4.9 (19,031) reviews 154,000
81,900*
NO COST EMI : 13,650 p.m for 6 Months
*Offer valid till 16th November 2025
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
Enrolling at DataMites was one of the best decisions I made for my career. Coming from a freelance writing background with no prior technical experience, I was initially unsure about stepping into the field of data analytics. But DataMites made the transition smooth and achievable. I’d highly recommend them to anyone looking for a guided, industry-ready data analytics course. A true DataMites success story!
To learn Artificial Intelligence, students need a combination of analytical thinking, programming knowledge, and mathematical understanding. Basic skills in Python, statistics, and linear algebra are essential since they form the foundation of AI algorithms and models. Knowledge of data analysis, problem-solving, and logical reasoning also helps in understanding AI concepts better.
The cost of an Artificial Intelligence Engineer course in Bangalore typically ranges between INR 40,000 and INR 2,00,000, depending on the institute, course duration, and level of training.
The main objective of AI training in Bangalore is to equip learners with the technical and analytical skills required to design intelligent systems and machine learning models. Students learn how to collect, process, and analyze data, apply predictive algorithms, and develop AI-driven applications that solve real-world problems.
Learning Artificial Intelligence in Bangalore offers multiple benefits from access to top training institutes to exposure to India’s biggest tech ecosystem. As the Silicon Valley of India, Bangalore is home to leading IT companies, startups, and AI research hubs, offering abundant career opportunities.
Bangalore offers a wide range of job roles in Artificial Intelligence, including AI Engineer, Data Scientist, Machine Learning Engineer, NLP Specialist, and Computer Vision Expert.
The Artificial Intelligence market in Bangalore is booming, with increasing adoption of machine learning, natural language processing, and generative AI across industries. Organizations are investing heavily in AI to automate operations, improve efficiency, and enhance customer experience.
The average salary of an Artificial Intelligence Engineer in Bangalore ranges between INR 7 LPA and INR 18 LPA, depending on experience, skill level, and company size.
The duration of an Artificial Intelligence course in Bangalore usually ranges from 4 to 9 months, depending on the program structure. Fast-track courses focus on core AI concepts, while comprehensive master programs may take up to a year, covering advanced machine learning, deep learning, and real-world projects.
Learning Artificial Intelligence opens doors to high-demand, high-paying job roles across industries. Professionals from IT, engineering, or even non-technical backgrounds can transition into data-driven careers by mastering AI tools and techniques.
Bangalore stands as India’s top hub for AI due to its thriving technology ecosystem, global IT presence, and strong research infrastructure. It hosts a dense network of MNCs, AI startups, and innovation labs, providing endless learning and career opportunities.
While prior programming knowledge is helpful, it is not mandatory. Most AI courses in Bangalore start from the basics of Python and programming logic, helping students gradually build their coding proficiency.
Yes, non-technical professionals can definitely learn AI. Many training programs are designed for beginners without a computer science background. With structured learning, mentorship, and hands-on projects, individuals from finance, management, or business fields can transition into AI-related careers successfully.
AI courses in Bangalore typically cover tools like Python, TensorFlow, PyTorch, Keras, Scikit-learn, Pandas, NumPy, and SQL. Students also gain exposure to AI applications in Natural Language Processing (NLP), Deep Learning, Computer Vision, and Generative AI models.
Artificial Intelligence is transforming the nature of work rather than outright replacing human jobs. While AI automates repetitive, routine, and data-intensive tasks, it also creates new roles that require human intelligence, creativity, and decision-making.
AI is closely related to Data Science and Cloud Computing but focuses more on building intelligent models that learn and make decisions. Data Science involves analyzing and interpreting data, while Cloud Computing provides the infrastructure for deploying AI solutions.
Yes, mathematics is a key component of AI. Topics such as statistics, probability, linear algebra, and calculus form the foundation for understanding algorithms and model behavior. However, most courses simplify these concepts, teaching them practically through coding exercises, so even beginners can grasp them easily.
AI skills are among the most sought-after in Bangalore’s IT sector. Companies use AI for automation, analytics, and predictive solutions, making professionals with AI expertise valuable assets. Learning AI enhances employability, helps secure senior technical roles, and ensures long-term career growth in a rapidly evolving job market.
The most important programming languages for AI professionals are Python, R, and SQL. Python is widely used for building AI models and machine learning algorithms, while R is valuable for statistical analysis. SQL is essential for data handling and querying large datasets, making these three languages the foundation for any AI professional.
Anyone with a basic understanding of computers and a keen interest in data-driven technology can join an AI course. Students, graduates, IT professionals, engineers, and even non-technical individuals can enroll.
AI may seem complex initially, but with structured learning and proper guidance, it becomes manageable. Many courses in Bangalore follow a step-by-step approach starting with programming basics, followed by machine learning, and finally deep learning making it easy for beginners to learn and apply concepts effectively.
DataMites conducts both morning and evening classes for Artificial Intelligence courses in Bangalore. You can opt between the two as per your convenience.
After completing the AI course at DataMites, students receive a globally recognized IABAC and NASSCOM FutureSkills, validating their skills in machine learning, deep learning, and AI applications.
DataMites is a preferred choice because of its industry-aligned curriculum, experienced instructors, and focus on practical learning. The courses are designed to cater to both beginners and professionals, covering AI, ML, NLP, deep learning, and real-time projects.
Yes, DataMites offers project-based learning and internship opportunities as part of an Artificial Intelligence course in Bangalore. Students get hands-on experience by working on real-world AI projects, which enhances their practical understanding and makes them more employable in competitive job markets.
DataMites provides flexible EMI options to make Artificial Intelligence courses affordable for students and working professionals. This allows learners to pay the course fees in installments while continuing their training without financial stress.
Yes, DataMites allows prospective students to attend a demo or trial class. This gives learners a clear understanding of the course structure, teaching methodology, and hands-on training approach before they commit to enrollment.
The cost of the Artificial Intelligence course at DataMites Bangalore typically ranges between INR 40,000 to INR 1,54,000, depending on the program level and batch type (online, weekend, or weekday). The course fee covers training, study materials, project work, and certification, providing comprehensive learning value.
Yes, DataMites offers placement assistance and career support for AI course students. This includes interview preparation, resume building, and connecting students with potential employers, helping learners secure roles as AI Engineers, Data Scientists, Machine Learning Engineers, and related positions.
DataMites has a transparent refund policy, allowing students to request refunds within a specified period before the course starts. The policy ensures that any cancellations are handled professionally, with applicable deductions clearly communicated, making it hassle-free for learners.
Students receive comprehensive study materials, including lecture notes, eBooks, case studies, Python and SQL code snippets, and project guidelines. These resources complement live sessions and provide a strong reference for learning AI concepts and implementing them in real-world projects.
DataMites Artificial Intelligence courses are taught by industry-experienced trainers who have extensive knowledge in AI, machine learning, and data science. The instructors focus on practical application, project guidance, and problem-solving techniques, ensuring students gain both theoretical understanding and hands-on skills.
Yes, DataMites emphasizes project-based learning. Students work on real-time AI projects involving machine learning, NLP, and deep learning applications, which are included in the certification process. This hands-on experience strengthens understanding and improves employability.
The Artificial Intelligence course in Bangalore duration at DataMites typically ranges from 4 to 9 months, depending on the batch type and mode of learning.
Yes, DataMites provides flexible make-up classes and recorded sessions. Students who miss live sessions can access recordings and extra sessions to ensure they stay on track without losing any learning content.
Students gain skills in Python, SQL, machine learning, deep learning, NLP, computer vision, data analysis, and AI model deployment. They also learn statistical analysis, algorithm selection, hyperparameter tuning, and real-world problem-solving, making them industry-ready AI professionals.
DataMites Artificial Intelligence Institute in Bangalore is conveniently situated at BTM Layout, Starttopia, Ground Floor, Vinir Tower No. 6, 100ft Main Road, 1st Stage, Bengaluru, Karnataka 560068, making it easily accessible for both students and working professionals.
The Artificial Intelligence course at DataMites Bangalore is open to learners from various backgrounds and is conveniently accessible for residents of nearby localities, including Mico Layout (560076), NS Palya (560078), and Sunshine Colony (560076), making it ideal for students and working professionals in and around BTM Layout.
The Flexi Pass is a flexible learning option offered by DataMites for online AI courses. It allows students to access recorded sessions, live classes, and study materials at their convenience, enabling them to learn at their own pace while balancing work or other commitments.
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.
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