PROFESSIONAL ARTIFICIAL INTELLIGENCE CERTIFICATE PROGRAM
Become an expert in the exciting new world of AI & Machine Learning,
get trained in cutting edge technologies and work on real-life industry grade projects.
How it Works?
Since there are limited number of seats, we start with a screening process to test some basic skills, fundamentals. If you have intense eagerness to learn.
Once selected, we begin our 8-week intense training program. During training students start from Introduction of Data Science and will dive deep into more advance topics and get paired up with industry experts from top companies
We provide 100% placement assistance, where we prepare our student for interviews, write resumes, negotiate salaries and get introductions directly from top companies & start ups.
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Training Key Features
178 hours of self-paced sessions with lifetime access
146 hours of highly interactive instructor-led training
Exercises and Project Work(s)
286 hours of real-time projects after every module
Lifetime access and free upgrade to the latest version
Attend multiple batches for lifetime and stay updated
Global industry-recognized certifications
Job assistance through 80+ corporate tie-ups
24/7 lifetime technical support and query resolution
Get on the path to an exciting, evolving career that is predicted to grow sharply into 2025 and beyond. Artificial Intelligence and Machine Learning will impact all segments of daily life by 2025, with applications in a wide range of industries such as healthcare, transportation, retail, insurance, transport and logistics and customer service.
The executive programme in Artificial Intelligence and Machine Learning will help you become an expert in the exciting new world of AI & ML by learning through cutting edge technologies and work on real-life industry grade projects.
The programme is designed for executives who would like to participate in Kaggle Data Science challenges or look for data-heavy context to conduct analysis, produce insights, and deploy a system that does useful work in the competitive environment.
Artificial Intelligence (AI) and Machine Learning (ML) have a rapidly growing presence in today’s world, with applications ranging from heavy industry to education. From streamlining operations to informing better decision making, it has become clear that this technology has the potential to truly revolutionize how the everyday world works.
AI, Machine Learning and Deep Learning are attracting more traction lately because of the recent innovations that have made headlines, Alexa, Siri, Humanoids, Chatbots, Robotics are some to name a few. The varied applicability of AI in a multitude of industries including entertainment, transportation, finance, retail etc. makes this technology a hot job and career destination.
According to Analytics Insight, experts predict that AI alone will generate close to 58 million new jobs by 2022. However, additionally, it is predicted that this technology will take away over 10 million jobs, leading to a net addition of half a million new jobs worldwide.
Why Learn About BigData
Soaring Demand for Big Data Analytics Professionals
Technology professionals who are experienced in Analytics are in high Demand as organizations are looking for ways to exploit the power of Big Data. This apparent surge is due to the increased number of organizations implementing Analytics and thereby looking for Analytics professionals. In a study by QuninStreet Inc., it was found that the trend of implementing Big Data Analytics is zooming and is considered to be a high priority among U.S. businesses. A majority of the organizations are in the process of implenting it or actively planning. Technology professionals who are experienced in Analytics are in high demand as organizations are looking for ways to exploit the power of Big Data.
MODULE 1- INTRODUCTION AND BASICS ABOUT THE DATA SCIENCE
- Introduction and Discussion about Industry Problems associated with Data Science
- Basics of Python – Installation of Anaconda framework, useful libraries- Scikit, Kera etc
- How to deﬁne a Data Science Problem
- Basics of Statistics as needed for this course
- Data Acquisition techniques – Reading Data from different sources
- A small primer about Big Data
- Installing Oracle Virtual VM box and reading data into HDFS (Hadoop Distributed File System)
- Visualization of Data – Visual interpretation of Data using plots and charts
- Statistical inference of Data Understanding Mean, Median, Standard Deviation, Percentile and other parameters Hands-on Exercise
- Quiz and Hand-on Exercises (Grades-80% Pass Mark)
MODULE 2-PREPARING DATA FOR MACHINE LEARNING AND BASIC MODELLING
- Preparing Data for Modelling – Indexing, Slicing, Pivoting, Aggregation Techniques and using outlier detection to reduce dimension of Data
- Basics of Data Modelling in Data Science – Relational Data Model, Semi Structured Data Model
- Feature Engineering in Data Science – Extracting useful Information from Data
- Techniques to Partition data into training, development and testing samples
- Using Statistics to understand how features in Data, impact ﬁnal prediction and what is correlation of features
- Machine Learning Models- Their Classiﬁcation and taxonomy. Where and when to use which model.
- Classiﬁcation Models – Naive Bayes, Linear Discriminate Analysis, Decision Trees, K Nearest Neighbour
- Regression – Linear Regression, Logistics Regression, Least Angle Regression (LARS)
- Clustering – K-Mean Clustering, Hierarchical Clustering, K-Median Clustering
- Basics of Machine Learning Model Performance
- Quiz and Hands-on exercise (Grades-80% Pass Mark)
MODULE 3-IMPROVING PERFORMANCE OF MACHINE LEARNING MODELS
- Some tools and techniques for data preparation which helps in improving accuracy-K-fold cross validation, Principal Component Analysis
- Running Statistical testing on Training data to validate data integrity. Hypothesis testing, Student T test, P Value, Conﬁdence Interval, Statistical Power
- Concepts of Bias, Variance in Data. Bias-variance balancing Performance Indicators of model such as Sensitivity, Speciﬁcity, Accuracy, Precision and Recall
- Using speciﬁc performance Indication tools such as ROC curves and RMSE scores
- Base lining Models using Random Prediction and Zero Rule Algorithm
- Concept of Gradient Boosting
- Comparing carious Machine Learning models
- Some Ensemble techniques (higher order Algorithm) to improve performance
- Quiz and Hands-on exercise (Grade 80% Pass Mark)
MODULE 4- NEURAL NETWORK AND ITS APPLICATION
- Basics of Neural Networks. How Neural Network is fast becoming one of the most used models, Some Applications.
- Introduction to Theano and Kera Libraries
- Basic building Blocks of Neutral Networks
- Solving Simple Logistical Regression classiﬁcation problems using Neural Network
- Some advanced concepts like Residual Net in Neural Network
- A Short Note on how to productive the model using APIs
- Quiz and Hands-on exercise (Grade 80% Pass Mark)
MODULE – 5 – ONE WEEK CAPSTONE PROJECT (HANDS ON)
- Industry Problem Identiﬁcation and deﬁnition
- Acquiring Data and preparing Data, with Exploratory Data Analysis
- Interim Report – I on results of Data Acquisition, preparation and Data Visualization
- Diving Data into Training, Development and test Data
- Selecting the right Model for solving the problem
- Applying Model to predict on Training Data
- Validating results using Statistical testing, P value, conﬁdence interval
- Interim report – 2 on the above three steps
- Predicting the performance of Model
- Comparing Models, Here students can choose between on lower order Model and one Neural Network model
- Finally using Ensembling techniques to show improvement of performance with ﬁnal Report, Report-3
- Grade 80% Pass Mark
- Congratulatory Note on completion
The course fees is dependent upon the category of location. For eg., USA centers will have different fees structure as compared to those in India. Within India, Delhi centers will command different fees structure as compared to those in Patna. And taxes as applicable in the country will be levied on the above fees, as per prevailing law of the country.
For USA: $1500 + Taxes Applicable.
For INDIA: Fee will depend upon the city + 18% GST Applicable.
|USA-CST||MONDAY||07:00 PM – 09:00 PM|
|USA-CST||WEDNESDAY||07:30 PM – 09:30 PM|
|USA-CST||SATURDAY||09:00 AM – 12:00 PM|
|INDIA-IST||WEDNESDAY||07:00 AM – 09:00 AM|
|INDIA-IST||FRIDAY||07:00 AM – 09:00 AM|
|INDIA-IST||SUNDAY||09:00 AM – 12:00 NOON|
USA – TRAINING CENTERS
Atlanta – Lawrenceville
Dallas – McKinney
INDIA – TRAINING CENTERS
Bangalore – JP Nagar
Bangalore – Whitefield
Bangalore – Kalyan Nagar
Ghaziabad (Indirapuram – Vaibhav Khand)
Delhi NCR – Vasant Kunj
Delhi NCR – Jagat Puri
Delhi NCR – Noida Altsols
Dharamshala, Himachal Pradesh