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Artificial Intelligence Training

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Start your journey in Artificial Intelligence with our exciting Artificial Intelligence Training. This comprehensive Artificial Intelligence Course covers key areas such as machine learning, neural networks, and natural language processing. Through practical projects and real-world applications, you’ll gain valuable experience and industry insights. Our Artificial Intelligence Course with Certifications and Placements ensures you receive guidance from expert instructors and access to the latest resources. Whether you’re new to the field or looking to advance your career, this training equips you with the skills to excel in AI roles and make a meaningful impact in technology.

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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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Breakdown of Artificial Intelligence Training Fee and Batches

Hands On Training
3-5 Real Time Projects
60-100 Practical Assignments
3+ Assessments / Mock Interviews
May 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)

Course Fee
May 2025
Week ends
(Sat-Sun)
Online/Offline

4 Hours Real Time Interactive Technical Training

(Suitable for working IT Professionals)

Course Fee

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Syllabus of Artificial Intelligence Training

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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

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The objectives of learning Artificial Intelligence Training include

  • Learn AI Basics: Understand the fundamental concepts and history of Artificial Intelligence.
  • Master Machine Learning: Get to know machine learning algorithms and how they work, including both supervised and unsupervised methods.
  • Explore Neural Networks: Learn about neural networks and deep learning models.
  • Use Natural Language Processing: Study how to analyze and process human language with AI.
  • Work with Data: Develop skills in handling, analyzing, and visualizing data for AI.
  • Build AI Models: Gain experience in creating and using AI models and applications.
  • Practice with Projects: Apply what you learn through practical projects.

Reason to choose SLA for Artificial Intelligence Training

  • 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.
Want to Speak to our Trainer about Artificial Intelligence Training?Request a Free Callback

Highlights of The Artificial Intelligence Training

What is Artificial Intelligence?

Artificial Intelligence (AI) is a branch of computer science focused on creating machines that can perform tasks requiring human-like thinking. This includes learning from data, understanding speech, recognizing patterns, and making decisions. AI uses data and programs to do these tasks quickly and accurately.

What are the reasons for learning Artificial Intelligence?

Learning Artificial Intelligence is valuable for several reasons:

  • High Demand: AI skills are in great demand across various industries.
  • Career Opportunities: It opens up numerous job roles and career paths.
  • Innovation: AI drives technological advancements and innovation.
  • Problem Solving: AI helps solve complex problems and improve efficiency.
  • Future Growth: AI is a growing field with significant future potential.
What are the prerequisites for learning Artificial Intelligence?

There are no mandatory prerequisites for learning Artificial Intelligence. However, having a basic understanding of programming, mathematics, and statistics can be helpful.

Our Artificial Intelligence Training 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 fees for Artificial Intelligence training vary based on the program level (basic, intermediate, or advanced) and course format (online or in-person). Typically, the fee for a 1.5-month Artificial Intelligence course is around ₹18,000, including international certification. For the most accurate and current details on fees, duration, and certification, please contact our Artificial Intelligence Training Institute directly.

What are some job roles related to Artificial Intelligence?

Here are some job roles related to Artificial Intelligence:

  • AI Engineer: Develops and implements AI models and systems.
  • Machine Learning Engineer: Creates algorithms and models that enable machines to learn from data.
  • Data Scientist: Examines and interprets complex data to support informed business decisions.
  • AI Research Scientist: Conducts research to advance AI technologies and methodologies.
  • AI Product Manager: Oversees the development and implementation of AI products.
  • Deep Learning Engineer: Focuses on creating and optimizing neural networks.
What is the salary range for an Artificial Intelligence Engineer?

A new AI Engineer usually makes about ₹9,20,000 lakhs a year. With 4 to 9 years of experience, AI Engineers earn around ₹15,30,000 lakhs annually. Those with 10 to 20 years of experience can earn about ₹36,20,000 lakhs per year.

List a few Artificial Intelligence real-time applications.

Here are a few real-time applications made with Artificial Intelligence :

  • Virtual Assistants: AI helps with tasks and answers questions through assistants like Siri and Alexa.
  • Chatbots: AI chatbots help with customer support and provide information.
  • Recommendations: AI suggests products or shows based on what you like, such as on Netflix or Amazon.
  • Fraud Detection: AI finds and prevents fraud in banks and finance.
  • Self-Driving Cars: AI allows cars to drive themselves and make decisions.
  • Speech Recognition: AI converts spoken words into text, used in voice commands.
  • Image Recognition: AI identifies objects or faces in photos for tagging and security.

Who are our Trainers for Artificial Intelligence Training?

Our Mentors are from Top Companies like:
  • Our trainers have extensive experience working in the field of Artificial Intelligence, ensuring deep industry knowledge.
  • They possess advanced certifications in AI and related technologies, demonstrating their expertise and qualifications.
  • The trainers have hands-on experience with real-world AI projects in various industries, providing practical insights.
  • Many hold advanced degrees in AI, machine learning, or computer science, showcasing strong academic backgrounds.
  • They are skilled at breaking down complex AI concepts into easy-to-understand explanations for effective learning.
  • Several trainers have conducted important AI research and published their findings, contributing to the field’s knowledge.
  • Practical, hands-on experience with AI tools and techniques is a core part of their teaching approach.
  • Trainers keep up with the latest advancements and trends in AI to ensure relevant and current knowledge.
  • They offer personalized support and guidance throughout the course, helping students with individual learning needs.
  • Our trainers have valuable industry connections that can provide networking opportunities and career insights.
Want to Speak to our Trainer about Artificial Intelligence Training?Request a Free Callback

What Modes of Training are available for Artificial Intelligence Training?

Offline / Classroom Training

A Personalized Learning Experience with Direct Trainer Engagement!
  • 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
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Online Training

Interactive Quiz Website
Instructor Led Live Training! Learn at the comfort of your home
  • 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
Explore Online Courses

Corporate Training

Blended Delivery model (both Online and Offline as per Clients’ requirement)
  • Industry endorsed Skilled Faculties
  • Flexible Pricing Options
  • Customized Syllabus
  • 12X6 Assistance and Support
Explore Offline Courses
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Certifications

Take your career to new heights with Softlogic's software training certifications.
Improve your abilities to get access to rewarding possibilities
Earn Your Certificate of Completion
Validate your achievements with Softlogic's Certificate of Completion, verifying successful fulfillment of all essential components.
Take Your Career to the Next Level with an IBM Certification
Get an IBM certification through our training programs to gain a competitive edge in the industry.
Stand Out from the Crowd with Codethon Certificate
Verify the authenticity of your real-time projects with Softlogic's Codethon certificate.

Project Practices for Artificial Intelligence Training 

Healthcare Diagnostics

Develop AI models to analyze medical images or patient data to assist in diagnosing diseases.

Fraud Detection

Design a system to identify and prevent fraudulent transactions or activities in financial systems.

Autonomous Vehicles

Work on AI algorithms for self-driving cars, focusing on navigation, object detection, and decision-making.

Voice Recognition System

Develop a system that converts spoken language into text and performs tasks based on voice commands.

Sentiment Analysis

Analyze text data from social media or reviews to determine the sentiment behind customer opinions.

Predictive Analytics

Create a model that forecasts future trends or outcomes, such as sales forecasts or stock market predictions.

Image Classification

Develop a model to classify and categorize images, such as identifying objects or faces in photos.

Recommendation Engine

Build a system that suggests products, movies, or music based on user preferences and behavior.

Chatbot Development

Create a chatbot for customer service or support, using natural language processing to handle user queries.

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The SLA way to Become
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Technology Training

Coding Practices
Realtime Projects

Placement Training

Aptitude Training
Interview Skills
CRM System Testing

Panel Mock
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Placement Support for a Artificial Intelligence Training

Genuine Placements. No Backdoor Jobs at Softlogic Systems.

Free 100% Placement Support

Aptitude Training
from Day 1

CRM System Testing

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

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FAQs for
Artificial Intelligence Training

An AI engineer should know programming, math, and statistics. They need skills in machine learning, data analysis, and problem-solving, and should be familiar with AI tools and algorithms.

Yes, working with Artificial Intelligence generally requires coding. Coding is essential for creating algorithms, training models, and implementing AI systems.

Yes, you can learn Artificial Intelligence on your own. There are many online resources, courses, and tutorials available to help you learn AI at your own pace.

Yes, Artificial Intelligence is still in high demand. Many industries use AI for automation, data analysis, and improving decision-making, which keeps the need for AI skills strong.

Definitely! We offer ongoing placement assistance to help candidates achieve their career goals. Contact our career advisor to arrange a free demo for the leading Artificial Intelligence course, featuring placement support.

Yes, SLA does indeed give the option of EMI to students with 0% interest. 

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

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.

We accept all sort of major payment methods like cash, credit cards (Visa, Maestro, Master card), Netbanking, etc

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

Our Artificial Intelligence Training 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.

Artificial Intelligence is becoming important in many industries, from healthcare to finance. As companies use AI for tasks like automation and data analysis, there is a growing need for skilled AI professionals. This means more job opportunities for those trained in AI.

AI technology is constantly improving, with new methods and tools coming out often. Staying updated with these changes is important for working on the latest projects and innovations in AI. Continuous learning will help you stay current in this fast-evolving field.

With the rise of AI, there are many career options available, including machine learning, data science, and AI research. Professionals can find jobs in various sectors like technology, finance, and healthcare. This variety ensures a promising career for those with AI skills.

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Artificial Intelligence Training

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