The Enroute to AI Career Book written by Mr Kanth(HardCopy) - Unexposed side of an AI Career
#1 Data Science Career Transition Program
"Goal is to get job as Data Scientist & build great career with great pay in Data Science & AI is pretty important"
Co-Developed by Senior Data Scientist's
4.8
Google Reviews
15000+
4Lac - 48Lac
Online
6 Months
20th May, 2021
Learners
Avg. Salary/annum
Mode of Learning
Learning + Internship
Next Batch starts from
"Customised Career transition roadmap based on your background, challenges and previous experience"
I'M FRESHER, I'M HAVING DIFFERENT EDUCATIONAL BACKGROUND, I'M HAVING GAP IN MY CAREER, CAN I GET PLACED AS DATA SCIENTIST?
HAVING EXPERIENCE IN OTHER DOMAINS, CAN I GET PLACED AS DATA SCIENTIST WITH BETTER SALARY THAN MY CTC
FROM NON-PROGRAMMING BACKGROUND, NON-TECHNICAL BACKGROUND, CAN I GET PLACED?
I COMPLETED COURSE FROM OTHER INSTITUTES BUT NO JOB, I'M IN NOTICE PERIOD CAN I GET PLACED AS DATA SCIENTIST?
"YOU ARE THE PRODUCT AND COMPANIES ARE BUYERS, THEY ARE IN NEED OF BUYING RIGHT PRODUCT. BUT ARE YOU MEETING COMPANIES REQUIREMENT TO GET HIRED"
Are you the right product?
Industry Specific Projects
40+ 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
5 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.
10+ 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
DATA
SCIENTIST
Choose wisely between two?
"Thousands of people are learning Data Science/AI/ML, What making you Different to hire you?
We make you unique with proper Unique selling points and real-time projects with better problem solving approach"
Listen to Data Science Interview Preparation Podcast
Listen to the entire podcast on "How to prepare for Data Science/AI/ML Interviews?" Checkout our podcasts on Spotify, iTunes, Google Podcasts e.t.c. and listen to our podcast with Data Scientists.
Tools Covered
Data Scientist/AI Engineer must be flexible with tools and coding.
.png)
KANTH
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.
Lead Instructor
Mr. Kanth
Data Scientist | Consultant | Podcaster | Youtuber | Mentor
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.
Are you struggling with career transition into Data Science/AI/ML? Wasted time & money??
Enroll to our #1 Data Science Career Transition Program
Book your Application on Data Science Career Transition Program
Our team going to call you and review your application for Data Science CT Program
Our mentor going to call you & design your customised roadmap on Data Science
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
Introduction to Life cycle of a Data Scientist
-
Introduction Data Science Terminologies like Data Science, Machine Learning, Deep Learning, AI & Data Mining
-
Lifecycle of Data Scientist and Topic Allocation across the lifecycle & Agenda Plan Deliverables across each phase
Phase-1: Mastering Python
- Goal Behind Python Mastering, Deliverables expected from every learner from Python
-
Basics of Python Like Install and setup, What is value, variable, function & libraries? What is an IDE?
-
Overview into Data Structures like list, tuple, set, dict, Series, Data Frame, Array -
Practice on List, Tuple, Set, Dict, Series, Data Frame & Array
-
Deep Dive into List & Tuple
-
Practice on List & Tuple
-
Deep Dive into Set & Dict
-
Explanation on: User Defined Functions, Escape Seq, For Loop, While Loop, Selection Statements, Iterations, Python Modules(Submission Date)
-
Assignment Working and Submission(Programming Live Tasks) -
Live Hackathon - 1 & Solution Discussion
-
Introduction to OOPS, OOPs vs POP, Types of OOPs, Types of Variables
-
Types of Methods in OOPS, Practical Implementation of all the different types of OOPS
-
Introduction to Pandas and Basic Pandas Commands
-
Practice on 10Mins to Pandas(Pandas Task)
-
Numpy Commands
-
Data Visualization Theory
-
Practice on Data Visualization with Matplotlib
-
Practice on Data Visualisation with Seaborn & Plotly
-
Data cleaning using Pandas
-
Data Cleaning using Sklearn, Label Encoding, One-Hot Encoding, Imputer
-
Assignment-1 on Data Cleaning
-
Assignment-2 on Data Cleaning
-
Introduction to Basics of Tableau like different charts & various functionalities, Filters, Actions, Maps, Dashboards & Storytelling
-
Storing Data into SQL & Integrating with Tableau & Generating Tableau Dashboard and Serving them into Tableau Server(Live Business Project) -
Interview Question on Python
MySQL Tool
- Database Basics
-
Designing your Database -
Data Types -
Creating Databases and Tables -
Querying Table Data -
Modifying Table Data -
Functions -
Joining Tables
Phase-2: Statistics, Probability, Time Series, Advanced ML
-
What is Data? Properties of Data like Uniform and Non-Uniform? What is Random Variable & Types of Random Variable like Continuous & Discrete Data.
-
Different Types of Analytics like Descriptive Analytics, Predictive Analytics & Prescriptive Analytics.
-
What is Sample vs the Population? Descriptive Statistics like Mean, Median, Mode, Standard Deviation Variance, Range, Quantiles, Outlier Identification
-
Normal Distribution, Skewness & Kurtosis -
Coding to check normality, skewness & kurtosis & CL
-
Understanding the concept of Correlation & Covariance, Probability theory & Different types of Probability and Probability Distributions
-
Coding Work on Above Topics and Doubts Clarification -
Working on Data Understanding Project with different Probability distribution coding and drawing insights out of business problems & Doubts clarification
-
Hypothesis Testing, Statistical Testing using ANOVA, 2 Sample, P-Value, Chi-Square Test, F-Statistics, Confidence Interval, Estimates
-
What is Supervised, Unsupervised, Self-Supervised & Reinforcement Learning?
-
Coding work on Hypothesis testing and different statistical testing & Doubts clarification
-
Associate Rules, Recommendation Engine, Apriori Algorithm, K-Means Clustering
-
Project Demonstration: Solving Real-Time UseCases with Unsupervised Learning
-
Live Project Work -1
-
Time Series Forecasting using AR/MA/ARIMA/ARMA/ SARIMA/Exponential Smoothing, Markov chains, Stationarity, Trend, Cyclic, e.t.c.
-
Project Demonstration: Solving Real-Time Usecases with Time Series Forecasting
-
Live Project Work-2
-
Introduction to Regression vs Classification Algorithms Linear Regression Assumptions, Math behind OLS & Why Linear Regression, Evaluation of Linear Regression, Feature Scaling & Feature Transformation Techniques
-
Linear Regression Maths & Scripting
-
Linear Regression Accuracy Improving -
Techniques ,Coding & how we convey the results to the client after constructing the model, Z-Values, Point Estimate, Interval Estimate, Margin of Error, Central Limit theorem, Normalization, VIF, RFE, Forward Addition, Backward Elimination.
-
Different Feature Engineering & Feature Selection Techniques which we use it based on Project
-
Practice on Linear Regression End - to - End
-
Regularisation of Linear Regression using Lasso Regression,Ridge Regression & Elastic Net
-
Linear Regression Doubt Clarification Session & Evaluation
-
Logistic Regression Math, Confusion Matrix, Classification Report, ROC Curve, AUC Curve, Variance bias tradeoff point, Odds Ratio
-
Project Demonstration on Classification Models
-
Live Project Work-3
-
Decision Tree(ID3, C4.5, CART, Greedy), Random Forest, Gradient Boosting, Ada Boosting, Hyperparameter Tuning, Grid Search
-
Coding on Hyperparameter tuning & Doubts Clarification Session
-
End-to-End Model Deployment using Heroku, Serialization, Github, Writing backend, connecting with frontend and constructing POC
-
SVM & KNN Math Deep Dive with various Regualization concepts in SVM and importance of SVM, Hinge Loss, Different Kernals.
-
Construction POC with Valid Business Usecase
-
NLP & Naive Bayes
-
Live Project Work-4
-
Dask for parallel computing problems
-
Cloud based ML Pipeline using Azure/AWS Sagemaker
-
Auto ML for easy ML Implementations
-
Devops Tool Chain & ML Ops
-
Deployment & Streamlit on ML & DL Models
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
Phase-3: Interview Preparation Phase
-
Interview Pattern of Data Science& AI Interviews
-
Most frequently asked Interview Questions with Answers & How to explain in interviews
-
Most Frequently asked Python Interview Questions & Coding Tests & How to explain in interviews
-
Most Frequently asked Statistics Interview Questions & How to explain in interviews
-
Most Frequently asked ML/DL Interview Questions & How to explain in Interviews
-
Most Frequently asked NLP Interview Questions & How to explain in interviews
-
Preparing Data Scientist/AI Resume with Proper Roles & Responsibilities, Analytical skills & live Projects
-
How to explain about your team members, project methodology, project lifecycle e.t.c -
How to speak in interviews based on the job description -
How to explain tell me about project which you worked? Live Explanation over the video call to understand confidence and e.t.c
-
Interview Etiquette to follow as Data Scientist/AI Engineer
-
Resume Marketing, Linkedin Marketing, Keywords optimisation, job profile optimisation e.t.c.