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Artificial Intelligence Course in Chennai

Learn the nuances of Artificial Intelligence with the help of Aimore’s Artificial Intelligence course in Chennai.
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Artificial Intelligence Training - An Overview

Aimore Technologies is one of the leading providers of Artificial Intelligence courses in Chennai. Aimore’s Data Science and Artificial Intelligence courses are designed to cater to the requirements of individuals from diverse backgrounds with different levels of understanding and interest in artificial intelligence. The syllabus has been designed by industry experts with hands-on experience working on real-time projects based on Artificial Intelligence technologies. Our curriculum covers Machine Learning, Deep Learning, Data Science, python, and more. With quality hours of intensive classroom and online sessions, you’ll be ready to take your career to the next level.

With the rising popularity of Artificial Intelligence, there has never been a better time to consider a career in this field. And with Aimore Technologies, the best software training institute in Chennai offering Artificial Intelligence courses, you'll always be a step ahead.

Aimore’s Artificial Intelligence Course Features

Guaranteed Placements
Aimore ensures 100% placement for its trainees in leading multinational corporations.
Real-World Experience
Our training incorporates extensive lab sessions that simulate real-world scenarios, providing you with practical experience.
Expert Instruction
Learn the technical skills and best practices from our accredited instructors. Choose from in-person or virtual course options.
Industry-Recognized Certification
Enhance your credibility with a credential recognised throughout the industry. You can earn this certification virtually via online proctoring or in a testing centre.
Flexible Learning Path
Follow a recommended learning plan tailored for a specific domain or job role. Or choose to skip around in our flexible, self-paced learning environment.
Instructor-Led Training
Benefit from 55 hours of comprehensive training from industry experts.
Personalised Doubt Resolution
Access one-on-one sessions to clarify doubts and reinforce learning.
Lifetime Batch Access
Attend as many batches as you wish for a lifetime, ensuring continuous learning and improvement.

Artificial Intelligence Course Timings

Weekdays 1 hr / day 9 AM to 9 PM 75 Days ONLINE/OFFLINE Enrol Now
Weekends 3 hrs 9 AM to 9 PM 12 Weeks ONLINE/OFFLINE Enrol Now

Start Your Journey in AI Today

Ready to navigate the exciting world of Artificial Intelligence? Join our Artificial Intelligence course in Chennai with placement.

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Our Training Prerequisites

As you prepare to undertake the Artificial Intelligence Training at Aimore Technologies, there are a few recommended prerequisites to enhance your learning experience.

Our course welcomes individuals from diverse academic backgrounds. Irrespective of the field in which you've completed your undergraduate studies, you are eligible to participate in this transformative learning journey.

However, to optimally benefit from the AI course offered in Chennai, we recommend prospective students to have some prior experience with programming. This foundational knowledge will enable you to better grasp the intricacies of AI algorithms and software, accelerating your understanding of the subject matter.

While it is not a strict prerequisite, students who come from a mathematical background or possess strong mathematical skills will find the course content more intuitive.

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Artificial Intelligence Course in Chennai: Syllabus


  • Overview of Python and Its Development Environment: Exploring the basics of Python and tools like Jupyter, Pycharm, and more for development.
  • Essential Python Concepts: Exploring variables, data types, loops, conditional statements, functions, decorators, lambda functions, file and exception handling, and more.
  • Fundamentals of Object-Oriented Programming: Discussing concepts such as classes, objects, inheritance, abstraction, polymorphism, and encapsulation.
  • Practical Sessions and Assignments: Engaging with hands-on exercises related to real-world scenarios for thorough understanding.


  • Getting Started with Linux: An introduction to the basic functions and features of Linux OS.
  • Key Linux Concepts: Focusing on file handling and data extraction.
  • Practical Sessions and Assignments: Engaging in exercises to familiarise with Linux basics.

Excel Core Concepts

  • Basics: Data input, formula referencing, name range, logical functions, conditional formatting, and advanced validation.
  • Advanced Features: Dynamic tables, sorting, filtering, pivot tables, dashboards, data and file security, and VBA macros.
  • Excel VBA: Exploring ranges, worksheets, loops, IF conditions, and debugging.

Data Analysis using Excel

  • Data Handling: Text data processing, date management, data conversion, handling of missing values, and table operations.

Visualising Data:

  • Using charts, pie charts, scatter plots, bar graphs, column charts, line graphs, maps, and more.

Excel Power Tools:

  • Introduction to Power Pivot, Power Query, and Power View.

Classification in Excel:

  • Topics include binary and multiple classification problems, confusion matrices, AUC, and ROC curves.

Understanding Information Measures:

  • Probability, entropy, mutual information, and dependence.

Regression Analysis in Excel:

  • Covering topics like standardisation, probability distributions, inferential statistics, linear and logistic regression, error measures, and more.

Practical Sessions:

  • Practical exercises focusing on real-world problem scenarios.

Basic SQL Concepts:

  • Introduction to SQL: Understanding tables, joins, and variables.

Advanced SQL Techniques:

  • Exploring SQL functions, subqueries, views, string and mathematical functions, date-time functions, and more.
  • User-Defined Functions: Discussing types, stored procedures, rank functions, triggers, etc.
  • SQL Performance: Delving into record grouping, searching, sorting, indexing, and more.
  • Practical Sessions: Engaging with practical exercises on real-world problem scenarios.

Data Integration:

  • Extract, Transform, Load (ETL) Methods: Web scraping and API interactions.

Data Management with Python Libraries:

  • Using NumPy: Exploring arrays, linear algebra concepts, CRUD operations, and more.

Manipulating Data with Pandas:

  • Loading data, understanding data frames and series, CRUD operations, and data segmentation.

Data Preparation:

  • Preliminary Data Inspection: EDA, feature engineering and scaling, normalisation, outlier analysis, VIF, and more.

Visualising Data with Python:

  • Using Matplotlib: Creating bar charts, scatter plots, line graphs, pie charts, etc.
  • Using Seaborn: Producing regression plots, categorical graphs, area charts, and more.
  • Practical Sessions

Descriptive Statistics

  • Measures of central tendency
  • Measures of spread
  • Five points summary

Probability Theory

  • Probability Distributions
  • Central limit theorem
  • Bayes theorem

Inferential Statistics

  • Correlation and covariance
  • Confidence intervals
  • Hypothesis testing: F-test, Z-test, t-test, ANOVA, chi-square test, and more

Machine Learning Overview

  • Supervised vs. Unsupervised learning
  • Introduction to tools: sci-kit-learn, Keras, and more

Regression Analysis

  • Understanding regression problems: Dependent and independent variables
  • Training, evaluating, and enhancing the performance of regression models

Classification Analysis

  • Understanding classification problems: Dependent and independent variables
  • Training, evaluating, and enhancing the performance of classification models

Clustering Analysis

  • Understanding clustering problems: Dependent and independent variables
  • Training, evaluating, and enhancing the performance of clustering models

Methods in Supervised Learning

  • Linear Regression: Building models for linear data with techniques like data preprocessing and normalization
  • Logistic Regression: Model building for binary outcomes
  • Decision Trees and Random Forests: Hierarchical model building
  • Support Vector Machines: For both regression and classification tasks
  • Gradient Descent: Iterative method to find function extremities
  • K-Nearest Neighbors: Classification method based on proximity
  • Time Series Analysis: Predictive analysis using time series data

Techniques in Unsupervised Learning

  • K-means Clustering: Grouping data based on similarities
  • Dimensionality Reduction: Managing multi-dimensional data
  • Linear Discriminant Analysis and Principal Component Analysis: Optimising multi-dimensional data

Performance Evaluation Metrics

  • Classification reports: Metrics such as recall, precision, and more
  • Confusion matrix: Assessing prediction outcomes
  • Metrics like r2, adjusted r2, and mean squared error

Foundations of AI

  • Introduction to TensorFlow and Keras API

Understanding Neural Networks

  • Basic Neural Networks
  • Multi-layer Neural Networks
  • Advanced Artificial Neural Networks

Techniques in Deep Learning

  • Deep Neural Architectures
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • GPU applications in deep learning
  • Exploration of Autoencoders and restricted Boltzmann machines

Introduction to Power BI

  • Overview of Power BI, its components, and BI Tools
  • Data Warehousing essentials
  • Workflow with Power BI Desktop and Data Extraction
  • Data handling with SaaS Connectors, Azure SQL database, and integrating Python and R
  • Advanced features: Power Query Editor, Data Transformations, and Hierarchies

DAX Fundamentals

  • Data Modeling concepts
  • Time-based functions in DAX and advanced functionalities

Advanced Data Visualization

  • Techniques such as Slicers, filters, and Drill Down Reports
  • Exploring Power BI Query, Q & A features, and Data Insights
  • Power BI settings, administration, and direct connections
  • Embedded Power BI API and mobile integration

Practical Session:

  • Hands-on experience creating a visual dashboard based on sales data

Introduction to MLOps

  • MLOps lifecycle and pipeline
  • MLOps components and processes

Deploying Machine Learning Models on the Cloud

  • An overview of Azure Machine Learning
  • Steps to deploy machine learning models using Azure

Understanding Version Control

  • Definition and importance of version control
  • Different types of version control systems
  • Introduction to SVN

Exploring Git

  • Life cycle of Git
  • Common commands and their uses
  • Branch operations in Git, including creation and merging
  • Conflict resolution during merges
  • Workflow best practices
  • Collaborating with GitHub
  • Collaborative techniques using pull requests
  • Different methods of GitHub authentication: SSH and HTTP

Establish a solid foundation in:

  • Extraction, loading, and transformation of data to derive insights
  • Manipulation and pre-processing of datasets
  • Engineering features and scaling data according to the problem's demands
  • Building models using various machine learning algorithms, both supervised and unsupervised
  • Evaluating and monitoring machine learning models

Constructing a Recommendation Engine

  • Techniques in machine learning for recommending movies, restaurants, books, etc.

Text Classification and Rating Prediction

  • Building machine learning models for text data, predicting ratings and sentiments

Predictive Analysis on Census Data

  • Analysing population data and predicting/classifying features such as population count, income levels, etc.

Real Estate Price Prediction

  • Building a model to forecast housing prices based on multiple features

Real-time Object Detection

  • Building a machine learning model for detecting objects

Stock Market Insights

  • Analysing historical stock data to gain insights on specific stocks through feature engineering and selection

Predicting Consumer Behaviour in Banking

  • Classification problem using machine learning models to predict consumer actions

Designing an AI Chatbot

  • Using the NLTK python library, apply machine learning techniques to create an interactive AI chatbot

Fundamentals of Text Processing

  • ]Text mining, cleaning, and pre-processing techniques
  • Tokenization: Various tokenizers, frequency distribution, stemming, POS tagging, lemmatisation, and entity recognition

Text Analysis

  • Text classification, sentiment analysis using NLTK
  • Machine Learning overview, words, term frequency, countvectorizer, inverse document frequency, text conversion, confusion matrix, Naive Bayes classifier

Understanding Language Structure

  • Language modelling, predicting tag sequences, syntax trees, context-free grammars, chunking, paraphrasing techniques

Building with NLP

  • Using NLP techniques, construct a recommendation engine and an AI chatbot assistant

Introduction to RBM, DBNs, & Variational AutoEncoder

  • Overview of RBM and autoencoders
  • Utilising RBM for deep neural networks and collaborative filtering
  • Features and applications of autoencoders
  • Implementing Object Detection using CNN
  • Building a CNN using TensorFlow
  • Understanding convolutional, dense, and pooling layers

Generating Images and Advanced Models

  • Neural style image generation and deep generative models
  • Overview of generative models, sequence-to-sequence model (LSTM)

Parallel and Distributed Computing for Deep Learning

  • Differentiating distributed and parallel computing
  • Introduction to TensorFlow distributed computing and parallel training

Deepening Knowledge in Reinforcement Learning

  • Mapping the human mind with DNN
  • Components and architecture of ANN

Deployment Techniques for Deep Learning Models

  • Persisting models, saving, and serialising in Keras
  • Deploying models with TensorFlow Serving, Docker, Kubernetes, and more
  • Introduction to TensorFlow Lite and CNN model deployment

Dive into Big Data and Spark

  • Overview of Apache Spark framework and RDDs
  • Exploring gaps in traditional computing methods

Advanced RDD Techniques

  • Working with RDD persistence, caching, transformations, actions, and functions
  • The role of Key-Value pairs in RDDs and partitioning
  • Mastering Advanced Concepts and Spark-Hive Integration
  • Passing functions to Spark and understanding Spark SQL architecture
  • UDFs, DataFrames, data loading techniques, and performance tuning
  • Integrating Spark with Hive
  • Strategies for Job Searching
  • Crafting a Resume
  • Optimising LinkedIn Profiles
  • Preparing for Interviews with Industry Expert Sessions
  • Simulated Interview Practices
  • Potential placements with X+ hiring partners after passing the Placement Readiness Test.
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Benefits of Getting

AI Certified at Aimore

Experienced Mentors
Expert trainers with 10+ years of experience in the field of Artificial Intelligence.
Comprehensive Instruction
Hours of intensive classroom and online instruction on everything from Machine Learning to Deep Learning to Data Science.
Current Curriculum
Constantly updated course curriculum that keeps up with the latest industry trends.
Advanced Resources
Access to our state-of-the-art facilities and resources, including hardware, software, and networks.
Interactive Engagements
Ask Me Anything sessions and exclusive hackathons.
Complete Assistance
Complete assistance before, during, and after completing the Artificial Intelligence course.

Your AI Career Begins Here

Ready to embrace a thriving career in Artificial Intelligence? Enrol in our leading AI courses in Chennai and set the foundation for your success. The future of AI is waiting for you.
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