Artificial Intelligence Course in Hyderabad, India

The Artificial Intelligence course in Hyderabad delivers you the latest innovations and trends in emerging technologies. Embrace in-demand skills and transform your career.

  • Classroom/Online Virtual Live Instructor-led Sessions
  • Get IBM Certification
  • 1:1 Mentorship
  • Work on Industry Live Projects
  • Industry Placement Training

How Artificial Intelligence Training in Hyderabad, will be beneficial to you?

Artificial intelligence training in Hyderabad provides a world of opportunities to students as well as professionals to help them achieve their career goals. The course on Artificial Intelligence is the most comprehensively designed training program that covers a complete understanding of Artificial Intelligence concepts, principles, algorithms, and fundamentals to understand how AI is transforming the world around us. We collaborate with companies and individuals to handle and address the needs of the current market and provide training following the latest trends. We have placed our students in reputed companies and our panel of experts and certified trainers provide world-class training which is a blend of classroom sessions, virtual interactive sessions, assignments, and 24/7 assistance.

Why should you Learn Artificial Intelligence?

AI has been an integral part of our everyday lives and many industries today are capitalizing on the advancements in AI. With large volumes of data all around us, AI has been successful in embedding smartness in machines and can get the most out of data by driving patterns and insights from it to automate tasks for a variety of business benefits. Learning Artificial Intelligence opens a world of truly endless possibilities. You can work as a software engineer with companies like Amazon, Facebook, or as a hardware engineer developing home assistant robots. AI is a truly exciting field that keeps growing and changing and is becoming valuable in all businesses to elevate performances. To build a successful career in Artificial Intelligence and learn to capitalize on the ever-growing amount of data that is being generated and collected, enroll for this course and prepare to be awestruck with the potential in this field.

Is the certification necessary for the Artificial intelligence course?

Yes, a certification in Artificial Intelligence Course in Hyderabad testifies your skill as an expert in the business applications of AI and makes you industry-ready for Artificial Intelligence job roles. It adds immense value to your profile and increases your chances of getting your dream job. Learn from industry experts who have decades of experience and are passionate trainers who have meticulously crafted the course material in conjunction with the recent trends so that our students remain ahead in their professional careers. The demand for AI is accelerating progressively in the modern world where everything is driven by data and automation. According to the report it is estimated that AI will contribute towards economic growth by an average of 2 percent across 15 industries by 2035. So, a certification in AI prepares you for this challenging and exciting profession that is growing forcefully into the years to come and helps you jumpstart your career in this fast-growing field. Get certified by prestigious universities/companies like UTM, IBM, CareerEx, and Panasonic, etc.

Who should pursue this course?

With the staggering demand for AI in a wide range of industries and is the most happening topic which is providing a thoughtful path for the individuals to move ahead with their career goals. The following candidates can pursue this innovative and thrilling field with limitless opportunities.

  • Freshers who are not familiar with AI or its implications.
  • Individuals or graduates aspiring to be Artificial Intelligence Engineer
  • Analytics Managers
  • Professionals who want to upskill expertise in Artificial Intelligence algorithms
  • Non-technical and Management participants
  • Big Data Engineer/Architect.
  • Business Intelligence Developer

What is Artificial Intelligence?

When computer systems can analyze data, which is collected from their environment and then perform various tasks to achieve specific goals or make a decision like a human would is termed as Artificial Intelligence. It is a system that is driven by data which makes AI a powerful tool. Unlike humans, artificial intelligence training in Hyderabad can analyze humongous volumes of data and can also dive deeper into the data with much more accuracy.

Course Overview

The Artificial intelligence training program in Hyderabad is designed to cover core topics of AI including Machine Learning, Deep Learning, and Natural Language Processing. Students will get to explore all the concepts of AI including basic principles, techniques, structures, models, and building algorithms. This is a perfect platform to learn the basic principles, techniques, and strengths of the various business applications of Artificial Intelligence through clustering and classification algorithms. The course is divided into various modules that promote step by step learning and are powered with a ‘hands-on’ approach that will contribute to further understanding of the concepts. All students receive subjective feedback upon submission of their assignments to facilitate improvement and engage in collaborative real-life projects. This training aims to prepare you to be job-ready for this fascinating and fast-moving field of artificial intelligence.

What are the Prerequisites?

Degree Subjective Knowledge Statistical Knowledge
A degree Bsc, Bcom, Btech, etc Statistics and Computer Skills Some Basic Programming Skills

Training Methodology

We offer the best artificial intelligence training course in Hyderabad to help advance your career. We provide a well-integrated course along with world-class ambiance and faculty. Out training, methiodal helps bring together the knowledge and understanding in a crystalized manner with the help of examples. Our trainers are industry experts who use a blended learning approach which includes theory as well as practical knowledge of the various concepts. We provide online as well as classroom training sessions to facilitate the needs of our students along with the flexibility of timings as per the needs of the participant. After the course, completion students can avail of the internship program that will expose them to live projects giving them the flavor of real-time cases.

What are the tools covered as part of Artificial Intelligence training?

With the growth of AI and ML, the number of tools and frameworks available to data scientists and developers has increased. The major architectural decisions or optimization tasks are done with the help of these tools. In this course, we will cover tools like OpenCV, Spyder, PyTorch, TensorFlow, Python, R, RStudio, and Keras.

About Instructor

data science course in hyderabad

Mr. Bharani Kumar Depuru, Director and CEO of 360DigiTMG and alumnus of IIT and ISB. With 15 plus years of experience, he is an expert in Data Science, Digital Transformation, Business Analytics, Artificial Intelligence, Deep Learning, IoT, and Project Management. He has trained more than 2500 professionals across the globe. With his grit and proactive approach to learning, he has been able to build a strong foundation of learning. He uses a mix of creative instruction methods to achieve learning objectives and to give learners a 360-degree view of their course content.

Career Opportunities in Artificial Intelligence

Build an exciting career in the fast-growing field of artificial intelligence that is defining our society in ways we never anticipated like unlocking our smartphones with our faces, asking questions, and receiving vocalized answers from our virtual assistants. Many such applications of AI continue to increase creating a positive career potential for those with the required skills to thrive in this industry. Some of the top careers defining the industry today include Artificial Intelligence Engineer, AI Project Manager, Researcher, and Artificial Intelligence consultant, Machine learning /AI Engineer, Computer Vision Engineer, Algorithm Developer, Research Scientist, etc.

Become Highly Demanding AI Professional

Companies are Hiring

Which Companies are Hiring?

Artificial Intelligence has become a growing force in business and is creating many job opportunities across a multitude of industries. Some of the top-notch companies like Amazon, Google, Apple, Microsoft, and IBM are hiring individuals with relevant AI Skill.

Curriculum in Detail

Module 1 - Get introduced to Deep learning and Artificial Intelligence

  • What we do at 360DigiTMG and Innodatatics
  • Introduction to Deep Learning and Artificial Intelligence
  • Outline of the course,Road Map and course takeaways
  • Introduction to CRISP-DM model
  • AI applications

Module 2 - All About Python Libraries

  • Introduction to libraries used for deep learning
    • Keras
    • Tensorflow
    • PyTorch
    • OpenCV
  • Getting into details of Keras,Tensorflow,PyTorch and OpenCV
  • Setting Up Anaconda,R,Spyder,Jupyter and google colab
  • Installation of Multiple deep learning libraries

Module 3 - Machine Learning Concepts

  • Explanation on Machine learning and its types
    • Supervised Learning
    • Unsupervised Learning
    • Semi-Supervised Learning
    • Reinforcement Learning
    • Active Learning
    • Transfer Learning
    • Structured Prediction
  • CRISP-DM Steps
    • Understanding the Business Problem
    • Data Collection
    • Data Cleaning and Preparation
      • Outlier Analysis
      • Missing values
      • Transformation
      • Normalization and Standardization
      • Discretization
    • Sampling technique to handle imbalance dataset
    • Measures of central tendency
      • Mean
      • Median
      • Mode
      • Variance
      • Standard deviation
      • Range
  • Data Types
    • Continuous
    • Discrete
    • Categorical
    • Count
    • Qualitative
    • Quantitative
    • Big Data
    • Non Big Data
    • Longitudinal
    • Time series Data
    • Structured
    • Unstructured
    • Cross Sectional
    • Balanced
    • Imbalanced
    • Batch Processing
    • Real Time Processing
  • Feature Engineering
    • Feature Extraction
    • Feature Selection
  • Error Functions
    • ME
    • MAD
    • MSE
    • MPE
    • RMSE
    • MAPE
    • Cross Table
    • Confusion matrix
    • Binary Cross Entropy
    • Categorical Cross Entropy

Module 4 - Mathematical Concepts

  • Optimizers
  • Slope
  • Derivatives
  • Tangent
  • Maxima and Minima
  • Linear regression
  • Probability

Module 5 - Introduction to Deep Learning

  • Human Brain
    • Introduction to Biological & Artificial Neuron
  • Data Compositionality
    • Images
    • Speech
    • Text
  • Mathematical Notations
  • Introduction to Artificial Neural Network
  • Components of Neural Network
    • Neuron
    • Weights
    • Activation Function
    • Integration Function
    • Bias
    • Output

Module 6 - Perceptron and Multilayer Perceptron

  • Introduction to Perceptron
  • Introduction to Multilayer Perceptron (MLP)
  • Activation Functions
    • Identity Function
    • Step Function
    • Ramp Function
    • Sigmoid Function
    • Tanh Function
    • ReLU
    • ELU
    • Leaky ReLU
    • Maxout
  • Back Propagation algorithm
  • Network Topology
  • Error Surface
    • Learning Rate
    • Random Weight Initialization
  • Local Minima issues in Gradient Descent Learning
  • Pros and Cons of DL

Module 7 - Deep Learning Challenges and Practical issues

  • Vanishing Gradient
  • Error Surface challenge
  • Learning Rate Challenges
  • Decay Parameter
  • Gradient Descent Algorithmic Approaches
  • Variants of SGD
    • Adam
    • AdaGrad
    • Adadelta
    • RMSProp
    • Momentum
    • Nestrov Momentum
  • DropOut
  • DropConnect
  • Adding Noise
  • Data Augmentation
  • Parameter Choices
  • Weights Initialization (Xavier, etc.)

Module 8 - CNN-Convolutional Neural Networks

  • ImageNet Challenge
  • Hierarchical Approach
  • Disadvantages of MLP in Images
  • Convolution Networks
    • 1D ConvNet
    • 2D ConvNet
    • Transposed Convolution
  • Convolution Layers and Visualizing Convolution Layers
  • Pooling Layer
  • Transfer Learning
    • VGG16
    • VGG19
    • Resnet
    • GoogleNet
  • Padding
  • Stride
  • Batch Normalization

Module 9 - Computer Vision and Image Processing

  • Introduction to Image Processing
  • Challenges of Image Processing
    • Interclass Variation
    • View Point Variation
    • ILlumination
    • Background Clutter
    • Occlusion
    • Multiple Categories
  • Transformation of Image
  • Image Processing Operations
  • Noise Reduction
    • Moving Average
    • 2D Moving Average
  • Image Filtering
    • Linear Filtering
    • Gaussian Filtering
  • Convolution
  • Boundary Effects
    • Zero
    • Wrap
    • Clamp
    • Mirror
  • Image Sharpening
  • Template Matching
  • Edge Detection
    • Image Filtering
    • Origin of Edges
  • Sobel Edge Detector
  • Effect of Noise
  • Laplacian Filter
  • Smoothing with Gaussian
  • LOG filter
    • Blob Detection
  • Noise Reduction
    • Salt and Pepper Noise
  • Nonlinear Filters
  • Bilateral Filters
  • Canny Edge Detector
    • Non Maximum Suppression
    • Hysteresis Thresholding
  • Image Up-sampling
  • Interpolation techniques
    • Nearest Neighbour Interpolation
    • Linear Interpolation
    • Bilinear Interpolation
    • Cubic Interpolation

Module 10 - RNN-Recurrent Neural Network

  • Adversaries
  • Language Models
    • Predicting the next word
    • Spelling Checkers
    • Auto correct
  • Traditional Language Models
  • Disadvantages of using MLP
  • State
  • RNN cell
  • Backpropagation Through Time
  • Loss in RNN
  • Types of RNN
    • One to One
    • One to Many
    • Many to One
    • Many to Many
  • Image Captioning
  • Bidirectional RNN
  • Deep Bidirectional RNN
  • Disadvantages of RNN

Module 11 - Mask-RCNN

  • Fast R-CNN and Mask R-CNN
  • CNN-RNN Variants
  • R-CNN
  • Fast R-CNN
  • Faster R-CNN
  • Mask R-CNN

Module 12 - LSTM-Long Short Term Memory

  • Architecture of LSTM
  • Cell State
  • Input Gate
  • Output Gate
  • Forget Gate
  • Mathematical behind LSTM
  • RNN vs LSTM
  • Bidirectional vs Deep Bidirectional RNN
  • Deep RNN vs Deep LSTM

Module 13 - Gates Recurrent Units

  • Architecture of GRU
  • Update Gate
  • Reset Gate
  • Current Memory
  • Applications of GRUs

Module 14 - Autoencoders, Variational Autoencoders

  • Comparing Autoencoders with other compressors
  • Implementation in Keras
  • Deep Autoencoders
  • Convolutional Autoencoders
  • Variational Autoencoders

Module 15 - Deep Belief Networks (DBNs) and RBM

  • Boltzmann architecture
  • RBM Architecture
  • Energy Functions
  • DBN Architecture
  • Applications of DBN

Module 16 - GAN’s

  • Model Inputs and Hyperparameters
  • Implementation of GANs
  • Generator and Discriminator
  • Discriminator and Generator Losses
  • Training GAN’s

Module 17 - Super Resolution GAN

  • Generator and discriminator for SRGAN
  • Loss Function
  • Keras Implementation of SRGAN

Module 18 - Reinforcement Learning and Q-learning

  • Deep Reinforcement Learning
  • Maximizing Rewards
  • Exploration and Exploitation Tradeoff
  • Q-Learning and Deep Q-Network
  • Improving and Moving ahead of DQN
  • Keras implementation of Deep Q-Network

Module 19 - Speech Recognition

  • Pipeline
  • Phonemes
  • Pre-processing
  • Acoustic Model
  • Deep Learning Models

Module 20 - Chatbots

  • NLP implementation in chatbots
  • Neural Networks Chatbot
  • Generative Chatbot Development
  • Building a Retrieval Based Chatbot
  • Deploying Chatbots

Artificial Intelligence Salaries in Hyderabad

Machines or computer systems can demonstrate levels of intelligence similar to human intelligence and more and more companies are leveraging Artificial Intelligence in their business processes giving the job opportunities in AI to grow exponentially. Refer to the average salaries per annum for some of the AI job roles in Hyderabad. The below given average salaries are based on factors like education, skills, job title, and certifications.

AI Job Roles Average salary as per Glassdoor Average salary as per PayScale
Artificial Intelligence Engineer Rs. 7,26,271 Rs. 7,500,641
AI Project Manager Rs. 16,19,559 Rs. 15,04,283
Computer Vision Engineer Rs. 5,59,698 Rs. 5,28,464
Machine Learning Engineer Rs. 9,51,687 Rs. 7,23,467
Algorithm Developer Rs. 17,46,737 Rs. 14,23,467

The salaries mentioned here are for reference only, it is not accurate. Salaries vary accordingly with skills and experience.

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Artificial Intelligence Interview Questions

Explain Symbolic AI versus Machine Learning?

Within Symbolic AI, we provide a bunch of rules along with the data as input and out come the answers in line with the rules. Within Machine Learning, we provide data as well as answers expected to the computer as input and we get rules as for output.

What is the main variation between Machine Learning and Statistics?

Machine Learning deals with a huge amount of complex datasets & applying statistical techniques such as Bayesian analysis for this kind of data is impractical. While there are a lot of similarities between ML and Statistics, there are a few valid differences as well.

What is probabilistic modeling?

Probabilistic modeling is a branch of statistical analysis, which was one of the early machine learning algorithms and a classic example of such an algorithm, which is still being used in the present world is the Naive Bayes algorithm.

How do we encode the categorical labels?

We use the function in Keras called "to_categorical" to encode the categorical labels.

What are the different loss functions related to the Neural Network?

For a 2-class problem, we use binary cross-entropy. For a multi-class classification problem, we use categorical cross-entropy. For a regression problem, we use the Mean Squared Error. For a sequence learning problem, we use Connectionist Temporal Classification.

What function do we use in the Python Keras library for creating the network architecture?

a. model.add()

b. model.compile()


d. All of the above

- A. model.add() is used to add layers to the neural network.

Which of the following is part of 4D tensors of shape?

a. Samples

b. Height & Width

c. Channels

d. All of the above

- D. Images have a 4D tensor shape and include samples, height, width, channels.

Which of the following is an additional attribute of 5D tensor when dealing with videos?

a. Samples

b. Frames

c. Height & Width

d. Color depth

- B. The frame is an additional attribute captured as part of tensor when we use videos.

Selecting specific elements of a tensor is called as_________

a. Tensor Addition

b. Tensor Subtraction

c. Tensor Division

d. Tensor Slicing

- D. Tensor Slicing is selecting specific elements in the tensor.

Which of the following is a Gradient descent algorithm?

a. Stochastic Minibatch gradient descent

b. Batch gradient descent

c. Minibatch gradient descent

d. All of the above

- D. SGD, Minibatch SGD, Batch GD are all the various variants of gradient descent algorithms.

Artificial Intelligence Hyderabad FAQs

1. What is the duration of this course?

This course is for 2.5 months with completion certification.

2. What is the preferable mode of training?

We provide you with both the options of online as well as a classroom session.

3. Do you provide any job assistance?

Yes, we provide 100% job assistance in the form of resume building and mock interviews.

4. Do I need to attend all the classes?

You need 80% of attendance which is compulsory to get certification.

5. What certifications are provided after the completion of the course?

Upon successful completion of the course, we will provide you with an industry-recognized course completion certificate and you can also apply for IBM certification.

6. Do you provide any practice sessions for the preparation of the test?

Yes, you will be given an ample amount of practice assignments to prepare for the test.

7. What do you mean by 24x7 support?

It means we provide you support through emails, chat, LMS access. We have a team that provides on-demand assistance.

8. What qualifications do I need to enroll in this program?

The main tools are Python, R, and Python libraries are explained thoroughly.

9. Can I attend a trial class?

Yes, you can attend a demo session and we also give the first 3 sessions of the training program for free.

10. What AI terminologies will be covered in the course?

This course will cover all the basic terminologies used in AI like Algorithms. Artificial Neural Networks, Deep Learning, and we will also cover tools like OpenCV, Spyder, PyTorch, TensorFlow, Python, etc.