will understand how the forecasting works and introduce time series analysis.
Using Machine Learning algorithms to find the classification and the customers in the consumer base on the basis of Color, Gender, etc.
Introduction to LSTM and ARIMA modeling. Working with Time series analysis to forecast the data.
Understanding the behavior of a football player to know the number of goals he achieved and brief analysis on the statistics of game.
Understanding unsupervised learning and working with the concepts of clustering. Introduction to K-means clustering and classification of the data to unknown labels.
Introduction to image classification and classification of the neural network concept. Classifying between a fruit/vegetable and a vegetable/fruit using training the models.
Introducing GANs and understanding all projects on GANs such as Scagan, ImageKart, working on image to see image translation.
| Benefits | iLearnings Training | other platforms | Youtube |
|---|---|---|---|
| Free placement assistance | |||
| Direct interview invites | |||
| Guaranteed 1-month internship | |||
| Live classes+ recorded | |||
| Non-IT to IT Transitions | |||
| Google Rating 4.8 | |||
| Hands-on Experience | |||
| Post-Course Support | |||
| Additional Certifications | |||
| Scholarships and Financial Aid |
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