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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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.
Frequently Asked Questions

Have Questions About This Course?

Find answers to the most common questions learners ask before enrolling.

No, beginners can also join the course without prior coding experience. Basic Python concepts will be covered during training sessions.

Basic logical thinking and simple statistics knowledge are helpful, but advanced mathematics is not mandatory for beginners. Concepts are explained in an easy and practical manner.

A laptop with at least 8GB RAM, i3/i5 processor, and stable internet connection is recommended for smooth practical work. This configuration is suitable for coding, projects, and AI tools.

No, machine learning fundamentals are included in the course and taught from basics. Beginners can easily start learning AI step-by-step.

Yes, students from any educational background can start learning AI with proper guidance and practice. The course is designed to support both technical and non-technical learners.

Artificial Intelligence (AI) is a technology that enables machines to think, learn, analyze data, and make decisions similar to humans. AI is used in chatbots, virtual assistants, recommendation systems, and automation tools.

AI helps automate tasks, improve decision-making, increase efficiency, and solve complex problems across industries like healthcare, finance, education, cybersecurity, and business. It is becoming one of the most in-demand technologies worldwide

Beginners can start with Python programming, basic AI concepts, simple machine learning, and practical projects. Learning through hands-on practice and real-world examples is the best approach.

AI developers build intelligent applications such as chatbots, recommendation systems, AI assistants, automation systems, and predictive models using AI technologies. They also work on training and improving AI models.

Yes, Python is the most commonly used programming language in AI because it is simple and supports powerful AI libraries and frameworks. It is beginner-friendly and widely used in the industry.

Yes, beginners and non-technical students can also start learning AI with proper guidance and step-by-step training. Basic logical thinking and interest in technology are helpful.

AI is used in virtual assistants, self-driving cars, healthcare diagnosis, fraud detection, smart recommendations, automation systems, and content generation tools. It is widely used across almost every industry today.

Yes, Machine Learning and Deep Learning are important subsets of Artificial Intelligence used to build intelligent systems. They help machines learn from data and improve automatically.

Yes, AI is one of the fastest-growing fields with excellent career opportunities, high salaries, and strong demand across industries worldwide. AI professionals are highly valued in the current job market.

Yes, Natural Language Processing (NLP) is an important field of AI that helps machines understand, process, and generate human language. It is used in chatbots, translators, voice assistants, and AI search systems.
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