Machine Learning Career Transition Program

Become a Machine Learning Engineer who can work from designing Machine Learning Solution Architecture, Data Understanding, Data Wrangling, Model Building, Model Evaluation and Model Deployment using Docker and Sagemaker.

Co-Developed by Senior AI Engineers

1000+

8Lac - 38Lac

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 ML

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 Business Case, Project Charter, Agile, Machine Learning Architecture to final Project release.

No Cost EMI

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

MOVE 

AS A 

ML

ENGINEER

Tools Covered

Data Scientist must be flexible with tools and coding. 

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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 Data Scientist's which helps learners to build knowledge from zero level to real time working confidence on Data Science Projects.

Program Syllabus

BASIC AND ADVANCED STATISTICS


  • PURPOSE OF STATISTICS
  • DESCRIPTIVE STATISTICS
  • INFERENTIAL STATISTICS
  • WHAT IS DATA?
  • DIFFERENT TYPES OF DATA
  • PROBABILITY DISTRIBUTION
  • NORMAL DISTRIBUTION
  • SKEWNESS & KURTOSIS
  • HYPOTHESIS TESTING
  • NULL HYPOTHESIS TESTING, ALTERNATE HYPOTHESIS
  • STATISTICAL TESTING
1. ANOVA TEST 2. 2-SAMPLE-T TEST 3. 2 - PROPORTION TEST 4. CHI-SQUARE TEST




PYTHON PROGRAMMING


  1. Installation of Anaconda and Setting up Google Colabs
  2. Basics of Python Programming
  3. Introduction to Data Structures
  4. Values, Variables, Functions and Librarys
  5. Various libraries in python and purpose of each library
  6. Deep Dive into List, Tuple, Set & Dictionary
  7. Selection/Conditional Branching Statements
  8. User Defined Functions
  9. Lamda Functions
  10. Iterators
  11. Loop Structures/Iterative Statements




MACHINE LEARNING ALGORITHMS PART -1


  • WHAT IS SUPERVISED LEARNING ?
  • WHAT IS UNSUPERVISED LEARNING ?
  • WHAT IS SEMI - SUPERVISED LEARNING ?
  • WHAT IS REINFORCEMENT LEARNING
  • DIFFERENCE BETWEEN MACHINE LEARNING & DEEP LEARNING.
  • DIFFERENCE BETWEEN A.I. & C.I
  • REGRESSION vs CLASSIFICATION
  • INTRODUCTION TO LINEAR REGRESSION
1. SIMPLE LINEAR REGRESSION 2. MULTIPLE LINEAR REGRESSION 3. MATHS BEHIND L.R 4. OLS 5. BASED ON SLOPE AND INTERCEPT EQUATIONS 6. GRADIENT DESCENTS
  • BUILD LINEAR REGRESSION MANUALLY
  • CODING LINEAR REGRESSION ON PYTHON
  • VALIDATION TECHNIQUES LIKE
  • R - SQUARED , MSE, RMSE, AIC, BIC E.T.C.
  • ASSUMPTIONS OF L.R
  • REAL TIME BUSINESS CASE EXPLANATION ON L.R
  • REAL TIME PROJECT - HEALTH CARE DOMAIN ON L.R
  • MATHS BEHIND LOGISTIC REGRESSION
  • BUILDING LOGISTIC REGRESSION MANUALLY
  • CODING LOGISTIC REGRESSION ON PYTHON
  • VALIDATION TECHNIQUES LIKE CONFUSION MATRIX
  • ROC, AIC E.T.C.
  • ASSUMPTIONS ON LOGISTIC REGRESSION
  • BUSINESS CASE EXPLANATION ON LOGISTIC
  • REGRESSION
  • BUSINESS CASE ON LOGISTIC REGRESSION FROM BANKING
  • K - MEANS CLUSTERING
  • H. CLUSTERING
  • MARKET SEGMENTATION USING K - MEANS CLUSTERING
  • CLUSTERING FOR DATA IMPUTATION
  • CLUSTERING IN CYBER SECURITY
  • H . CLUSTERING TO UNDERSTAND THE PURCHASE PATTERN
  • WORKING ON A REAL TIME PROJECT BASED ON ABOVE 3
  • ALGORITHMS




MACHINE LEARNING DEPLOYMENT & ARCHITECTURE


1. What are the skillset required from companies for machine learning

2. Skills evaluations for individual participants

3. Life cycle of a machine learning developer

3.1 Problem Understanding

3.2 Data collection

3.3 Data Wrangling

3.4 Choosing right algorithm

3.5 Building Model

3.6 Model Evaluation

3.7 Model Performance Improvement

3.8 Model Finalisation

3.9 Model Deployment

3.10. Model Documentation

4. Best Practices on Machine Learning

4.1 Scrum Methodology Implementation on Machine Learning

4.2 Agile CRISP Implementation on Machine Learning

5. Building Machine Learning Solution Architecture for Banking Domain Project

5.1 Data Flow Design

5.2 Data Capturing

5.3 Running Machine Learning Engine in AWS using Docker

5.4 Implementations of Jenkins for Machine Learning Deployment

6. Live Project on Machine Learning Practical

6.1 Design Business Case for Machine Learning

6.2 Designing Machine Learning Architecture

6.3 Work allocation

6.4 Data Validation

6.5 Data Understanding Phase

6.6 Domain Understanding Phase

6.7 Feature Extraction Phase

6.8 Environment Study and Algorithm Finalisation

6.9 Model Building

6.10 Model Evaluation & Deployment

6.11 Client Feedback or Stakeholder Feedback on Deployed ML Engine

6.12. Performance Improvement

6.13. Change in Tools for better deployment and better features on improvement

6.14. Machine Learning Model Release

7. How to learn different Machine Learning Tools Faster

7.1 List of tools

7.2 Google Collabs, Google ML Kit

7.3 Apache Mahout, Apache Spark

7.4 ML Deployment Eco-System Docker, Jenkins, Django, Flask

7.5. Documentation and Github Release




ADVANCED MACHINE LEARNING ALGORITHMS


  • DECISION TREE - INTERNAL MECHANISM
  • ENTROPY & GINI
  • HYPER PARAMETER TUNING
  • CONTROLLING THE OVER FITTING
  • BAGGING & BOOSTING FOR DECISION TREE AND MANUALLY BUILDING A
  • BASIC DECISION TREE FOR A SAMPLE DATA
  • HOW TO BUILD A POC ?
  • EXAMPLE POC BUILDING.
  • BUILD A POC ON DECISION TREE/RANDOM FOREST/ GRADIENT BOOSTING
  • FROM (AIRLINES INDUSTRY)
  • SUPPORT VECTOR MACHINES
  • MATHS BEHIND SUPPORT VECTOR MACHINES
  • SUPPORT VECTOR REGRESSION & SVC
  • HINGE LOSS
  • CHOOSING DECISION BOUNDARIES




ADVANCED NATURAL LANGUAGE PROCESSING


  • POS TAGGING
  • SEMANTIC ANALYSIS
  • ENTITY RECOGNITION
  • WORD CLOUD
  • LEXICAL ANALYSIS
  • SENTIMENTAL ANALYSIS
  • NAVIES BAYES FOR NLP MODEL BUILDING
  • BUILDING A PROJECTS ON NLP USING NLTK & SPACY
  • LIMITATIONS OF NAIVE BAYES OVER RNN & LSTM




PICK ONE ELECTIVE TOPIC


  • CONVOLUTION NEURAL NETWORK(IMAGE RECOGNITION)
  • DEPLOYMENT OF MACHINE LEARNING MODELS
  • BUILDING REINFORCEMENT LEARNING MODELS
  • HIDDEN MARKOV CHAINS





ANY PROFESSIONAL FROM ANY INDUSTRY WITH ANY EXPERIENCE 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 simplified. 

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 Data Scientist

Register your seat!!
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PODCASTS
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