Hyderabad School of Artificial Intelligence With Data Science

How It Works

Dream Job

The Enrolment Process

Hyderabad School of Artificial Intelligence provides a very simple process to get you job ready starting from skills learning to real company interviews.

Learn the course

HSAI AI-Data science Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly

Complete 3 Day Project Bootcamp

A typical 3 days in a boot camp would be from 35 hours of hands-on programming focussed on an algorithms learnt

AI companies visit

Get interacted with AI company founders and See the whole ecosystem that is happening inside the company

Get JRR and groomed

Groomed by Hr Panel of visited companies and get real feedback from the recruitment panel

Perfect match

The Recruiting Process

Hyderabad School of Artificial Intelligence provides the best job ready match to a companies hiring requirement based on skills, CTC, location & even culture.

Post job requirements

Tell us about the skills required for the job role.

Set CTC & location

You will see only job ready candidates who fit your CTC budget & location criteria's

Get a perfect match

Browse through top job candidates who match upto 95% to your requirement of skills, CTC, location.

Guaranteed interviews

Check for culture fit & shortlist. Sit back & relax while our team coordinate for your interviews.

Assessed and Certified by

TCS Digital Learning

TCS ProCert - Artificial Intelligence is a 2-Stage certification to help experienced professionals gain recognition for their Artificial Intelligence proficiency and enhance their industry credibility. This certification is designed by top industry practitioners to evaluate an individual's expertise in Artificial Intelligence. The first stage is a proctored assessment which will be conducted at TCS iON assessment centres spread across India. The second stage is a Case Study based assessment to test application of domain knowledge.

Advantages of TCS ProCert - Artificial Intelligence

• Get Recognised - Gain recognition for your Artificial Intelligence experience and expertise
• Get an Edge - Stand out among your peers by enhancing your industry credibility
• Gain Access to Top Corporates Looking for Talent in Artificial Intelligence
• Get Noticed - Get career opportunities in Artificial Intelligence with top recruiters

SAMPLE CERTIFICATIONS

Skills you will master

Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Data Mining
Statistics
Linear algebra
Naivebayes
Time series
Python Libraries
Spark
RDD
Pyspark
Text Mining
Neural Networks
Clustering
Keras
Tensorflow
Image Recognition
Deep learning
Optimization
Hadoop
Nosql
Association
Decision tree
Recommendations
Many More

Bootcamp and Experiencecamp

Bootcamp - Real Project solving

Experiencecamp - presenting 3 years experienced data scientist knowledge

Bootcamps and Experiencecamps are 3 Days from 8Am -7Pm intense programming and personality development sessions conducted at all three HSAI Branches (KUKATPALLY, GACHIBOWLI , Bangalore ) on a regular basis.

Participants will learn what is needed - the methods, tools, strategies, culture, attitudes - to succeed in today's highly demanding professions and markets and gain a competitive advantage using AI and machine learning technologies.

A typical day in a bootcamp would be 10-12 hours of hands-on programming focussed on either deep-dive into an ML algorithm or using these algorithms to participate in an online competition such as Kaggle. Coding session will be followed by 1-2 hours of mindcamp sessions (debates, discussions, role-playing, etc.) which will help students build essential professional skills needed in the industry to complement the technical skills learned in the bootcamp.

Schedule

These bootcamp-mindcamp sessions will be roughly offered once every month at any one location. Please check the bootcamp-mindcamp calendar for upcoming sessions. The theme for every session may marginally vary and hence, specific schedule for the 2-weeks will be announced after registration.

Benefits

One day experience camp will be equivalent to 3 years of real data scientist experience Bootcamps and Experience camps are designed primarily for refreshing your curriculum and learning through a complete hands-on approach, to inculcate in you a holistic approach towards problem-solving and to better prepare you for your job interviews. It will also encourage you to build your public profile and contribute to websites such as Kaggle, GitHub, StackOverflow, etc.

Who should attend

It is highly encouraged for all HSAI students to attend after completing your course. If you are seeking career services from us, you may be REQUIRED to go through at least one bootcamp-experiencecamp session depending on your performance in the program.

Artificial Intelligence & Data Science : One-Day Startup Tech Tour

Hosted by Hyderabad School of Artificial Intelligence

Artificial intelligence and Data science are undoubtedly two of the most disruptive technologies in the world today. Join us for this Startup and Tech Tour to gain exclusive access to the companies driving this change and find out why they believe our lives are about to be radically altered.

You'll get first-hand experience of the products , services and concepts which are challenging existing industries and creating new ones. Get ahead of the curve and find out how your knowledge can tap into a dynamic new world.

• One-day tour
• Runs every Month
• Visit to two companies

About the host

At Hyderabad School of Artificial Intelligence our mission is to help global businesses accelerate transformation. Through our unique innovation tours, executive immersion programs, corporate strategy sessions, startup showcases and executive conferences we can connect your organization to some of the most exciting technology companies and startups Indian Silicon Valley has to offer.

A UNIQUE INDIAN SILICON VALLEY EXPERIENCE

Inspirational Insights

Discover powerful new ideas and products and services about the future of your industry by interacting with like-minded Employees, Managers, Hr recruitment team and Founders.

Practical Takeaways

Tap into the wealth of Industrial real experience has to offer. Experience disruptive new technologies and real work and find out how companies have developed their products or services

Experiential Learning

Immerse yourself in one day tour to two companies and learn from real-life experiences , challenges , failures, setbacks of project from data scientists , AI engineers and Lead managers from startups and entrepreneurs.

Hands-on Experience

See technology demos and test new products first-hand. Be the first to try out the latest innovations. Witness the future today

How will it impact my career?

Speak to us at

9618117333

Curriculum

Statistics and Probability
Course Curriculum
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Course Curriculum

Module 1: Descriptive Statistics

Objective: This session introduces you with how statistics is used in business with basic statistical concepts like levels of data and measures of central tendencies

Topics

Introduction to Statistics
Charts and Graphs
Descriptive Statistics
Probability

Lab Session: You will execute measures of central tendencies and different charts using R programming , and hand based problems on estimating mean , median , mode and various problems on Probability

Module 2: DISTRIBUTIONS AND SAMPLING

Objective: You will get into the deeper aspects of various distributions. You will understand the parameters that define the probability distributions and differences between discrete and continuous distributions.

Topics

Discrete Distributions
Continuous Distributions
Sampling and Sampling Distributions
Probability
Bayes Theorem

Lab Session: We will solve several problems related on Bayes Theorem. Hands-on implementation of Monte Carlo Simulations in R will also be done.

Module 3: MAKING INFERENCES ABOUT POPULATION PARAMETERS

Objective: You will also start making statistical inferences about populations from samples.

Topics

Statistical Inference: Estimation for Single Populations
Statistical Inference: Hypothesis Testing for Single Populations
Statistical Inferences About Two Populations
Analysis of Variance and Design of Experiments

Lab session:

you will learn how to conduct a statistical hypothesis testing In R and will be introduced practical session and various problems and using various methods such as t-test, z-test, F-test and ANOVA methods in detail.

Module 4: NONPARAMETRIC STATISTICS

Objective:Till this point you will have received the complete picture how to understand the data, attributes, distributions, sample versus population, and procedure for statistical testing, etc. While you continue the analysis of a variable, you will extend that understanding to analyse the relationship between categorical variables.

Topics

Analysis of Categorical Data
Nonparametric Statistics

Lab session: Hands on R session on chi-square test and various paper based problems

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Course Curriculum

Module 1: Introduction to R concepts

Objective: In this introduction to R, you will master the basics of this beautiful open source language, including factors, lists and data frames. With the knowledge gained in this course, you will be ready to undertake your first very own data analysis

Topics

Intro to Basics
Vectors
Matrices
Factors
Data Frames
Lists

Module 2: Intermediate R

Objective: The intermediate R course is the logical next stop on your journey in the R programming language. In this R training you will learn about conditional statements, loops and functions to power your own R scripts. Next, you can make your R code more efficient and readable using the apply functions.

Topics

Conditionals and Control Flow
Loops
Functions
Apply () family
Utilities

Module 3: Data Manipulation

Objective: To clean the data we use various Pre-processing Techniques techniques

Topics

Binning
Filling missing values
Standardization & Normalization
type conversions
train-test data split
ROCR1

Note:You will be using R throughout the course by with various algorithms

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Decision Modelling
Course Curriculum
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Course Curriculum

Module 1: Linear Regression

Objective: Predictive analytics search for patterns found in historical and transactional data to understand a business problem and predict future events. In many business problems, we try to deal with data on Several variables, sometime more than the number of observations. Regression models help us understand the relationships among these variables and how the relationships can be exploited to make decisions. Primary objective of this module is to understand how regression and causal forecasting models can be used to analyse real-life business problems such as prediction, classification and discrete choice problems. The focus will be case-based practical problem-solving using predictive analytics techniques to interpret model outputs.

Topics

Covariance
Correlation
Simple linear Regression
Multiple Linear Regression
Coefficient of determination
StepAIC
Multicollinearity check
Model diagnostics
Model Validations

Lab session : On given Data Set hands on training will be given to execute various steps to perform linear regression using R Programming . You will be given one more data set as home work assignment to be submitted

Module 2: Logistic Regression

Objective: Logistic regression is a method for classifying data into discrete outcomes. For example, we might use logistic regression to classify an email as spam or not spam. In this module, we introduce the notion of classification, the cost function for logistic regression, and the application of logistic regression to multi-class classification

Topics

Logistic function
Estimation of probability using logistic regression
Model Evaluation
Confusion Matrix
Naive Bayes classifier

Lab session: On given Data Set hands on training will be given to execute various steps to perform Logistic regression using R Programming . You will be given one more data set as home work assignment to be submitted

Module 3: Feature Engineering and Regularization

Objective: One of the major aspects of training your machine learning model is avoiding overfitting. The model will have a low accuracy if it is overfitting. This happens because your model is trying too hard to capture the noise in your training

Topics

PCA
EDA
Lasso
Ridge Regression
Over fitting

Lab session : You will be using all the Dimensionality Reduction Techniques on a given data set and will be asked to apply any one of linear or logistic regression using R

Module 4: Time series Analysis

Objective: A powerful approach to analyze financial data and other forms of data based on their time dependent past values is known as the Time series analysis.

Topics

Trend analysis
Cyclical and Seasonal analysis
Smoothing
Moving averages
Auto-Correlation
ARIMA

Lab session: On given Data Set hands on training will be given to execute various steps to perform Time Series Analysis using R Programming . You will be given one more data set as home work assignment to be submitted

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Machine Learning
Course Curriculum
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Course Curriculum

Module 1: Clustering

Objective: Learn about unsupervised learning and various types of clustering that can be used to analyze the data

Topics

Distance Calculation
Hierarchical Clustering
K means Clustering
K medoids Clustering
Model Based Clustering

Lab session : On given Data Set hands on training will be given to execute various steps to perform Clustering using R Programming . You will be given one more data set as home work assignment to be submitted

Module 2: Recommendation Systems

Objective: Learn how to build a powerful recommendation engine using various Methods

Topics

Collaborative Filtering
Hybrid recommendation
Association Rules
Apriori Algorithm
User - Based Method
Item-Based Methods
Different Methods of Recommending
Evaluation Goals

Lab session: On given Retail data set we perform various methods of recommending items by building recommendation engine using R . You will be given one more data set as home work assignment to be submitted

Module 3: Decision Tree

Objective: This Algorithm is meant to learn tree based modeling. After the successful completion of this Algorithm, one is expected to become proficient at using tree based algorithms and build predictive models. They are adaptable at solving any kind of problem at hand (classification or regression).

Topics

Sample Decision Tree
CART
C5.0
Entropy
Information Gain
Building Regression

Lab session: Implementing a Decision tree Model on given data set for both classification and Prediction . You will be given one more data set as home work assignment to be submitted

Module 4: Ensemble Methods

Objective: In machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any one single algorithm. The basics of ensembles are bagging & boosting that will be covered in detail and later progress with machine learning methods that use either or both approaches to build ensemble models.

Topics

Bagging and Boosting
Random Forest
GBM
XGBoost
Stacking

Lab session : Performing Multiple combination of models on a given dataset using R . You will be given one more data set as home work assignment to be submitted

Module 5: KNN Algorithm

Objective: KNN can be used for both classification and regression problems! It’s far more popularly used for classification problems

Topics

Distance Calculation
Computational geometry
Voronoi Diagrams
Delaunay Triangulations
Wilson editing and triangulations

Labsession : Implementing a KNN Model on given data set for both classification and Prediction . You will be given one more data set as home work assignment to be submitted

Module 6: Support vector machines

Objective: Support Vector Machines (SVM) is the most elegant technique developed in the last two decades. You will also learn another powerful technique Hidden Markov Models (HMM) that has a very rich mathematical structure and forms a very strong theoretical basis for a wide range of applications.

Topics

SVM Algorithm
Linear learning machines
Kernel space
Making Kernels and working in feature space
Svm Classification
Svm Regression

Labsession : Implementing a SVM Model on given data set for both classification and Prediction . You will be given one more data set as home work assignment to be submitted

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Data Visualization
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Python Programming
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Deep Learning
Course Curriculum
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Course Curriculum

Module 1: Artificial Neural Networks

Objective: Deep learning techniques are so powerful because they learn with layers the best way to represent the problem while learning how to solve the problem.

Deep architecture
ANN Initution
Single Layer Perceptron
Multi Layer Perceptron
Back propagation
Keras

Labsession : On a Given Data set We execute Ann Algorithm with both layers based on Data set using keras Package of python . Home assignment will be given with another data set

Module 2: Auto Encoders

Objective: Generally, you can consider autoencoders as an unsupervised learning technique, since you don’t need explicit labels to train the model on. All you need to train an autoencoder is raw input data.

Topics

simple autoencoder based on a fully-connected layer
a sparse autoencoder
a deep convolutional autoencoder
an image denoising model
a sequence-to-sequence autoencoder
a variational autoencoder

Module 3: Optimization Techniques

Objective: At the end of the Module, you will be able to answer questions like “Should I outsource a service or do it in-house?”, “How to optimize a supply chain?”, “How to price a product when faced with demand uncertainty?” and “How to price a derivative product using a Monte Carlo simulation

Topics

Linear Programming
Monte Carlo simulation
Genetic Algorithms
Gradient Descent
Markov chains

Lab Sessions: You will be given Transportation problem and finance Problem dataset. Based on the problem we use different types of techniques in R and Python

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Computer vision
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Natural Language Processing
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Intelligent Systems
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AI projects @ Hyderabad school of Artificial intelligence

Pre-populate Chest X-Ray Scan reports

Pre-populate Chest X-Ray Scan reports It is a chest X-ray screening tool built with deep learning. It classifies chest X-rays as normal or abnormal, identifies the abnormal findings, and highlights them the X-ray.
IT also generates a description of the X-ray findings, including name, size and location of the abnormality, that is used to pre-fill radiology reports.

Healthcare Analytics

There are a variety of techniques available that are expected to examine these varied kinds of data. Analyzing healthcare data will allow physicians to recognize the patterns that are still uncovered in the data. It will also help them to make a patient profile of individuals and can estimate the likelihood of a patient to undergo from a particular medical complexity in the near future accurately.

Building ChatBots with Deep Learning

A ChatBot is an implementation of Conversational Interface Intelligently comprising of Machine Learning, Deep Learning as their technology. . A chatbot is a computer program that copies human communications in its natural format including text or spoken language using AI procedures such as Natural Language Processing, image, video processing, and audio analysis. The most impressive characteristic of the bots is that they learn from past interactions and become intelligent and more intelligent over time. However, smart machine based chatbots receive its capabilities from Artificial Intelligence and Cognitive Computing and adapt their operation based on customer interactions

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Focus Financial Partners

Leveraging the SenseID Authentication Service, Focus Financial Partners enables a variety of services for registered users in the process of applying for a credit line.
This includes face verification (including liveness detection), identity authentication, and ID card and credit card verification. Not only has this greatly reduced the firm's financial risks, but it has also enhanced the overall user experience while strengthening customer risk management and control capabilities.

Research Projects from leading AI

Health Care

1. Drug discovery using Neural Networks
2. Tumor detection from Brain MRI images
3. Detection and Classification of cancer cells in MRI Images
4. Organ Segmentation and Labelling in MRI Images
5. Cancer cell detection and segmentation

Know more

Artificial Intelligence with Data Science Program with Digital learning certification

Programme:

Course Formation Time

Gachibowli Branch

Eligibility:

Duration:

Tuition Fee:

Placements:

ASuccessful candidates will get a chance to appear for the single round of interview from TCS. Candidates will be asked technical, management and HR questions during the interview. Candidates are advised to prepare accordingly.

Skills you will master

How It Works

Advantages of TCS ProCert - Artificial Intelligence

Skills you will master

Bootcamp and Experiencecamp

Bootcamp - Real Project solving

Experiencecamp - presenting 3 years experienced data scientist knowledge

Schedule

Benefits

Who should attend

Hosted by Hyderabad School of Artificial Intelligence

About the host

A UNIQUE INDIAN SILICON VALLEY EXPERIENCE

Inspirational Insights

Practical Takeaways

Experiential Learning

Hands-on Experience

Course Curriculum

Watch Recording

Course Curriculum

Watch Recording

Course Curriculum

Watch Recording

Course Curriculum

Watch Recording

Course Curriculum

Watch Recording

Course Curriculum

Watch Recording

Course Curriculum

Watch Recording

Course Curriculum

Watch Recording

Course Curriculum

Course Curriculum

Watch Recording

Course Curriculum

Watch Recording

AI projects @ Hyderabad school of Artificial intelligence

Pre-populate Chest X-Ray Scan reports

Healthcare Analytics

Building ChatBots with Deep Learning

Focus Financial Partners

Research Projects from leading AI

Health Care

Address