Artificial intelligence


Artificial intelligence (AI) is a rapidly evolving field at the intersection of computer science, mathematics, and cognitive psychology, aimed at creating machines that can perform tasks that typically require human intelligence. These tasks range from understanding natural language and recognizing patterns in data to making decisions and learning from experience. AI systems are designed to emulate human cognitive functions, such as reasoning, problem-solving, perception, and language understanding, often leveraging techniques like machine learning, neural networks, natural language processing, and computer vision. As AI technologies advance, they are increasingly integrated into various aspects of everyday life, from virtual assistants and recommendation systems to autonomous vehicles and medical diagnostics, reshaping industries and societies worldwide. However, along with its transformative potential, AI also raises ethical, societal, and economic considerations, including issues related to bias, privacy, job displacement, and the equitable distribution of benefits. Thus, the development and deployment of AI require careful consideration of both its capabilities and its broader implications for individuals and societies.

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We are the best trainers in the latest, coveted technologies across the globe, and we can help you carve your career. Come learn with us and give yourself the gift of knowledge.

High Paying Skills for AI

  1. Machine Learning (ML) and Deep Learning: Proficiency in machine learning algorithms and deep learning frameworks like TensorFlow and PyTorch is highly sought after. Knowledge of neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and reinforcement learning is particularly valuable.
  2. Data Science and Analytics: Strong skills in data preprocessing, data analysis, and statistical modeling are essential for deriving insights from large datasets. Proficiency in programming languages such as Python and R, along with expertise in data visualization tools like Tableau or Matplotlib, is also crucial.
  3. Natural Language Processing (NLP): NLP enables machines to understand, interpret, and generate human language, making it indispensable for applications like chatbots, sentiment analysis, and language translation. Skills in NLP techniques, frameworks like NLTK and spaCy, and pre-trained language models (e.g., BERT, GPT) are highly valued.
  4. Computer Vision: Computer vision involves teaching machines to interpret and understand visual information from images or videos. Proficiency in image processing techniques, feature extraction, object detection, and image classification using libraries like OpenCV and frameworks such as TensorFlow Object Detection API is in demand.
  5. Reinforcement Learning: Reinforcement learning focuses on training agents to make sequential decisions in dynamic environments to maximize cumulative rewards. Skills in reinforcement learning algorithms, such as Q-learning, Deep Q-Networks (DQN), and policy gradients, along with experience in implementing and optimizing RL algorithms, are highly sought after.
  6. Software Engineering: Strong software engineering skills, including proficiency in programming languages like Python, Java, or C++, version control systems (e.g., Git), and software development best practices (e.g., agile methodologies, unit testing), are essential for building scalable and maintainable AI systems.
  7. Domain Knowledge: Expertise in specific domains such as healthcare, finance, autonomous vehicles, or robotics, combined with AI skills, can significantly enhance career opportunities and earning potential. Understanding domain-specific challenges, regulations, and data nuances is valuable for developing effective AI solutions tailored to industry needs.
  8. Cloud Computing: Knowledge of cloud computing platforms like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP) is increasingly important for deploying and scaling AI applications. Skills in cloud services, containerization (e.g., Docker, Kubernetes), and serverless computing enable efficient utilization of cloud resources for AI projects.
Projects Developed
Hiring Partners
Hrs. of classes delivered

Program Objective

Learn AI Tasks: Speech recognition, decision-making, and pattern identification.

Learn and deploy AI Technologies: Machine Learning , Deep learning, natural language processing (NLP)

Learn Types of AI : Narrow AI (Weak AI) General AI (Strong AI), Super AI.

Learn Common AI Applications: Chatbots , Computer Vision, Recommendation Systems ( movies, music etc.), Self-Driving Cars, Healthcare, Finance.

Discuss Ethical Considerations Bias: AI systems can inherit biases from training data. Transparency: Understanding how AI decisions are made. Privacy: Balancing data utilization with user privacy.

Discuss Benefits and Challenges Benefits: Efficiency, automation, improved decision-making, and innovation. Challenges: Ethical dilemmas, job displacement, and safety concerns.


  • AI program is a training and mentorship program that teaches learners what is artificial intelligence and how to apply in business for recognizing speech, making decisions, and identifying patterns.
  • The program aims to help learners to develop skills to use deep learning techniques, Machine learning and Natural Language Processing (NLP).
  • The program prepares learners for careers such as AI Engineer , Machine Learning Engineer , Robotics or Software Engineer, and Data scientist.
  • The Program is Powered by KPMG. There will be Certification from DaCh –KPMG.
  • The program last for 200 hours and offer Live Projects, Assignments, Live Capstone Projects and certification opportunities.
  • The program uses real data from different domains, such as sports, entertainment, healthcare, finance and retail.

Munmun bhagat

  • 8 Years of professional & Consulting experience 6 yeras of extensive experience in Training Have trained over 1500+ Professional across india Worked in various educational Institutes in various capacties as Assistant proffessor,TPO,Project coordinator ect.Have trained on google cloud.salesforce A through implementer with abilities in project management

Vishal Petkar

  • Ph.D. in Computer Science
  • MSc in Information Technology
  • SME in Software Development,
  • Technical Training in Data Science, Data Analytics & Full Stack Architecture
  • Technical Course Design, Development and Enrichment

Sandhiya B

  • M.E in Computer Science Engineering, with Thesis focuses on Image Processing, Biometrics.
  • Trained 2000+ professionals in Full Stack Programs
  • Ability to deliver lecture, and hold group conversation on related-topic with students.
  • Published 6 books in international Journals

Sagar R

  • Java, Hibernate & Spring Boot, Python, Django
  • Panda, JavaScript, SQL, Manual Testing, Automation Testing, Selenium
  • Test NG, Data Science, Data Analytics, Big Data, Python
  • Specialist. Specialist t in Data Architecture, Hadoop – Data Lakes

Parth Sagar

  • 9 Years of professional & Consulting experience 8 yeras of extensive experience in Training Have trained over 1700+ professionals across globe Clpose to 9 years training experience and conducted multiple training in data science ,salesforces,AWS ,R,Python and Weka across the globle. -published several research papers in SCI.


  • Master of computer Application
  •,B.Ed in computer science
  • 8years of IT industry Experience
  • Cyber Security Foundation Professional Certificate
  • Senior Full stcak developer
  • Skilled in web develpment and API functionally

Topic 13: Product Management with AI – Modules 20

Topic 14: AI Adoption, Strategy & Applications –


Topic 01: AI Introduction – Module 1

Topic 02: Python – Module 2 & 3

Topic 03: SQL– Module 4

Topic 04: Python for Data Science and Pandas– Module 5

Topic 05: Neural Networks – Module 6

Topic 06: Applied Deep Learning with Pytorchs– Module 7

Topic 07: NLP – Module 8 & 9

Topic 08: LLM – Modules 10-15

Topic 09: ChatGPT- Modules 16-17

Topic 10: Prompt Engineering – Modules 17-18

Topic 12: Deep learning and its architectures– Module 19

Take your first step towards RESULT.


Chools Industry mentorship: One-on-one mentorship with industry experts can provide personalized career advice and help Trainees develop the skills they need to succeed in the field.

Chools Industry mentorship: One-on-one mentorship with industry experts can provide personalized career advice and help Trainees develop the skills they need to succeed in the field.

Chools Resume and cover letter review: Resume and cover letter review services from Chools can help Trainees create effective job application materials that highlight their skills and experience.

Chools Interview preparation: Interview preparation services from Chools can help Trainees prepare for job interviews by providing tips on how to answer common interview questions and how to present themselves professionally.


Number of Weekdays: 100 Days

Number of Hours: 200 Hrs

Assessment Methods

  • Assessments: One Final Exam
  • Capstone Projects: Four Live Projects
  • Class Assessments: 50

Mode of Delivery

  • Mode of Delivery: Online
  • Live Classes: Yes
  • Recorded Sessions: Available for 12 months