Instructor Led Live Online
Self Learning + Live Mentoring
In - Person Classroom Training
MODULE 1 : ARTIFICIAL INTELLIGENCE OVERVIEW
• Evolution Of Human Intelligence
• What Is Artificial Intelligence?
• History Of Artificial Intelligence
• Why Artificial Intelligence Now?
• Areas Of Artificial Intelligence
• AI Vs Data Science Vs Machine Learning
MODULE 2 : DEEP LEARNING INTRODUCTION
• Deep Neural Network
• Machine Learning vs Deep Learning
• Feature Learning in Deep Networks
• Applications of Deep Learning Networks
MODULE3 : TENSORFLOW FOUNDATION
• TensorFlow Structure and Modules
• Hands-On:ML modeling with TensorFlow
MODULE 4 : COMPUTER VISION INTRODUCTION
• Image Basics
• Convolution Neural Network (CNN)
• Image Classification with CNN
• Hands-On: Cat vs Dogs Classification with CNN Network
MODULE 5 : NATURAL LANGUAGE PROCESSING (NLP)
• NLP Introduction
• Bag of Words Models
• Word Embedding
• Hands-On:BERT Algorithm
MODULE 6 : AI ETHICAL ISSUES AND CONCERNS
• Issues And Concerns Around Ai
• Ai And Ethical Concerns
• Ai And Bias
• Ai:Ethics, Bias, And Trust
MODULE 1 : PYTHON BASICS
• Introduction of python
• Installation of Python and IDE
• Python Variables
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
MODULE 2 : PYTHON CONTROL STATEMENTS
• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements
MODULE 3 : PYTHON DATA STRUCTURES
• Basic data structure in python
• Basics of List
• List: Object, methods
• Tuple: Object, methods
• Sets: Object, methods
• Dictionary: Object, methods
MODULE 4 : PYTHON FUNCTIONS
• Functions basics
• Function Parameter passing
• Lambda functions
• Map, reduce, filter functions
MODULE 1 : OVERVIEW OF STATISTICS
• Introduction to Statistics
• Descriptive And Inferential Statistics
• Basic Terms Of Statistics
• Types Of Data
MODULE 2 : HARNESSING DATA
• Random Sampling
• Sampling With Replacement And Without Replacement
• Cochran's Minimum Sample Size
• Types of Sampling
• Simple Random Sampling
• Stratified Random Sampling
• Cluster Random Sampling
• Systematic Random Sampling
• Multi stage Sampling
• Sampling Error
• Methods Of Collecting Data
MODULE 3 : EXPLORATORY DATA ANALYSIS
• Exploratory Data Analysis Introduction
• Measures Of Central Tendencies: Mean,Median And Mode
• Measures Of Central Tendencies: Range, Variance And Standard Deviation
• Data Distribution Plot: Histogram
• Normal Distribution & Properties
• Z Value / Standard Value
• Empherical Rule and Outliers
• Central Limit Theorem
• Normality Testing
• Skewness & Kurtosis
• Measures Of Distance: Euclidean, Manhattan And Minkowski Distance
• Covariance & Correlation
MODULE 4 : HYPOTHESIS TESTING
• Hypothesis Testing Introduction
• P- Value, Critical Region
• Types of Hypothesis Testing
• Hypothesis Testing Errors : Type I And Type II
• Two Sample Independent T-test
• Two Sample Relation T-test
• One Way Anova Test
• Application of Hypothesis testing
MODULE 1: MACHINE LEARNING INTRODUCTION
• What Is ML? ML Vs AI
• Clustering, Classification And Regression
• Supervised Vs Unsupervised
MODULE 2: PYTHON NUMPY PACKAGE
• Introduction to Numpy Package
• Array as Data Structure
• Core Numpy functions
• Matrix Operations, Broadcasting in Arrays
MODULE 3: PYTHON PANDAS PACKAGE
• Introduction to Pandas package
• Series in Pandas
• Data Frame in Pandas
• File Reading in Pandas
• Data munging with Pandas
MODULE 4: VISUALIZATION WITH PYTHON - Matplotlib
• Visualization Packages (Matplotlib)
• Components Of A Plot, Sub-Plots
• Basic Plots: Line, Bar, Pie, Scatter
MODULE 5: PYTHON VISUALIZATION PACKAGE - SEABORN
• Seaborn: Basic Plot
• Advanced Python Data Visualizations
MODULE 6: ML ALGO: LINEAR REGRESSION
• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Modeling and Evaluation in Python
MODULE 7: ML ALGO: LOGISTIC REGRESSION
• Introduction to Logistic Regression
• How it works: Classification & Sigmoid Curve
• Modeling and Evaluation in Python
MODULE 8: ML ALGO: K MEANS CLUSTERING
• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works : K Means theory
• Modeling in Python
MODULE 9: ML ALGO: KNN
• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Modeling and Evaluation in Python
MODULE 1: FEATURE ENGINEERING
• Introduction to Feature Engineering
• Feature Engineering Techniques: Encoding, Scaling, Data Transformation
• Handling Missing values, handling outliers
• Creation of Pipeline
• Use case for feature engineering
MODULE 2: ML ALGO: SUPPORT VECTOR MACHINE (SVM)
• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Modeling and Evaluation of SVM in Python
MODULE 3: PRINCIPAL COMPONENT ANALYSIS (PCA)
• Building Blocks Of PCA
• How it works: Finding Principal Components
• Modeling PCA in Python
MODULE 4: ML ALGO: DECISION TREE
• Introduction to Decision Tree & Random Forest
• How it works
• Modeling and Evaluation in Python
MODULE 5: ENSEMBLE TECHNIQUES - BAGGING
• Introduction to Ensemble technique
• Bagging and How it works
• Modeling and Evaluation in Python
MODULE 6: ML ALGO: NAÏVE BAYES
• Introduction to Naive Bayes
• How it works: Bayes' Theorem
• Naive Bayes For Text Classification
• Modeling and Evaluation in Python
MODULE 7: GRADIENT BOOSTING, XGBOOST
• Introduction to Boosting and XGBoost
• How it works?
• Modeling and Evaluation of in Python
MODULE 1: TIME SERIES FORECASTING - ARIMA
• What is Time Series?
• Trend, Seasonality, cyclical and random
• Stationarity of Time Series
• Autoregressive Model (AR)
• Moving Average Model (MA)
• ARIMA Model
• Autocorrelation and AIC
• Time Series Analysis in Python
MODULE 2: SENTIMENT ANALYSIS
• Introduction to Sentiment Analysis
• NLTK Package
• Case study: Sentiment Analysis on Movie Reviews
MODULE 3: REGULAR EXPRESSIONS WITH PYTHON
• Regex Introduction
• Regex codes
• Text extraction with Python Regex
MODULE 4: ML MODEL DEPLOYMENT WITH FLASK
• Introduction to Flask
• URL and App routing
• Flask application – ML Model deployment
MODULE 5: ADVANCED DATA ANALYSIS WITH MS EXCEL
• MS Excel core Functions
• Advanced Functions (VLOOKUP, INDIRECT..)
• Linear Regression with EXCEL
• Data Table
• Goal Seek Analysis
• Pivot Table
• Solving Data Equation with EXCEL
MODULE 6: AWS CLOUD FOR DATA SCIENCE
• Introduction of cloud
• Difference between GCC, Azure,AWS
• AWS Service ( EC2 instance)
MODULE 7: AZURE FOR DATA SCIENCE
• Introduction to AZURE ML studio
• Data Pipeline
• ML modeling with Azure
MODULE 8: INTRODUCTION TO DEEP LEARNING
• Introduction to Artificial Neural Network, Architecture
• Artificial Neural Network in Python
• Introduction to Convolutional Neural Network, Architecture
• Convolutional Neural Network in Python
MODULE 1: DATABASE INTRODUCTION
• DATABASE Overview
• Key concepts of database management
• Relational Database Management System
• CRUD operations
MODULE 2: SQL BASICS
• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MY SQL workbench installation
MODULE 3: DATA TYPES AND CONSTRAINTS
• Numeric, Character, date time data type
• Primary key, Foreign key, Not null
• Unique, Check, default, Auto increment
MODULE 4: DATABASES AND TABLES (MySQL)
• Create database
• Delete database
• Show and use databases
• Create table, Rename table
• Delete table, Delete table records
• Create new table from existing data types
• Insert into, Update records
• Alter table
MODULE 5: SQL JOINS
• Inner join
• Outer join
• Left join
• Right join
• Cross join
• Self join
• Windows functions: Over, Partition , Rank
MODULE 6: SQL COMMANDS AND CLAUSES
• Select, Select distinct
• Aliases, Where clause
• Relational operators, Logical
• Between, Order by, In
• Like, Limit, null/not null, group by
• Having, Sub queries
MODULE 7: DOCUMENT DB/NO-SQL DB
• Introduction of Document DB
• Document DB vs SQL DB
• Popular Document DBs
• MongoDB basics
• Data format and Key methods
MODULE 1: GIT INTRODUCTION
• Purpose of Version Control
• Popular Version control tools
• Git Distribution Version Control
• Terminologies
• Git Workflow
• Git Architecture
MODULE 2: GIT REPOSITORY and GitHub
• Git Repo Introduction
• Create New Repo with Init command
• Git Essentials: Copy & User Setup
• Mastering Git and GitHub
MODULE 3: COMMITS, PULL, FETCH AND PUSH
• Code commits
• Pull, Fetch and conflicts resolution
• Pushing to Remote Repo
MODULE 4: TAGGING, BRANCHING AND MERGING
• Organize code with branches
• Checkout branch
• Merge branches
• Editing Commits
• Commit command Amend flag
• Git reset and revert
MODULE 5: GIT WITH GITHUB AND BITBUCKET
• Creating GitHub Account
• Local and Remote Repo
• Collaborating with other developers
MODULE 1: BIG DATA INTRODUCTION
MODULE 2: HDFS AND MAP REDUCE
MODULE 3: PYSPARK FOUNDATION
MODULE 4: SPARK SQL and HADOOP HIVE
MODULE 1: TABLEAU FUNDAMENTALS
• Introduction to Business Intelligence & Introduction to Tableau
• Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
• Bar chart, Tree Map, Line Chart
• Area chart, Combination Charts, Map
• Dashboards creation, Quick Filters
• Create Table Calculations
• Create Calculated Fields
• Create Custom Hierarchies
MODULE 2: POWER-BI BASICS
• Power BI Introduction
• Basics Visualizations
• Dashboard Creation
• Basic Data Cleaning
• Basic DAX FUNCTION
MODULE 3 : DATA TRANSFORMATION TECHNIQUES
• Exploring Query Editor
• Data Cleansing and Manipulation:
• Creating Our Initial Project File
• Connecting to Our Data Source
• Editing Rows
• Changing Data Types
• Replacing Values
MODULE 4 : CONNECTING TO VARIOUS DATA SOURCES
• Connecting to a CSV File
• Connecting to a Webpage
• Extracting Characters
• Splitting and Merging Columns
• Creating Conditional Columns
• Creating Columns from Examples
• Create Data Model
MODULE 1: NEURAL NETWORKS
• Structure of neural networks
• Neural network - core concepts(Weight initialization)
• Neural network - core concepts(Optimizer)
• Neural network - core concepts(Need of activation)
• Neural network - core concepts(MSE & RMSE)
• Feed forward algorithm
• Backpropagation
MODULE 2: IMPLEMENTING DEEP NEURAL NETWORKS
• Introduction to neural networks with tf2.X
• Simple deep learning model in Keras (tf2.X)
• Building neural network model in TF2.0 for MNIST dataset
MODULE 3: DEEP COMPUTER VISION - IMAGE RECOGNITION
• Convolutional neural networks (CNNs)
• CNNs with Keras-part1
• CNNs with Keras-part2
• Transfer learning in CNN
• Flowers dataset with tf2.X(part-1)
• Flowers dataset with tf2.X(part-2)
• Examining x-ray with CNN model
MODULE 4 : DEEP COMPUTER VISION - OBJECT DETECTION
• What is Object detection
• Methods of Object Detections
• Metrics of Object detection
• Bounding Box regression
• labelimg
• RCNN
• Fast RCNN
• Faster RCNN
• SSD
• YOLO Implementation
• Object detection using cv2
MODULE 5: RECURRENT NEURAL NETWORK
• RNN introduction
• Sequences with RNNs
• Long short-term memory networks(part 1)
• Long short-term memory networks(part 2)
• Bi-directional RNN and LSTM
• Examples of RNN applications
MODULE 6: NATURAL LANGUAGE PROCESSING (NLP)
• Introduction to Natural language processing
• Working with Text file
• Working with pdf file
• Introduction to regex
• Regex part 1
• Regex part 2
• Word Embedding
• RNN model creation
• Transformers and BERT
• Introduction to GPT (Generative Pre-trained Transformer)
• State of art NLP and projects
MODULE 7: PROMPT ENGINEERING
• Introduction to Prompt Engineering
• Understanding the Role of Prompts in AI Systems
• Design Principles for Effective Prompts
• Techniques for Generating and Optimizing Prompts
• Applications of Prompt Engineering in Natural Language Processing
MODULE 8: REINFORCEMENT LEARNING
• Markov decision process
• Fundamental equations in RL
• Model-based method
• Dynamic programming model free methods
MODULE 9: DEEP REINFORCEMENT LEARNING
• Architectures of deep Q learning
• Deep Q learning
• Reinforcement Learning Projects with OpenAI Gym
MODULE 10: Gen AI
• Gan introduction, Core Concepts, and Applications
• Core concepts of GAN
• GAN applications
• Building GAN model with TensorFlow 2.X
• Introduction to GPT (Generative Pre-trained Transformer)
• Building a Question answer bot with the models on Hugging Face
MODULE 11: Gen AI
• Introduction to Autoencoder
• Basic Structure and Components of Autoencoders
• Types of Autoencoders: Vanilla, Denoising, Variational, Sparse, and Convolutional Autoencoders
• Training Autoencoders: Loss Functions, Optimization Techniques
• Applications of Autoencoders: Dimensionality Reduction, Anomaly Detection, Image
The scope of Artificial Intelligence in Hyderabad's job market is rapidly expanding, with increasing demand for skilled professionals across IT, healthcare, finance, and startups driven by the city's thriving tech ecosystem.
Why should you pursue an Artificial Intelligence course in Hyderabad because the city offers top-tier training institutes, access to expert mentors, and abundant job opportunities in a rapidly growing AI-driven tech industry.
An AI course in Hyderabad teaches key skills such as Python programming, machine learning, deep learning, natural language processing (NLP), data analysis, and real-time project development.
Anyone interested in AI, including students, working professionals, career changers, and entrepreneurs from both technical and non-technical backgrounds, can enroll in the Artificial Intelligence training program in Hyderabad.
An AI course in Hyderabad typically spans 3 to 9 months and costs between INR 70,000 to INR 2,50,000, depending on the program's depth, training mode (online/offline/blended), course add-ons, and offers. EMI and scholarships may also be available.
Some of the technical skills that would prove advantageous in learning an Artificial Intelligence course are:-
Knowledge of Mathematics and Statistics.
Knowledge of Algorithms.
Knowledge of programming languages- C, C++, Java
Knowledge of Neural Networks
Knowledge of Natural Language Processing- NLP Libraries
The demand for an Artificial Intelligence course in Hyderabad is soaring, with over 2,000 AI-related job openings in 2025 and a 35–40% annual growth rate in AI roles across industries such as IT, healthcare, finance, and startups
After completing an AI course in Hyderabad, you can pursue career opportunities as an AI Engineer, Machine Learning Engineer, Data Scientist, NLP Engineer, Deep Learning Specialist, or Business Intelligence Analyst in top tech companies and startups.
The AI program teaches tools and technologies such as Python, TensorFlow, Keras, Scikit-learn, Pandas, NumPy, OpenCV, NLTK, and cloud platforms like AWS or Google Cloud.
Industries such as IT, healthcare, finance, e-commerce, pharmaceuticals, and smart city infrastructure in Hyderabad are actively hiring AI professionals.
No, prior coding knowledge is not mandatory, as most AI courses in Hyderabad include beginner-friendly programming training, especially in Python.
Hyderabad is becoming a growing hub for AI due to its strong IT ecosystem, government-backed tech initiatives, availability of skilled talent, and presence of multinational companies.
Artificial Intelligence enhances productivity, enables automation, improves decision-making, personalizes user experiences, and solves complex problems across various sectors.
Machine learning is a core subset of AI that allows systems to learn from data and make decisions or predictions without explicit programming.
Computer vision enables AI systems to interpret, analyze, and make decisions based on visual data like images and videos.
AI significantly impacts healthcare, finance, education, transportation, retail, manufacturing, entertainment, and cybersecurity.
Generative AI refers to AI models that create content such as text, images, audio, and code, and it's used in industries like media, marketing, healthcare, design, and software development.
Python is the most widely used and recommended programming language for AI due to its simplicity and extensive library support.
While not compulsory at the start, learning to code is essential for developing and implementing AI models effectively.
The future of artificial intelligence promises exponential growth in automation, personalization, and innovation across every major industry, transforming how we live and work.
DataMites provides globally recognized AI certification accredited by IABAC upon course completion.
DataMites is preferred for its expert trainers, comprehensive curriculum, hands-on projects, and strong placement support.
Yes, DataMites offers internship opportunities to provide practical industry experience during the AI course.
Yes, DataMites provides flexible EMI plans to make Artificial Intelligence training in Hyderabad affordable for all learners.
Yes, DataMites offers free trial classes so prospective students can experience the training before enrolling.
Yes. DataMites offers internship opportunities for the Artificial Intelligence course which helps you to get exposure, understand and implement the concepts learned in the course to build AI models for solving real-world problems. DataMites provides 10 Capstone projects and 1 client project for the Artificial Intelligence course.
Yes. You will learn Deep Learning as a part of the AI Engineer course. It includes - Layers, Loss Function, Optimization, Model Training, and Evaluation, etc.
Yes. You will learn Computer Vision as a part of the Artificial Intelligence course. It includes - Convolutional Neural Networks, CNN with KERAS, Transfer Learning, etc.
Yes. You will learn Neural Networks as a part of the Artificial Intelligence course. It includes - Core Concepts of Neural Networks, Structure of Neural Networks, Back Propagation, etc.
The Artificial Intelligence course offered by DataMites in Hyderabad covers the following topics:-
Artificial Intelligence Foundation.
Machine Learning
Tensorflow
Core Learning Algorithms
Neural Networks
Natural Language Processing(NLP)
Deep Computer Vision- Convolutional Neural Networks
Reinforcement Learning.
The duration of the Artificial Intelligence course provided by DataMites in Hyderabad is 6 months with 120 hrs of live online training conducted by industry experts.
Artificial Intelligence is a vast subject for study, it is a mix of Statistics and Computer Science. DataMites in Hyderabad offers quality training sessions in Artificial Intelligence, Machine Learning, etc. The Artificial Intelligence courses provided by DataMites in Hyderabad are exclusively designed in tune with the current industry requirements. Also with many projects to work on, under the mentoring of industry experts.
DataMites offers an Artificial Intelligence course in Hyderabad in three different modes. The Live Virtual/Online and Classroom training is offered at a fee/cost of Rs 99000/-, and the Self Learning mode is offered at Rs 69000/-.
The registrations cancelled within 48 hrs of enrollment will be refunded in full. The processing time of the refund is within 30 days, from the date of the receipt of the cancellation request
You have access to the online study materials from 6 months up to 1 year.
DataMites accepts all the online payments(Debit/Credit)for the AI course in Hyderabad through Razor pay. If you opt to pay through your credit card there will be an EMI option. DataMites collect token advance during the time of registration and the remaining payment should be settled in full before the completion of the course.
All the online sessions are recorded. If you happen to miss a session you can access the online recording.
Yes. The Artificial Intelligence certification exam fee is included in the total course fee. Therefore once you are registered for a course, you are also eligible to attend the exam.
Yes. You will learn Natural Language Processing(NLP) as a part of the Artificial Intelligence course. It includes - The Basics of Natural Language Processing, Integer Coding, Word Embedding, and Bag Of Words.
Yes. One of the courses out of the bundle of AI course talks about Reinforcement Learning. It includes- Markov Decision Process, Fundamental Equations in Reinforcement Learning.
Yes. One of the courses out of the bundle of AI course talks about Tensorflow. It includes-Basics of Tensorflow, Installation and Basic Operation in Tensorflow, Tensorflow 2.0 Eager Mode.
Yes. One of the courses out of the bundle of AI course talks about Machine Learning. It includes-Basics of Machine Learning, Mathematics for Machine Learning.
Yes. One of the courses out of the bundle of AI course talks about Python. It includes-
Yes, the Artificial Intelligence Engineer course provided by DataMites comprises a topic on Machine Learning in the syllabus. Therefore when you learn the AI course, you also get an opportunity to learn Machine Learning. The Machine Learning topics covered are:-
Machine Learning Overview, Mathematics for Machine Learning, Advanced Machine Learning Concepts, etc.
Yes. DataMites will provide you with a course completion certificate after you clear the AI certification examination.
The AI course offered by DataMites in Hyderabad includes 10 capstone projects and 1 client project.
The mode of training offered by DataMites in Hyderabad is primarily online. However, classroom training can be made available in Hyderabad, if there is adequate demand for the same.
DataMites is a global institute for Artificial Intelligence education. It has a history of training for more than 15000 candidates. The syllabus provided by DataMites in Hyderabad is exclusively designed in tune with the current industry trends. The following makes DataMites unique from others:-
Globally Recognised Certification- IABAC
Experienced Trainers
Industry aligned courses
Internship Opportunities
Career Guidance
More than 15000 certified learners
DataMites provides Flexi Pass, which gives you the privilege to attend unlimited batches in a year. The Flexi Pass is specific to one particular course. Therefore if you have a Flexi pass for a particular course of your choice, you will be able to attend any number of sessions of that course. It is to be noted that a Flexi pass is valid for a particular period.
The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -
The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.
No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.