All our Courses are designed in an easy-to-master approach.
Even Non-IT students find – “Learning is Fun” !!
This course will help you understand both basic & advanced level concepts of Data Analytics, Python, Machine Learning, Deep Learning, Artificial Intelligence (AI) in the field of Data Science. Professionals who don’t have good coding skill need not worry, Deep Learning, Artificial Intelligence (AI) is the most user-friendly and easy to learn technology, is used as a powerful tool in handling advanced analytics applications.
- Gyansetu trainers are well known in Industry, they are highly qualified working professionals in MNCs, having a wide experience in training industry.
- We provide interaction with faculty before the course starts.
- Our Train the Trainer approach ensures you learn proactively and come out as an expert.
- We are open seven days a week and provide 24×7 Lab Support Services.
After the completion of Course, you will be able to:
- Develop and Implement various Deep Learning Algorithms in daily practices & Live Environment.
- Build Real time Deep Learning Applications
- Implement Data Analytics models (CNN & RNN) on various Data Sets
- Data Mining across various file formats using Deep Learning models
- Building Image & Video Classifiers, Speech Analytics using Deep Learning models
- Perform various type of Analysis (Time Series, Image, Video, Audio, Face Detection & Recognition)
- Implement plotting & graphs using various Deep Learning Libraries (Tensor Flow & Keras)
- Perform Big Data Analytics using DeepLearning4j & other frameworks
- Building different Neural networks using TensorFlow, Keras, PyTorch & other Deep Learning Libraries.
Who should learn Python, Deep Learning & Artificial Intelligence (AI) ?
1. Testing professionals
2. Senior IT Professionals
3. BI /ETL/DW professionals
4. Developers and Architects
5. Mainframe professionals
Pre-requisites for Deep Learning and Artificial Intelligence Course
1) Bitcoin Historical Data Analysis :
Bitcoin is the longest running and most well known cryptocurrency, first released as open source in 2009 by the anonymous Satoshi Nakamoto.
Bitcoin serves as a decentralized medium of digital exchange, with transactions verified and recorded in a public distributed ledger (the blockchain) without the need for a trusted record
keeping authority or central intermediary. Transaction blocks contain a SHA-256 cryptographic hash of previous transaction blocks, and are thus “chained” together, serving as an immutable record of all transactions that have ever occurred.
As with any currency/commodity on the market, bitcoin trading and financial instruments soon followed public adoption of bitcoin and continue to grow. Included here is historical bitcoin market data at 1-min intervals for select bitcoin exchanges where trading takes place. Happy (data) mining!
2) Trending YouTube Video Statistics:
YouTube (the world-famous video sharing website) maintains a list of the top trending videos on the platform.
According to Variety magazine, “To determine the year’s top-trending videos, YouTube uses a combination of factors including measuring users
interactions (number of views, shares, comments and likes). Note that they’re not the most-viewed videos overall for the calendar year”.
Top performers on the YouTube trending list are music videos (such as the famously virile “Gangam Style”), celebrity and/or reality TV performances,
and the random dude-with-a-camera viral videos that YouTube is well-known for.
3) Global Terrorism Database(GTD) :
Information on more than 180,000 Terrorist Attacks
The Global Terrorism Database (GTD) is an open-source database including information on terrorist attacks around the world from 1970 through 2017.
The GTD includes systematic data on domestic as well as international terrorist incidents that have occurred during this time period and now includes more than 180,000 attacks.
The database is maintained by researchers at the National Consortium for the Study of Terrorism and Responses to Terrorism (START), headquartered at the University of Maryland.
4) Real Time Stock Market Data Analysis:
High-quality financial data is expensive to acquire and is therefore rarely shared for free.
Here I provide the full historical daily price and volume data for all US-based stocks and ETFs trading on the NYSE, NASDAQ, and NYSE MKT.
It’s one of the best datasets of its kind we can obtain.
5) TED Talks Data Analysis:
These datasets contain information about all audio-video recordings of TED Talks uploaded to the official TED.com website until September 21st, 2017.
The TED main dataset contains information about all talks including number of views, number of comments, descriptions, speakers and titles.
The TED transcripts dataset contains the transcripts for all talks available on TED.com.
6) Emojinator :
Emojis are ideograms and smileys used in electronic messages and web pages.
Emoji exist in various genres, including facial expressions, common objects, places and types of weather, and animals.
They are much like emoticons, but emoji are actual pictures instead of typographics
7) Drowsiness_Detection : This can be used by riders who tend to drive for a longer period of time that may lead to accidents
A computer vision system that can automatically detect driver drowsiness in a real-time video stream
and then play an alarm if the driver appears to be drowsy.
8) Build a deep reinforcement learning bot to play Flappy Bird : We may have played Flappy Bird sometime in the past. For those who don’t know, it was an extremely addictive Android game in which the aim was to keep flying the bird in air by avoiding obstacles.
In this application, a flappy bird Bot is created by using advanced reinforcement learning techniques. Here’s a demo of a trained bot.
9) Cred card Fraud Detection : It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.
We will design Credit Card Fraud Detection system so that we can categorize transaction ad fraud and non-fraud using DL models.
10) Web Traffic Time Series Forecasting :
This competition focuses on the problem of forecasting the future values of multiple time series,
as it has always been one of the most challenging problems in the field. More specifically,
we aim the competition at testing state-of-the-art methods designed by the participants,
on the problem of forecasting future web traffic for approximately 145,000 Wikipedia articles.
WIKI Forecast Exploration (WTF EDA) : This challenge is about predicting the future behaviour of time series’ that describe the web traffic for Wikipedia articles
11) AVITO Duplicate Ads Detection:
Online marketplaces make it a breeze for users to both find and buy unique treasures or unload their dusty record collections in the spirit of spring cleaning. As one of the world’s largest and fastest growing online classifieds,
Avito hosts high volumes of listings and competitive sellers often go to great lengths to get their wares noticed.
12) Analyze Lending CLUB Issued LOANS:
These files contain complete loan data for all loans issued through the 2007-2015, including the current loan status (Current, Late, Fully Paid, etc.) and latest payment information.
The file containing loan data through the “present” contains complete loan data for all loans issued through the previous completed calendar quarter. Additional features include credit scores, number of finance inquiries, address including zip codes, and state, and collections among others.
The file is a matrix of about 890 thousand observations and 75 variables.
13) Weather Forecasting (Time Series and similar forecasting challenges)
- a) Unvariate Time Series Forecasting
- b) Multi-variate Time Series Forecasting
- c) Demand/Load Forecasting
14) Predict Blood Donation
15) Satellite Imagery Processing for Socioeconomic Analysis
16) Music/Audio Recommendation Systems
17) Music Genre Recognition using neural networks
18) Clinical Diagnostics : Image Identification,classification and segmentation
19) WINE REVIEWS :
After watching Somm (a documentary on master sommeliers) I wondered how I could create a predictive model to identify wines through blind tasting like a master sommelier would. The first step in this journey was gathering some data to train a model. I plan to use deep learning to predict the wine variety using words in the description/review.
The model still won’t be able to taste the wine, but theoretically it could identify the wine based on a description that a sommelier could give.
If anyone has any ideas on how to accomplish this, please post them!
20) Automatic Colourization of Black and White Images
21) Automatic adding sounds to silent movies
22) Automatic Machine Translation : (Automatic translation of Text and Images)
23) Object Classification & Detection in photographs
24) Automatic Handwriting Generation
25) Character Text Generation
26) Automatic Image Caption Generation
27) Automatic Game Playing
28) Automatic Speech Recognition
29) Automatically created stylized images from rough sketches.
30) Automatically turing sketches into photos.
31) Automatic Speech Understanding
32) Automatically focus attention on objects in images.
33) Face Recognition
34) Deep Drumpf
36) Quadcopter Navigation in Forest
37) Neural Doodle
39) Neural Talk2
40) Image Analogies
42) Quarterly NewsLetter
44) Cucumber Classifier