Practical AI Development Program

  • High Demand In The It Industry: Ai is in demand because companies use it for automation, smart applications, customer support, data analysis, and decision-making.
  • Useful In Multiple Career Fields: Ai is used in healthcare, finance, education, e-commerce, cybersecurity, marketing, automation, and software development.
  • Build Smart Real-world Applications: Learners can create chatbots, recommendation systems, prediction tools, ai assistants, automation workflows, and intelligent business solutions.
1.5 Months ₹11,999 ₹8,999
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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 45 days course helps learners build a strong foundation in AI concepts, Python for AI, machine learning basics, data handling, NLP basics, computer vision basics, generative AI, prompt engineering, and practical AI project development.

Course with Live Project

No Refund Available

ai concepts and intelligent systems: learners understand ai fundamentals, intelligent decision-making, automation, ai workflow, and real-world ai applications.

machine learning, nlp, and computer vision basics: work with basic prediction systems, chatbot logic, text processing, image recognition concepts, and beginner-level ai models.

Practical Ai Project Development: Develop Projects Like Ai Chatbots, Career Recommendation Assistants, Smart Study Planners, Resume Screening Tools, And Simple Ai 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

  • • 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
  • • exploring python libraries for ai

  • • understanding data types in ai
  • • learning statistics for artificial intelligence
  • • understanding mean, median, and mode
  • • exploring probability and predictions
  • • understanding correlation between variables
  • • learning matrix and vector concepts
  • • exploring linear algebra for ai

  • • 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

  • • 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

  • • understanding deep learning fundamentals
  • • learning artificial neural networks
  • • exploring perceptron working concepts
  • • understanding activation function basics
  • • learning hidden layer concepts
  • • exploring real-world deep learning uses

  • • understanding natural language processing
  • • learning basic text processing techniques
  • • understanding text tokenization process
  • • removing stop words from text
  • • exploring chatbots and language models
  • • understanding sentiment analysis concepts
  • • working with language-based ai

  • • 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

  • • 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
  • • learning responsible ai practices

  • • planning real-world ai applications
  • • preparing datasets for ai models
  • • training beginner-level ai models
  • • testing model performance accuracy
  • • creating ai-based mini projects
  • • understanding ai deployment basics
  • • presenting ai project outcomes

  • • ai ethics & bias analysis
  • • nlp text processing exercise
  • • chatbot conversation logic design

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 basics, functions, modules, data structures, file handling, oop basics, and logic building for ai applications.
Data Handling Basics: Work with numpy, pandas, data cleaning, missing values, basic transformations, and exploratory data understanding.
Machine Learning Basics: Learn supervised learning, unsupervised learning, regression, classification, clustering, model training, and prediction concepts.
Nlp Basics: Understand text processing, tokenization, stop words, chatbot logic, sentiment analysis basics, 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 Concepts: Understand chatgpt, ai assistants, text generation, content generation, ai tools, and responsible ai usage.
Prompt Engineering: Practice writing effective prompts for content creation, learning support, coding help, automation tasks, and ai productivity.
Ai Tools Usage: Work with python, jupyter notebook, google colab, scikit-learn basics, opencv basics, and modern ai productivity tools.
Ai Project Development Skills: Practice planning, building, testing, documenting, and presenting beginner-to-intermediate ai projects.

Career Opportunities after Artificial Intelligence Course

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

Ai Intern:

Support basic ai projects through data preparation, prompt writing, chatbot testing, and beginner-level ai implementation.

Ai Project Assistant:

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

Prompt Engineering Assistant:

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

Junior Ai Developer:

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

Ai Automation Assistant:

Work on simple automation tasks using ai tools, prompt workflows, and beginner-level intelligent systems.

Why Enroll in Artificial Intelligence with Solitaire Learning?

Beginner-friendly Ai Training: The course starts from ai fundamentals and gradually moves toward python, ml basics, nlp, computer vision, and generative ai.
Practical Project-based Learning: Learners work on real-world ai projects like chatbots, recommendation assistants, study planners, and ai automation tools.
Industry-relevant Ai Tools: The course covers python, google colab, scikit-learn basics, opencv basics, chatgpt, prompt engineering, and ai productivity tools.
Mentor-guided Learning: Learners receive mentor support for concept clarity, project development, prompt practice, doubt solving, and portfolio preparation.
Strong Foundation For Advanced Ai Courses: The course builds a solid base before moving into 2 months, 3 months, 4 months, or 6 months artificial intelligence programs.
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.
Course FAQ

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