AI Engineering Career Track

  • High Demand In The It Industry: Ai is in demand because companies use it for automation, data analysis, customer support, content generation, prediction, and smart business applications.
  • Useful In Multiple Career Fields: Ai is used in healthcare, finance, education, e-commerce, cybersecurity, marketing, software development, and business automation.
  • Build Smart Real-world Applications: Learners can create chatbots, recommendation systems, ai assistants, text analysis tools, image-based applications, and automation workflows.
3 Months ₹22,999 ₹16,999
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AI Engineering Career Track
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Course Overview

Artificial Intelligence is a modern technology that enables machines to think, learn, analyze data, understand language, recognize images, and make intelligent decisions. This 3 months course helps learners build strong AI skills using Python, AI fundamentals, machine learning, data handling, NLP, computer vision, generative AI, prompt engineering, AI tools, and real-world AI project development.

Course with Live Project

No Refund Available

Ai And Intelligent System Development: Learners Understand Ai Concepts, Automation, Intelligent Decision-making, Ai Workflow, And Real-world Ai Applications.

Machine Learning, Nlp, And Computer Vision: Work With Prediction Systems, Chatbot Logic, Text Processing, Image Recognition, Opencv Basics, And Beginner-to-intermediate Ai Models.

Portfolio-based Ai Project Development: Develop Practical Projects Like Ai Mock Interview Coaches, Ai Productivity Assistants, Chatbots, Recommendation Tools, And Smart Automation Systems.

Course Content

  • • Understanding artificial intelligence concepts
  • • Exploring history and evolution of ai
  • • Learning different types of ai
  • • Understanding real-world ai applications
  • • Exploring ai across multiple industries
  • • Comparing ai, ml, and deep learning
  • • Understanding ai workflow process
  • • Setting up ai development environment

  • • Learning python programming fundamentals
  • • Understanding variables and data types
  • • Working with conditional statements logic
  • • Using loops for program execution
  • • Creating functions and reusable modules
  • • Managing data with python collections
  • • Understanding file handling concepts
  • • Handling errors and exceptions
  • • Exploring python libraries for ai

  • • Understanding mean, median, and mode
  • • Learning variance and standard deviation
  • • Exploring probability and predictions
  • • Understanding correlation between variables
  • • Learning matrix and vector concepts
  • • Understanding linear algebra fundamentals
  • • Exploring data distribution patterns
  • • Applying statistics for ai systems
  • • Understanding optimization techniques basics

  • • Understanding numpy for data processing
  • • Learning pandas for data analysis
  • • Managing data using dataframes
  • • Reading csv and excel files
  • • Cleaning and preparing datasets
  • • Handling missing data efficiently
  • • Transforming data for ai models
  • • Performing exploratory data analysis
  • • Working with real-world datasets

  • • Understanding machine learning concepts
  • • Exploring types of machine learning
  • • Learning supervised learning techniques
  • • Understanding unsupervised learning concepts
  • • Working with regression models
  • • Exploring classification algorithm basics
  • • Measuring prediction and accuracy
  • • Training models using datasets
  • • Understanding model evaluation concepts
  • • Applying machine learning in ai

  • • Understanding deep learning fundamentals
  • • Learning artificial neural networks
  • • Exploring perceptron working concepts
  • • Understanding activation function basics
  • • Learning hidden layer concepts
  • • Exploring convolutional neural networks
  • • Understanding recurrent neural networks
  • • Working with deep learning models
  • • Applying deep learning applications

  • • Understanding natural language processing
  • • Learning basic text processing techniques
  • • Understanding text tokenization process
  • • Removing stop words from text
  • • Understanding text classification concepts
  • • Exploring chatbots and language models
  • • Understanding sentiment analysis concepts
  • • Building beginner-level ai chatbots
  • • Working with nlp applications

  • • Understanding computer vision concepts
  • • Learning basic image processing methods
  • • Understanding face detection techniques
  • • Exploring object detection concepts
  • • Learning opencv library basics
  • • Understanding ai in healthcare imaging
  • • Working with image recognition systems
  • • Building vision-based ai applications

  • • Understanding generative artificial intelligence
  • • Learning large language model basics
  • • Exploring chatgpt and ai assistants
  • • Understanding prompt engineering concepts
  • • Using ai for content generation
  • • Exploring ai automation techniques
  • • Understanding text and image generation
  • • Learning responsible ai practices

  • • Understanding ai model deployment concepts
  • • Integrating ai models with applications
  • • Working with apis for ai
  • • Understanding cloud-based ai services
  • • Deploying beginner-level ai models
  • • Managing ai project workflow

  • • Solving real-world ai problems
  • • Preparing datasets for ai models
  • • Training and testing ai systems
  • • Evaluating ai model performance
  • • Building industry-oriented ai projects
  • • Presenting ai project results

  • • Sentiment analysis task
  • • Ai model evaluation exercise
  • • Recommendation logic design
  • • Nlp processing exercise
  • • Ai case study analysis

Skills Developed with Artificial Intelligence Course

Ai Fundamentals: Understand ai concepts, types of ai, ai workflow, real-world applications, and the difference between ai, ml, and deep learning.
Python For Ai: Learn python programming, functions, modules, oop basics, file handling, data structures, and logic building for ai applications.
Data Handling And Preprocessing: Work with numpy, pandas, data cleaning, missing values, transformations, and exploratory data analysis.
Machine Learning Concepts: Learn supervised learning, unsupervised learning, regression, classification, clustering, model training, testing, and prediction concepts.
Deep Learning Basics: Understand neural networks, activation functions, hidden layers, tensorflow/keras basics, and deep learning applications.
Natural Language Processing: Work with text processing, tokenization, stop words, sentiment analysis, chatbot logic, and language-based ai systems.
Computer Vision Basics: Learn image processing concepts, face detection basics, object detection overview, opencv basics, and image recognition use cases.
Generative Ai And Prompt Engineering: Use chatgpt, ai assistants, prompt writing, text generation, content generation, automation tasks, and responsible ai practices.
Ai Tools And Libraries: Work with python, jupyter notebook, google colab, scikit-learn, tensorflow basics, opencv basics, chatgpt, and modern ai tools.
Ai Project Development Skills: Practice planning, building, testing, improving, documenting, and presenting intermediate-level ai projects.

Career Opportunities after Artificial Intelligence Course

This course opens doors to multiple high-demand career paths across industries.

Ai Intern:

Support ai projects through data preparation, prompt writing, chatbot testing, model testing, and beginner-to-intermediate ai implementation.

Ai Project Assistant:

Help ai teams with research, dataset understanding, workflow planning, documentation, testing, and project coordination.

Prompt Engineering Assistant:

Create and improve prompts for chatbots, content generation, ai assistants, automation workflows, and productivity tools.

Junior Ai Developer:

Build ai applications such as chatbots, recommendation tools, ai planners, automation tools, and basic intelligent systems.

Ai Automation Assistant:

Work on ai-based automation tasks using prompt workflows, apis, ai tools, and beginner-to-intermediate intelligent systems.

Why Enroll in Artificial Intelligence with Solitaire Learning?

Beginner-friendly Ai Learning Path: The course starts from ai fundamentals and gradually moves toward python, ml, nlp, computer vision, generative ai, and projects.
Practical Project-based Training: Learners work on real-world ai projects like mock interview coaches, productivity assistants, chatbots, recommendation tools, and ai automation systems.
Industry-relevant Ai Tools: The course covers python, google colab, scikit-learn, tensorflow basics, opencv basics, chatgpt, prompt engineering, and ai productivity tools.
Mentor-guided Project Support: Learners receive mentor guidance for concept clarity, prompt practice, model understanding, project development, and portfolio preparation.
Strong Foundation For Advanced Ai Learning: The course prepares learners for 4 months and 6 months advanced artificial intelligence programs with deeper ai, deployment, and industry-level projects.

Ready to Take the Next Step in Your Career?

Join our expert-led training program, gain industry-recognized skills, and move closer to your professional goals. Seats are limited — enroll today!

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