ARTIFICIAL INTELLIGENCE CERTIFICATION AUTHORITIES

Artificial Intelligence Course Features

ARTIFICIAL INTELLIGENCE COURSE LEAD MENTORS

ARTIFICIAL INTELLIGENCE COURSE FEE IN MADHAPUR, HYDERABAD

Live Virtual

Instructor Led Live Online

76,000
47,900

  • IABAC® & DMC Certification
  • 9-Month | 780 Learning Hours
  • 100-Hour Live Online Training
  • 10 Capstone & 1 Client Project
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Blended Learning

Self Learning + Live Mentoring

46,000
28,900

  • Self Learning + Live Mentoring
  • IABAC® & DMC Certification
  • 1 Year Access To Elearning
  • 10 Capstone & 1 Client Project
  • Job Assistance
  • 24*7 Learner assistance and support

Classroom

In - Person Classroom Training

76,000
54,900

  • IABAC® & DMC Certification
  • 9-Month | 780 Learning Hours
  • 100-Hour Classroom Sessions
  • 10 Capstone & 1 Client Project
  • Cloud Lab Access
  • Internship + Job Assistance

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The entire training includes real-world projects and highly valuable case studies.

IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.

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WHY DATAMITES FOR ARTIFICIAL INTELLIGENCE TRAINING

Why DataMites Infographic

SYLLABUS OF ARTIFICIAL INTELLIGENCE CERTIFICATION COURSE

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 

  • Big Data Overview
  • Five Vs of Big Data
  • What is Big Data and Hadoop
  • Introduction to Hadoop
  • Components of Hadoop Ecosystem
  • Big Data Analytics Introduction

MODULE 2: HDFS AND MAP REDUCE 

  • HDFS – Big Data Storage
  • Distributed Processing with Map Reduce
  • Mapping and reducing  stages concepts
  • Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort

MODULE 3: PYSPARK FOUNDATION 

  • PySpark Introduction
  • Spark Configuration
  • Resilient distributed datasets (RDD)
  • Working with RDDs in PySpark
  • Aggregating Data with Pair RDDs

MODULE 4: SPARK SQL and HADOOP HIVE 

  • Introducing Spark SQL
  • Spark SQL vs 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

OFFERED ARTIFICIAL INTELLIGENCE COURSES IN MADHAPUR

ARTIFICIAL INTELLIGENCE TRAINING COURSE REVIEWS

ABOUT ARTIFICIAL INTELLIGENCE COURSE IN MADHAPUR

Artificial Intelligence course in Madhapur opens doors to a world of opportunities, equipping individuals with the skills and knowledge to succeed in the rapidly evolving field of AI. The market size is expected to show an annual growth rate (CAGR 2025-2031) of 26.95%, resulting in a market volume of US$309.70bn by 2031. according to a Statista report. Additionally, the salary of an artificial intelligence engineer in Madhapur ranges from INR 11,60,174 per year according to a Glassdoor report.

DataMites provides a well-recognized Artificial Intelligence Engineer program, accredited by both IABAC and NASSCOM FutureSkills, ensuring global training standards. This 9-month course is delivered at the DataMites offline center in Madhapur, Hyderabad, offering a blend of in-person instruction and practical learning. Tailored for students and professionals alike, the program includes real-time projects, internship opportunities, and customized mentoring. With dedicated placement support, participants are equipped to launch successful careers in the dynamic field of Artificial Intelligence Course in Hyderabad.

DataMites presents key features for its Artificial Intelligence Course in Madhapur, encompassing:

Experienced Instructors: Ashok Veda, the founder of the Artificial Intelligence startup Rubixe, leads our faculty, bringing extensive mentoring experience to over 20,000 individuals in the fields of data science and Artificial Intelligence.

Comprehensive Curriculum: Our Artificial Intelligence courses delve into fundamental topics, providing a thorough understanding of the subject.

Recognized Certifications: Attain industry-recognized certifications from IABAC and NASSCOM FutureSkills, enhancing your professional credibility.

Flexible Learning Options: Opt for live online classes, self-paced learning, or offline artificial intelligence training in Madhapur to accommodate your schedule.

Hands-on Projects: Gain practical insights through hands-on projects that utilize real-world data, enhancing your practical experience.

Internship Opportunities: Apply your acquired skills in real-world settings through our Artificial Intelligence internships, gaining valuable industry exposure.

Placement Support: Our dedicated team provides guidance, support, and job references to propel your Artificial Intelligence career forward.

Comprehensive Learning Materials: Access hardcopy learning materials and books for continuous reference throughout your journey in Artificial Intelligence.

Affordable Pricing and Scholarships: Access quality Artificial Intelligence education at reasonable prices, with available scholarships for eligible candidates.

DataMites Offline Center – Hyderabad

Artificial Intelligence Courses in Madhapur through in-person training. Join us at our dedicated offline training center located in the heart of Madhapur. 313, 4th Floor, Ayyappa Society Main Rd, Ayyappa Society, Megha Hills, Mega Hills, Madhapur, Hyderabad, Telangana 500081.

The DataMites Artificial Intelligence Course in Madhapur is ideal for fresh graduates, working professionals, career switchers, and anyone with an interest in data. With a focus on practical, industry-relevant skills, the course is easily accessible to learners from key localities including Hitec City (500081), Gachibowli (500032), Kavuri Hills (500033), Kothaguda (500084), Anjaneya Nagar (500072), Kondapur (500084), Kokapet (500075), Mehdipatnam (500028), Miyapur (500049), Dammaiguda (500083), Attapur (500048), Balapur (500005), Manikonda (500089), Jubilee Hills (500033), Banjara Hills (500034), Kukatpally (500072), and Ameerpet (500018), all of which can conveniently access the Madhapur center for offline classes at DataMites.

Artificial Intelligence Course with Internships in Madhapur

At DataMites, our Artificial Intelligence Courses in Madhapur, which include internships, seamlessly combine academic learning with practical training. This unique approach offers students valuable hands-on experience in AI, refining their skills and equipping them for successful careers in the dynamic fields of AI and Machine Learning Course.

Artificial Intelligence Course with Placement in Madhapur

DataMites offers AI courses with placement support in Madhapur, ensuring a seamless transition from education to employment. Our initiatives align students with the evolving AI job market, preparing them for successful careers in both AI and machine learning. Through these integrated services, DataMites equips students to be industry-ready AI and machine learning courses, well-prepared for the challenges and opportunities in the field.

Madhapur, a bustling IT and business district in Hyderabad, India, is known for its vibrant commercial landscape and technological advancements. The scope of artificial intelligence in Madhapur is burgeoning, driving innovation across various industries, particularly in the thriving IT sector, where AI applications are reshaping business processes and enhancing technological capabilities. The demand for skilled AI professionals in Madhapur reflects the city's commitment to leveraging cutting-edge technologies for sustainable growth. Join DataMites Artificial Intelligence training in Madhapur as a significant step toward attaining success in the field.

ABOUT DATAMITES ARTIFICIAL INTELLIGENCE COURSE IN MADHAPUR

Artificial Intelligence (AI) is the simulation of human intelligence in machines, enabling them to perform tasks such as learning, reasoning, problem-solving, and decision-making. It forms the backbone of technologies like machine learning, natural language processing, robotics, and computer vision.

The duration of AI courses can vary. Short-term certifications may take 3 to 9 months, while more extensive diploma or postgraduate programs may last 1 to 2 years.

The cost of AI courses in Madhapur varies depending on the institute, course duration, and curriculum, but typically ranges between ₹50,000 to ₹2,00,000 for comprehensive programs that include hands-on training and placement assistance.

To pursue a career in Artificial Intelligence in Madhapur, candidates typically need a strong foundation in mathematics, statistics, and programming. A bachelor's degree in computer science, engineering, data science, or a related field is often required.

The average monthly salary for an AI engineer in Hyderabad ranges between ₹60,000 to ₹2,00,000, depending on experience, skills, and job role. Freshers can expect to start around ₹6–8 LPA, while experienced professionals can command significantly higher packages.

AI certification courses in Madhapur are ideal for students, working professionals, IT graduates, software engineers, data analysts, and anyone interested in building a career in AI, regardless of prior technical expertise.

There is significant demand for Artificial Intelligence professionals, with the U.S. Bureau of Labor Statistics (BLS) projecting a 15% growth in the computer and information technology sector, encompassing AI jobs, from 2021 to 2031.

Key technical skills include:

  • Programming: Python, R, or Java
  • Mathematics: Linear algebra, calculus, and probability
  • Statistics and Data Analysis
  • Machine Learning algorithms
  • Working with tools like TensorFlow, Keras, and scikit-learn

The future scope of AI is vast and ever-expanding. From autonomous vehicles and intelligent healthcare systems to smart cities and financial automation, AI is revolutionizing every sector. Professionals with AI expertise will be at the forefront of this transformation.

Yes, many institutes in Madhapur offer placement support, internships, and real-world project experience, enabling recent graduates to enter the AI job market with confidence.

Yes, AI is one of the highest-paid domains in the tech industry. Due to the skill gap and high demand, AI engineers, data scientists, and machine learning specialists often receive lucrative salary packages, particularly in metropolitan cities like Hyderabad.

To become an AI engineer, follow these steps:

  • Gain a strong foundation in mathematics, statistics, and programming (preferably Python).
  • Learn core AI concepts such as machine learning, deep learning, and neural networks.
  • Work on real-world projects and build a portfolio.
  • Earn relevant certifications and stay updated with emerging trends and tools.

Yes, coding is essential for Artificial Intelligence. Most AI development is done using programming languages such as Python, R, and Java. Python is particularly popular due to its simplicity and vast ecosystem of AI libraries like TensorFlow, Keras, and PyTorch.

The best beginner-level AI course covers foundational topics like Python programming, basic machine learning, data preprocessing, and introductory AI algorithms. Look for courses that offer hands-on exercises, clear explanations, and gradual progression into more advanced areas.

After completing AI training, learners can pursue roles such as AI Engineer, Machine Learning Engineer, Data Scientist, NLP Specialist, Robotics Engineer, Computer Vision Engineer, and AI Research Analyst, among others.

Most AI programs include hands-on training with tools and technologies such as:

  • Programming Languages: Python, R
  • Frameworks: TensorFlow, PyTorch, Keras
  • Libraries: Scikit-learn, NumPy, Pandas, OpenCV
  • Platforms: Jupyter Notebook, Google Colab

Madhapur is a major tech hub with numerous AI-driven companies, startups, and research institutions. Learning AI here gives you exposure to real-world industry practices, access to expert trainers, and networking opportunities.

An AI course equips you with in-demand technical skills, hands-on project experience, and industry-relevant knowledge that enables a smooth transition into data science, analytics, or AI-related roles.

Artificial Intelligence can be challenging due to its reliance on mathematics, programming, and logical reasoning. However, with the right learning path and support, even beginners can master it. A structured course, real-world practice, and consistent effort make it manageable and rewarding.

Yes, Artificial Intelligence is considered one of the most promising and future-proof careers today. With growing applications across industries like healthcare, finance, transportation, and e-commerce, the demand for skilled AI professionals continues to rise globally, including in tech hubs like Hyderabad.

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FAQ'S OF ARTIFICIAL INTELLIGENCE TRAINING IN MADHAPUR

The cost of the Artificial Intelligence course at DataMites in Madhapur, Hyderabad, generally falls between INR 60,000 and INR 1,50,000, varying based on the chosen training mode online, offline, or blended.

Yes, prospective students can attend a free demo or trial session to experience the teaching methodology, course content, and faculty approach before enrolling.

Absolutely. The AI course curriculum includes hands-on projects, real-time case studies, and practical assignments based on industry-relevant scenarios.

DataMites provides globally recognized AI certification accredited by IABAC upon course completion.

DataMites offers flexible training formats, including:

  • Online Live Training
  • Offline Classroom Training at the Madhapur center
  • Self-paced Learning with recorded lectures and study materials
  • Blended Learning combining self-paced and instructor-led sessions

Yes, offline classroom sessions are conducted at the DataMites training center in Madhapur, one of Hyderabad's key IT and learning hubs. It offers convenient access and a collaborative in-person learning environment.

Yes, the course includes placement assistance, including resume building, mock interviews, and job referrals to hiring partners in Hyderabad and beyond. Dedicated career support is provided to help students transition smoothly into AI roles.

Students receive:

  • One-on-one mentorship from AI professionals
  • Career counseling and resume building
  • Support in building a professional portfolio

Yes, internship opportunities are offered to eligible students as part of the training program. These internships help in applying learned concepts to real-world scenarios, enhancing credibility and work experience.

The Artificial Intelligence Course at DataMites in Madhapur is ideal for fresh graduates, working professionals, career switchers, and anyone with an interest in data. Learners from nearby localities such as Hitec City (500081), Gachibowli(500032), Kavuri hills(500033),  Kothaguda(500084), Anjaneya Nagar(500072), Kondapur(500084), Kokapet(500075), Mehdipatnam(500028), Miyapur(500049), Dammaiguda(500083), AttaPur(500048), Balapur(500005), Manikonda(500089), Jubilee Hills(500033), Banjara Hills(500034), Kukatpally(500072), and Ameerpet (500018) can conveniently access the Madhapur center for offline classes at DataMites.

The Artificial Intelligence course fee at DataMites Madhapur typically ranges between INR 70,000 and INR 1,50,000, depending on the program level.

Yes, DataMites provides flexible EMI plans to make Artificial Intelligence training in Hyderabad affordable for all learners.

DataMites has a transparent refund policy which varies based on the timing of cancellation and course terms.

DataMites operates a center in Madhapur, Hyderabad, strategically located in 313, 4th Floor, Ayyappa Society Main Rd, Ayyappa Society, Megha Hills, Mega Hills, Madhapur, Hyderabad, Telangana 500081.

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: -

  • 1. Job connect
  • 2. Resume Building
  • 3. Mock interview with industry experts
  • 4. Interview questions

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.

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