Java Certification Training

In this project, the candidates will model a chatbot employing IBM Watson’s API. Watson is considered to be a prime example of what AI can help them in accomplishing. The idea behind this project was for harnessing Watson’s deep learning abilities for creating a chatbot for engaging with humans just like another human being. Chatbots are intelligent supremely and can provide solutions to questions of humans or requests in real-time. This is the major reason why an increasing amount of enterprises across every domain are adopting such chatbots in their infrastructure of customer support.

For this project, all the candidate requires is to have a python 2/3 in their machine with an internet connection and a blue mix account and they are good to go.

WaveGlow is considered to be one of the most interesting deep learning projects. It is a flow-based generative network for speech synthesis offered and developed by NVIDIA. This project can generate high-quality speech from melspectograms. It also helps in blending the insights obtained from wavenet and glow for facilitating efficient, high-quality, and fast audio synthesis without the requirement of auto-regression.

WaveGlow can also be implemented through a single network and also be trained to employ a single cost function. The main aim of the project is for optimizing the likelihood of the data training, thus making the procedure of training stable and manageable.

Image caption generator is one of the most trending projects of deep learning. It is a python based deep learning project which leverages CNN and LTSM (a type of RNN) for building a deep learning model that can generate a caption for a picture. An image caption generator merges both natural language processing techniques as well as visions of the computer for identifying the context of an image and also describing it accordingly in human languages. This project will help the candidates by strengthening their knowledge of LTSM and CNN, such that they will be able to implement them in real-world applications.

The music genre classification system is quite an intriguing and excellent project fir improving and nurturing the deep learning skills of the candidates. In this project, the candidate will be creating a deep learning model that uses neural networks for classifying the genre of music automatically. For this project, the students will be using the FMA dataset also known as Free Music Archive. It is known as an interactive library that comprises of high-quality and legal audio downloads. FMA is an easily accessible dataset which is excellent for a host of MIR tasks, which includes browsing and organizing of huge music collections.

Though, one thing the students need to keep in mind before accessing the model for classification of audio files by genres, that they need to extract the required information from the samples of audios.

For a long time now, automate image colorization of B&W pictures has been a trendy topic of exploration in computer vision and deep learning field. A recent study has stated, that if we train a set of neural networks employing a rich and voluminous dataset, then we can create a deep learning model that can hallucinate colors with a black and white image.

In this image colorization project, the aspirants will be using OpenCV DNN and Python architecture. The main moto of the project will be the creation of a colored reproduction of grayscale pictures. For this, the students would be using a NumPy and a pre-trained Caffe model, a prototxt file.

DeepMimic is known as an example-guided deep reinforcement learning of the physics-based character skills. In simple words, it is considered as a neural network trained by leveraging reinforcement learning for reproducing motion-captured movement through a simulated humanoid or any other physical agent.

The functioning of DeepMimic is quite simple. Firstly, the students need to set simulation of the thing that they wish to animate. Then they need to use the motion capture data for training a neural network through reinforcement learning. The input here is configuring the arms and legs at distinct time points as the reward is the distinction between simulation and real thing at particular time points.

A face detection system is one of the most intriguing project ideas. In this project, with the help of advanced deep learning, facial recognition technology has also been advanced increasingly. Face recognition technology is considered as a subset of object detection which focuses on perceiving the instance of semantic objects. It is constructed for visualizing and tracking the human faces without the use of digital images.

In this project, the aspirants will be learning ways to perform human recognition in real-time. for this, they need to develop the model in OpenCV and Python.

This project involves a digit recognition system which can help in the classification of digits based on the set tenets. In this project, the students will be using the MNSIT dataset inclusive of images. The project aims to create a recognition system that can classify the digits from 0 to9 making use of a combination of deep neural network and shallow network by the implementation of logistic regression. Multinomial or softmax logistic regression is the ideal choice for this one.

Detectron is known as a Facebook AI Reasrch’s (FAIR) software system constructed for executing and running state of the art object detection algorithms. This project is written in python and is based on Caffe 2 dep learning framework. It offers high performance and high-quality performance codebase for object detection research. It consists of over 50 pre-trained models and is quite flexible as it supports the evaluation and rapid implementation of novel research.

In this project of Python, the aspirants will be working with the United States SSA that is known to make data on the frequency of the names of babies from 1880 to 2016. It requires analyzation of data considering distinct methods. You will also determine the naming trends while coming up with popular names for a specific year and also visualizing the most frequent names.

In this project, the students will be working with the telecom company’s customer dataset, which includes subscribing to customer’s telephone details. Each column includes data on the number, call minutes throughout the day, lifetime duration of account charges incurred, and also if the customers have churned some services by unsubscribing it. The main aim is to forecast whether a customer will eventually churn or not.

In this project, the candidates will be getting hands-on experience with most of the algorithms of ML. They will learn ways to visualize the data for figuring out the patterns and trends among distinct factors, manipulating data for gaining useful insights, and implementation of algorithms like decision tree, linear regression, and naïve Bayes

In this project, the students will learn the trends of Covid-19 spread and check if the restrictions imposed by the government around the world have helped in curbing Covid-19, and if yes then by what degree. They will be using data science and python and perform visualizations to better understand the data. Also, the candidates will be using time series analysis for making a prediction about future cases.