Deep Learning Career Transition Program

Become a Deep Learning Engineer who can work from designing Deep Learning Solution Architecture like Feed Forward Networks, CNN's, Computer Vision Problems, Video Analytics, Audio Recognition Algorithms using LSTM's. etc. Build Deep Learning Solutions with Deployment.

Co-Developed by Senior AI Engineers

700+

11Lac - 47Lac

Online

3 Months

Learners

Avg. Salary/annum

Mode of Learning

Learning Duration

Industry Specific Projects

15+ Real Life Projects while learning

Live projects going to be provided based on your current experience & industry. 

Get eligibility to collect internship certificate for 4Months

2 Hackathon's on DL

Connect with Trainer's 24x7 help

Build one POC(Proof of Concept) from specific industry with complete solution with deployment

We believe in connecting our trainers with students via whatsapp, linkedin and instagram.

3+ Live Projects Demonstration

Starting from neural network architecture design choosing right activation function, right loss function, optimiser and choosing around GPU's etc upto deployment.

No Cost EMI

No need to worry about payment, we got you No cost EMI option. Start now!

MOVE 

AS A 

DL

ENGINEER

Tools Covered

Data Scientist must be flexible with tools and coding. 

Lead Instructor

Mr. Kanth

Data Scientist | Consultant | Podcaster | Youtuber | Mentor 

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Mr. Kanth is a Data Scientist and Six Sigma Certified. He is a Data Science Consultant for various Top-MNC's like Nokia, EY, Cognizant, BMW etc. He delivered end-to-end AI Solutions using Machine Learning and Deep Learning on Embedded Devices. Built various AI Solutions which impacted financial gains and human effort reduction. 

 

Mr. Kanth is an Orator & Mentor delivered AI, Machine Learning, Deep Learning, IOT, Industry 4.0 & Digital Twin Customised Training Programs across Dubai, Malaysia, Singapore, South Africa, Sudan etc. He delivered more than 100+ trainings on both sides like client side and vendor side. 

Q & A Session from Recently Placed Participants:

We're trying all the possible ways to make your career transition more simplified with Q & A Session from recently placed participants, to build great exposure towards real-time interviews and how every learner need to prepare for their program as well as for their interviews. We host Q & A Sessions from different learners who got placed recently. 

Q & A Sessions helping every learner to crack their Data Science Interviews or AI Interviews with a more simplified approach. 

Program Syllabus

Best in industry career transition based syllabus built by Senior AI Engineers which helps learners to build knowledge from zero level to real time working confidence on Deep Learning Projects.

Program Syllabus

INTRODUCTION TO DEEP LEARNING & FEED FORWARD


  • Feed Forward Neural Network
  • Different Optimizer
  • Different FFN - Archt
  • Hidden Layers
  • How inforamtion is flowing from one neuron to another neuron
  • Different Activation Function
  • Different Loss Function
  • Back - Propagation
  • Building Feed Forward Neural Net
  • Assignmnet on FFN
  • Evaluation of FFNN




DEEP DIVE INTO COMPUTER VISION


  • Architecture of CNN
  • Padding
  • Strides
  • Conv layer, Max Polling,
  • Maths in Convolution Neural Network
  • Diff conv matrix to choose
  • Diff max pooling sizes to choose
  • Different filter matrix in Conv
  • 2-d image features are extracted
  • 3-d feature extracted
  • Which activation to use in different layers of CNN
  • What is FC - Fully connected layer
  • VGGNEt, Stylnet, Lenet
  • Using double conv layer
  • How we see an output in CNN, Argmax
  • Assigmnet
  • Emotion Detection - Project 1
  • Project - 2 - Different Garbage classification




DEEP DIVE INTO VOICE RECOGNITION


  • Why RNN
  • Purpose of Tanh in RNN
  • Various Gates in RNN
  • Limitation of RNN
  • Build a model on RNN Network, songs or we use voices, NLP
  • LSTM's
  • Arch of LSTM Cell
  • Gates inside LSTM
  • Building a network with LSTM for Sequential Prediction - Voice Prediction
  • Bidirectional LSTM's
  • Try to submit a document - How RNNS can be used for Self-Driving Car
  • Project on LSTM - NLP /Voice




UNSUPERVISED DEEP LEARNING USING AUTOENCODERS


  • DeConv Autoencoders
  • Sparse Autoencoders
  • Encoding and Decoding
  • Autoencoders for DR
  • Project - Blurred images and need to convert them into HD Images




DEEP DIVE INTO GENETIC ALGORITHMS


  • Gene
  • Parent
  • Child
  • Mutation
  • Genetic Algorithm
  • Project - 1: Improving the model accuracy of an algorithm - FFNN by using different child's of GA




STOCHASTIC MODELS USING HMM'S


  • Stochastic Mechanism
  • Probab of Events
  • Why Markov Chains
  • Limiataion of Markov Chain
  • Project : 1 NLP Text Mining
  • Why HMM?
  • Prupose of HMM
  • Predicting the current event based on it's subevent
  • Predicting under closed state
  • Study: How Google Search Engine is working based on HMM
  • Study2 : How HMM Can be used for Self - Driving cars in Path planning & Localization
  • Project:1 HMM
  • Tensorflow Deployement
  • Tensorflow Parallel processing
  • Tensorflow Weight Saving
  • Weights importing
  • Tensorboard





PROFESSIONALS HAVING KNOWLEDGE ON MACHINE LEARNING CAN ENROLL FOR THIS PROGRAM.

YOU RECEIVE INTERNSHIP CERTIFICATE BY COMPLETING EVERY PROJECT PROVIDED.

SYLLABUS & TRAINING FORMAT DESIGNED FOR WORKING PROFESSIONALS AS WELL AS FRESHERS.

WE ASSUME OUR LEARNERS ARE FROM BANKING, BFSI, MANUFACTURING, HEALTHCARE, OIL & GAS, RETAIL ETC.

"I'm a Project Manger with 15+ years of experience in BFSI. I'm impressed with projects they provided and articulated my CV and detailing of every topic as a project manager to master like pitching, CBA, ML Design etc are greatly nailed by Mr.Kanth"

Avg. 10+ Career Transitions Every

Month

Every month we are making our students and participants from different backgrounds and different learning formats we're making them to get placed. We fix every block and hurdle to make your career transition. 

FRESHERS

Majority of freshers are confused with what to do? How to attend interviews and how to crack them? We support them end-to-end until they get placed.

EXPERIENCED

Making a career transformation with experience is difficult. But we make it possible based on your current experience as reference.

NON-PROGRAMMERS

Non-technical or non-programmers feels pretty confusing to move into data science career. But we take care of your non-programmer journey to Data Scientist.

Best in industry projects to work & to place in resume

Dedicated team to Build your resume

Mock interviews before attending live interviews

Feedback on interviews & support until getting placed as Deep Learning Engineer

Register your seat!!
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