Start learning AI at our top Artificial Intelligence Training Institute in OMR. Our course covers important AI topics like machine learning and computer vision. With expert guidance, you’ll learn practical skills for a successful AI career. Join our Artificial Intelligence Course with Certifications and Placements to start your career strong. Our curriculum is designed to prepare you for the AI field, with hands-on learning and content that matches what the industry needs. Enroll now in our Artificial Intelligence Training in OMR to start your AI career with confidence.
Artificial Intelligence Training in OMR
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2 Months
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What this Course Includes?
- Technology Training
- Aptitude Training
- Learn to Code (Codeathon)
- Real Time Projects
- Learn to Crack Interviews
- Panel Mock Interview
- Unlimited Interviews
- Life Long Placement Support
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Course Syllabus
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Breakdown of Artificial Intelligence Training in OMR Fee and Batches
Hands On Training
3-5 Real Time Projects
60-100 Practical Assignments
3+ Assessments / Mock Interviews
April 2025
Week days
(Mon-Fri)
Online/Offline
2 Hours Real Time Interactive Technical Training
1 Hour Aptitude
1 Hour Communication & Soft Skills
(Suitable for Fresh Jobseekers / Non IT to IT transition)
April 2025
Week ends
(Sat-Sun)
Online/Offline
4 Hours Real Time Interactive Technical Training
(Suitable for working IT Professionals)
Save up to 20% in your Course Fee on our Job Seeker Course Series
Syllabus of Artificial Intelligence Training in OMR
Deep Learning: A revolution in Artificial Intelligence
- Limitations of Machine Learning
What is Deep Learning?
- Need for Data Scientists
- Foundation of Data Science
- What is Business Intelligence
- What is Data Analysis
- What is Data Mining
What is Machine Learning? Analytics vs Data Science
- Value Chain
- Types of Analytics
- Lifecycle Probability
- Analytics Project Lifecycle
- Advantage of Deep Learning over Machine learning
- Reasons for Deep Learning
- Real-Life use cases of Deep Learning
- Review of Machine Learning
Data
- Basis of Data Categorization
- Types of Data
- Data Collection Types
- Forms of Data & Sources
- Data Quality & Changes
- Data Quality Issues
- Data Quality Story
- What is Data Architecture
- Components of Data Architecture
- OLTP vs OLAP
- How is Data Stored?
Big Data
- What is Big Data?
- 5 Vs of Big Data
- Big Data Architecture
- Big Data Technologies
- Big Data Challenge
- Big Data Requirements
- Big Data Distributed Computing & Complexity
- Hadoop
- Map Reduce Framework
- Hadoop Ecosystem
Data Science Deep Dive
- What Data Science is
- Why Data Scientists are in demand
- What is a Data Product
- The growing need for Data Science
- Large Scale Analysis Cost vs Storage
- Data Science Skills
- Data Science Use Cases
- Data Science Project Life Cycle & Stages
- Data Acuqisition
- Where to source data
- Techniques
- Evaluating input data
- Data formats
- Data Quantity
- Data Quality
- Resolution Techniques
- Data Transformation
- File format Conversions
- Annonymization
Python
- Python Overview
- About Interpreted Languages
- Advantages/Disadvantages of Python pydoc.
- Starting Python
- Interpreter PATH
- Using the Interpreter
- Running a Python Script
- Using Variables
- Keywords
- Built-in Functions
- StringsDifferent Literals
- Math Operators and Expressions
- Writing to the Screen
- String Formatting
- Command Line Parameters and Flow Control.
- Lists
- Tuples
- Indexing and Slicing
- Iterating through a Sequence
- Functions for all Sequences
Operators and Keywords for Sequences
- The xrange() function
- List Comprehensions
- Generator Expressions
- Dictionaries and Sets.
Numpy & Pandas
- Learning NumPy
- Introduction to Pandas
- Creating Data Frames
- GroupingSorting
- Plotting Data
- Creating Functions
- Slicing/Dicing Operations.
Deep Dive – Functions & Classes & Oops
- Functions
- Function Parameters
- Global Variables
- Variable Scope and Returning Values. Sorting
- Alternate Keys
- Lambda Functions
- Sorting Collections of Collections
- Classes & OOPs
Statistics
- What is Statistics
- Descriptive Statistics
- Central Tendency Measures
- The Story of Average
- Dispersion Measures
- Data Distributions
- Central Limit Theorem
- What is Sampling
- Why Sampling
- Sampling Methods
- Inferential Statistics
- What is Hypothesis testing
- Confidence Level
- Degrees of freedom
- what is pValue
- Chi-Square test
- What is ANOVA
- Correlation vs Regression
- Uses of Correlation & Regression
Introduction
- ML Fundamentals
- ML Common Use Cases
- Understanding Supervised and Unsupervised Learning Techniques
Clustering
- Similarity Metrics
- Distance Measure Types: Euclidean, Cosine Measures
- Creating predictive models
- Understanding K-Means Clustering
- Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
- Case study
Implementing Association rule mining
- What is Association Rules & its use cases?
- What is Recommendation Engine & it’s working?
- Recommendation Use-case
- Case study
Decision Tree Classifier
- How to build Decision trees
- What is Classification and its use cases?
- What is Decision Tree?
- Algorithm for Decision Tree Induction
- Creating a Decision Tree
- Confusion Matrix
- Case study
Random Forest Classifier
- What is Random Forests
- Features of Random Forest
- Out of Box Error Estimate and Variable Importance
- Case study
Naive Bayes Classifier.
- Case study
Problem Statement and Analysis
- Various approaches to solve a Data Science Problem
- Pros and Cons of different approaches and algorithms.
Linear Regression
- Case study
- Introduction to Predictive Modeling
- Linear Regression Overview
- Simple Linear Regression
- Multiple Linear Regression
Logistic Regression
- Case study
- Logistic Regression Overview
- Data Partitioning
- Univariate Analysis
- Bivariate Analysis
- Multicollinearity Analysis
- Model Building
- Model Validation
- Model Performance Assessment AUC & ROC curves
- Scorecard
Support Vector Machines
- Case Study
- Introduction to SVMs
- SVM History
- Vectors Overview
- Decision Surfaces
- Linear SVMs
- The Kernel Trick
- Non-Linear SVMs
- The Kernel SVM
Time Series Analysis
- Describe Time Series data
- Format your Time Series data
- List the different components of Time Series data
- Discuss different kind of Time Series scenarios
- Choose the model according to the Time series scenario
- Implement the model for forecasting
- Explain working and implementation of ARIMA model
- Illustrate the working and implementation of different ETS models
- Forecast the data using the respective model
- What is Time Series data?
- Time Series variables
- Different components of Time Series data
- Visualize the data to identify Time Series Components
- Implement ARIMA model for forecasting
- Exponential smoothing models
- Identifying different time series scenario based on which different Exponential Smoothing model can be applied
- Implement respective model for forecasting
- Visualizing and formatting Time Series data
- Plotting decomposed Time Series data plot
- Applying ARIMA and ETS model for Time Series forecasting
- Forecasting for given Time period
- Case Study
Machine learning algorithms Python
- Various machine learning algorithms in Python
- Apply machine learning algorithms in Python
Feature Selection and Pre-processing
- How to select the right data
- Which are the best features to use
- Additional feature selection techniques
- A feature selection case study
- Preprocessing
- Preprocessing Scaling Techniques
- How to preprocess your data
- How to scale your data
- Feature Scaling Final Project
Which Algorithms perform best
- Highly efficient machine learning algorithms
- Bagging Decision Trees
- The power of ensembles
- Random Forest Ensemble technique
- Boosting – Adaboost
- Boosting ensemble stochastic gradient boosting
- A final ensemble technique
Model selection cross validation score
- Introduction Model Tuning
- Parameter Tuning GridSearchCV
- A second method to tune your algorithm
- How to automate machine learning
- Which ML algo should you choose
- How to compare machine learning algorithms in practice
Text Mining& NLP
- Sentimental Analysis
- Case study
PySpark and MLLib
- Introduction to Spark Core
- Spark Architecture
- Working with RDDs
- Introduction to PySpark
- Machine learning with PySpark – Mllib
Deep Learning & AI
- Case Study
- Deep Learning Overview
- The Brain vs Neuron
- Introduction to Deep Learning
Introduction to Artificial Neural Networks
- The Detailed ANN
- The Activation Functions
- How do ANNs work & learn
- Gradient Descent
- Stochastic Gradient Descent
- Backpropogation
- Understand limitations of a Single Perceptron
- Understand Neural Networks in Detail
- Illustrate Multi-Layer Perceptron
- Backpropagation – Learning Algorithm
- Understand Backpropagation – Using Neural Network Example
- MLP Digit-Classifier using TensorFlow
- Building a multi-layered perceptron for classification
- Why Deep Networks
- Why Deep Networks give better accuracy?
- Use-Case Implementation
- Understand How Deep Network Works?
- How Backpropagation Works?
- Illustrate Forward pass, Backward pass
- Different variants of Gradient Descent
Convolutional Neural Networks
- Convolutional Operation
- Relu Layers
- What is Pooling vs Flattening
- Full Connection
- Softmax vs Cross Entropy
- ” Building a real world convolutional neural network
- for image classification”
What are RNNs – Introduction to RNNs
- Recurrent neural networks rnn
- LSTMs understanding LSTMs
- long short term memory neural networks lstm in python
Restricted Boltzmann Machine (RBM) and Autoencoders
- Restricted Boltzmann Machine
- Applications of RBM
- Introduction to Autoencoders
- Autoencoders applications
- Understanding Autoencoders
- Building a Autoencoder model
Tensorflow with Python
- Introducing Tensorflow
- Introducing Tensorflow
- Why Tensorflow?
- What is tensorflow?
- Tensorflow as an Interface
- Tensorflow as an environment
- Tensors
- Computation Graph
- Installing Tensorflow
- Tensorflow training
- Prepare Data
- Tensor types
- Loss and Optimization
- Running tensorflow programs
Tensorflow
- Tensors
- Tensorflow data types
- CPU vs GPU vs TPU
- Tensorflow methods
- Introduction to Neural Networks
- Neural Network Architecture
- Linear Regression example revisited
- The Neuron
- Neural Network Layers
- The MNIST Dataset
- Coding MNIST NN
Tensorflow
- Deepening the network
- Images and Pixels
- How humans recognise images
- Convolutional Neural Networks
- ConvNet Architecture
- Overfitting and Regularization
- Max Pooling and ReLU activations
- Dropout
- Strides and Zero Padding
- Coding Deep ConvNets demo
- Debugging Neural Networks
- Visualising NN using Tensorflow
- Tensorboard
Keras and TFLearn
- Transfer Learning Introduction
- Google Inception Model
- Retraining Google Inception with our own data demo
- Predicting new images
- Transfer Learning Summary
- Extending Tensorflow
- Keras
- TFLearn
- Keras vs TFLearn Comparison
Objectives of Learning Artificial Intelligence Training in OMR
The objectives of learning Artificial Intelligence Training include
- Introduction to Artificial Intelligence: Begin by grasping the basics of Artificial Intelligence, including its setup and core concepts.
- Inventory Handling: Learn to manage your server inventory effectively using Artificial Intelligence, enabling better organization and control.
- Using Artificial Intelligence Playbooks: Master the creation and execution of Artificial Intelligence playbooks, which automate tasks and system configurations.
- Working with Docker: Explore how Artificial Intelligence can interact with Docker, facilitating the management of Docker containers.
- Creating Robust Infrastructure: Discover how Artificial Intelligence can be leveraged to build resilient, highly available infrastructure.
- Automating Deployments: Learn to automate the deployment process, ensuring efficient and error-free application deployment across servers.
Reason to choose SLA for Artificial Intelligence Training in OMR
- SLA stands out as the Exclusive Authorized Training and Testing partner in Tamil Nadu for leading tech giants including IBM, Microsoft, Cisco, Adobe, Autodesk, Meta, Apple, Tally, PMI, Unity, Intuit, IC3, ITS, ESB, and CSB ensuring globally recognized certification.
- Learn directly from a diverse team of 100+ real-time developers as trainers providing practical, hands-on experience.
- Instructor led Online and Offline Training. No recorded sessions.
- Gain practical Technology Training through Real-Time Projects.
- Best state of the art Infrastructure.
- Develop essential Aptitude, Communication skills, Soft skills, and Interview techniques alongside Technical Training.
- In addition to Monday to Friday Technical Training, Saturday sessions are arranged for Interview based assessments and exclusive doubt clarification.
- Engage in Codeathon events for live project experiences, gaining exposure to real-world IT environments.
- Placement Training on Resume building, LinkedIn profile creation and creating GitHub project Portfolios to become Job ready.
- Attend insightful Guest Lectures by IT industry experts, enriching your understanding of the field.
- Panel Mock Interviews
- Enjoy genuine placement support at no cost. No backdoor jobs at SLA.
- Unlimited Interview opportunities until you get placed.
- 1000+ hiring partners.
- Enjoy Lifelong placement support at no cost.
- SLA is the only training company having distinguished placement reviews on Google ensuring credibility and reliability.
- Enjoy affordable fees with 0% EMI options making quality training affordable to all.
Highlights of The Artificial Intelligence Training in OMR
What is Artificial Intelligence?
Artificial Intelligence (AI) is a field of computer science that enables machines to perform tasks that typically require human intelligence, like recognizing images, understanding speech, and making decisions. AI systems learn from data, adapt to new inputs, and can work independently. The goal is to create machines that think, reason, and act like humans, ultimately enhancing our lives.
What are the reasons for learning Artificial Intelligence?
Learning Artificial Intelligence (AI) is beneficial for several reasons:
- Career Opportunities: AI offers many job options in various industries.
- Creativity: AI lets you develop innovative solutions.
- Problem-Solving: AI enhances your ability to solve complex problems.
- Future Technology: AI is part of the future of technology.
- Automation: AI can automate tasks, improving efficiency.
What are the prerequisites for learning Artificial Intelligence?
No specific requirements are needed to begin learning Artificial Intelligence (AI). Basic math knowledge, such as algebra, and some programming skills, particularly in Python, can be beneficial.
Our Artificial Intelligence Training in OMR is suitable for:
- Students
- Professionals seeking a career change
- IT professionals aiming to enhance their skills
- Enthusiastic programmers
- Job Seekers
What are the course fees and duration?
Our Artificial Intelligence Training in OMR costs around 18,000 INR, but the fee can vary depending on the course level (basic, intermediate, or advanced) and format (online or in-person). The training usually lasts for about 1.5 months and includes certification. For accurate details on fees, duration, and certification, please contact our training center directly.
What are some job roles related to Artificial Intelligence?
Here are some jobs related to Artificial Intelligence (AI):
- Machine Learning Engineer: Creates AI programs that learn from data.
- Data Scientist: Analyzes data to find patterns and make predictions using AI.
- AI Research Scientist: Conducts research to improve AI technologies.
- AI Solutions Architect: Designs AI systems for businesses.
- AI Product Manager: Manages the development of AI products.
- Robotics Engineer: Designs and builds AI-powered robots.
- Natural Language Processing Engineer: Develops AI for understanding and using human language.
What is the salary range for Artificial Intelligence Engineer?
A fresher in AI engineering typically earns about ₹8,00,000 annually with less than three years of experience. A mid-career AI Engineer, with 4-9 years of experience, earns an average of ₹1,51,000 per year. An experienced AI Engineer, with 10-20 years of experience, earns an average of ₹3,71,000 Lakhs per year.
List a few Artificial Intelligence real-time applications.
Here are some real-time applications of Artificial Intelligence (AI) across industries:
- Healthcare: AI is used in medical image analysis, treatment planning, and virtual health assistants.
- Finance: AI is applied in fraud detection, algorithmic trading, and customer service chatbots.
- Retail: AI powers recommendation systems, inventory management, and personalized marketing.
- Automotive: AI enables autonomous vehicles, driver-assist systems, and predictive maintenance.
- Manufacturing: AI is used for predictive maintenance, quality control, and supply chain optimization.
- Agriculture: AI is used for crop monitoring, predictive analytics for crop yield, and precision farming.
Who are our Trainers for Artificial Intelligence Training in OMR?
Our Mentors are from Top Companies like:
- Our instructors for Artificial Intelligence (AI) Training in OMR are seasoned experts with extensive knowledge of AI technologies.
- They hold advanced degrees in computer science, data science, or related fields.
- Often, our instructors have industry certifications in AI and related technologies.
- They bring practical experience from working on AI projects across various industries.
- Proficiency in programming languages such as Python is a distinguishing feature of our instructors.
- Our instructors are well-versed in AI frameworks and libraries commonly used in the industry.
- They excel in communication, ensuring effective teaching of complex AI concepts.
- Our instructors are dedicated to teaching and guiding students towards mastering AI skills.
- They possess a profound understanding of AI concepts and principles.
- Our instructors are committed to staying updated with the latest trends and advancements in AI.
What Modes of Training are available for Artificial Intelligence Training in OMR?
Offline / Classroom Training
- Direct Interaction with the Trainer
- Clarify doubts then and there
- Airconditioned Premium Classrooms and Lab with all amenities
- Codeathon Practices
- Direct Aptitude Training
- Live Interview Skills Training
- Direct Panel Mock Interviews
- Campus Drives
- 100% Placement Support
Online Training
- No Recorded Sessions
- Live Virtual Interaction with the Trainer
- Clarify doubts then and there virtually
- Live Virtual Interview Skills Training
- Live Virtual Aptitude Training
- Online Panel Mock Interviews
- 100% Placement Support
Corporate Training
- Industry endorsed Skilled Faculties
- Flexible Pricing Options
- Customized Syllabus
- 12X6 Assistance and Support
Certifications
Improve your abilities to get access to rewarding possibilities
Earn Your Certificate of Completion
Take Your Career to the Next Level with an IBM Certification
Stand Out from the Crowd with Codethon Certificate
Project Practices for Artificial Intelligence Training in OMR
Energy Management
Use AI to optimize energy use in buildings by adjusting heating, cooling, and lighting.
Environmental Monitoring
Use AI to monitor air and water quality and noise levels for environmental conservation.
Health Monitoring Wearables
Use AI to analyze data from wearable devices for personalized health recommendations.
Video Surveillance
Use AI for security cameras to detect suspicious activities or objects.
Sentiment Analysis
Use AI to analyze customer reviews or social media posts to understand sentiment.
Traffic Management
Use AI to improve traffic flow and reduce congestion in cities.
Fraud Detection
Use AI to detect fraud in financial transactions
Smart Home Automation
Use AI to control home appliances with voice commands or sensors for energy savings and convenience
Medical Image Analysis
Use AI to help doctors diagnose diseases by analyzing X-rays and MRIs.
The SLA way to Become
a Artificial Intelligence Training in OMR Expert
Enrollment
Technology Training
Realtime Projects
Placement Training
Interview Skills
Panel Mock
Interview
Unlimited
Interviews
Interview
Feedback
100%
IT Career
Placement Support for a Artificial Intelligence Training in OMR
Genuine Placements. No Backdoor Jobs at Softlogic Systems.
Free 100% Placement Support
Aptitude Training
from Day 1
Interview Skills
from Day 1
Softskills Training
from Day 1
Build Your Resume
Build your LinkedIn Profile
Build your GitHub
digital portfolio
Panel Mock Interview
Unlimited Interviews until you get placed
Life Long Placement Support at no cost
FAQs for
Artificial Intelligence Training in OMR
What is the main goal of AI?
1.
The main aim of Artificial Intelligence (AI) is to make machines smart enough to perform tasks that usually need human intelligence, like understanding speech or recognizing objects. The goal is to create AI systems that can learn, adapt, and solve problems on their own, which would help us in many ways.
Who is eligible for an AI course?
3.
Candidates with a minimum educational qualification, such as a bachelor’s degree in any field are eligible for an AI course.
Does AI need maths?
4.
Yes, math is important for AI. It helps develop algorithms and models that AI systems use. Understanding concepts like algebra, calculus, probability, and statistics is essential for working with AI.
What makes SLA Institute a good place to study Artificial Intelligence?
5.
SLA Institute stands out as an excellent AI study destination, boasting expert trainers, practical learning, industry-aligned curriculum, modern facilities, and a strategic location in OMR Chennai’s IT hub.
Is Artificial Intelligence easy to learn?
6.
Artificial Intelligence can be challenging to learn due to its complex concepts and algorithms. However, with dedication, perseverance, and the right resources, it is definitely possible to grasp and excel in AI.
How does the placement team at SLA support us?
7.
The placement team at SLA enhances your job prospects by providing comprehensive support. Whether you’re a certified student looking to switch careers or entering the workforce for the first time, you’ll receive extensive assistance through our placement services. SLA offers the following premium services as part of our placement support:
- Resume building
- Career guidance and advising
- Interview practice sessions
- Career expos
What accreditation will I get once the course is completed?
8.
Upon completion of SLA’s training, you will be awarded with globally recognized course completion certificates from SLA, renowned IBM certificates, and Codeathon certificates, validating your real-time project experience.
What are the different payment options available?
9.
We accept all sort of major payment methods like cash, credit cards (Visa, Maestro, Master card), Netbanking, etc
I have more queries?
10.
Please contact our course counselor by call or Whatsapp at +91 86818 84318. As an alternative, you can use our Website chat, Website form, or email us at [email protected]
Additional Information for
Artificial Intelligence Training in OMR
Our Artificial Intelligence Training in OMR has the best curriculum among other IT institutes ever. Our institute is located in the hub of IT companies, which creates abundance of opportunities for candidates.. Our Artificial Intelligence course syllabus will teach you topics that no other institute will teach. Enroll in our Artificial Intelligence training to explore some innovative Top project ideas for the Artificial Intelligence Course.
1.
Artificial Intelligence (AI) Impact:
Artificial Intelligence (AI) is advancing rapidly, with far-reaching implications for various industries. In healthcare, AI enhances medical image analysis, diagnosis, and treatment planning. AI also plays a key role in finance, detecting fraud, automating trading, and personalizing customer experiences.
2.
AI in Transportation and Entertainment:
In transportation, AI drives the development of autonomous vehicles, promising transformative changes in mobility. In entertainment, AI enhances gaming experiences and tailors content recommendations. Overall, AI’s evolution across industries creates new opportunities for innovation and growth, making it an exciting and impactful field.