Certification in Artificial Intelligence

Artificial Intelligence (AI) is a branch of computer science that focuses on creating systems or machines capable of performing tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, understanding natural language, and recognizing patterns.

AI technologies are widely used in various applications such as voice assistants (like Siri and Alexa), recommendation systems (like Netflix and YouTube), self-driving cars, medical diagnosis, and robotics.

The goal of AI is to develop smart systems that can think, act, and adapt like humans, or even surpass human capabilities in certain areas.

Job after Artificial Intelligence Course:

AI Engineer – Build AI models/systems.

Data Scientist – Analyze data with AI.

Machine Learning Engineer – Create ML algorithms.

Research Scientist – Develop new AI tech.

AI Consultant – Advise companies on AI solutions.

Complete  syllabus for Artificial Intelligence (AI) Course:

Module 1: Introduction to Artificial Intelligence

• Definition and History of AI
  
  

• Types of AI (Narrow, General, Super AI)
  
  

• Applications of AI in various sectors
  
  

• Ethical and Social Implications of AI
  
  

• AI vs Machine Learning vs Deep Learning
  
  

Module 2: Mathematics for AI

• Linear Algebra: Vectors, Matrices, Eigenvalues
  
  

• Probability and Statistics: Bayes Theorem, Distributions
  
  

• Calculus: Derivatives, Gradients, Chain Rule
  
  

• Optimization Techniques: Gradient Descent
  
  

Module 3: Programming for AI (Python)

• Python Basics and Libraries (NumPy, Pandas, Matplotlib)
  
  

• Data Handling and Preprocessing
  
  

• Data Visualization
  
  

• Writing AI Algorithms from Scratch
  
  

Module 4: Machine Learning Fundamentals

• Supervised Learning (Regression, Classification)
  
  

• Unsupervised Learning (Clustering, Dimensionality Reduction)
  
  

• Model Evaluation: Confusion Matrix, Precision, Recall, F1 Score
  
  

• Cross-Validation and Bias-Variance Tradeoff
  
  

• Scikit-learn Framework
  
  

Module 5: Deep Learning

• Introduction to Neural Networks
  
  

• Activation Functions (ReLU, Sigmoid, Tanh)
  
  

• Training Deep Networks (Backpropagation, Optimization)
  
  

• Convolutional Neural Networks (CNNs)
  
  

• Recurrent Neural Networks (RNNs) & LSTMs
  
  

• Frameworks: TensorFlow, Keras, or PyTorch
  
  

Module 6: Natural Language Processing (NLP)

• Text Preprocessing (Tokenization, Lemmatization)
  
  

• Language Modeling (N-grams, TF-IDF, Word2Vec)
  
  

• Sentiment Analysis and Chatbots
  
  

• Transformers and BERT Basics
  
  

Module 7: Computer Vision

• Image Processing Basics
  
  

• Object Detection and Recognition
  
  

• OpenCV Introduction
  
  

• CNNs for Image Classification
  
  

• Face Recognition Techniques
  
  

Module 8: Reinforcement Learning (Basics)

• Markov Decision Processes
  
  

• Q-Learning and SARSA
  
  

• Policy Gradient Methods
  
  

• Applications in Games and Robotics
  
  

Module 9: AI in Practice

• Building AI Projects (End-to-End Workflow)
  
  

• AI Model Deployment (using Flask/Streamlit)
  
  

• Introduction to MLOps
  
  

• Cloud Platforms for AI (Google Colab, AWS, Azure AI)
  
  

Module 10: Capstone Project & Certification

• Real-world AI project
  
  

• Problem Statement, Data Collection, Model Building
  
  

• Deployment and Presentation
  
  

• Assessment & Final Certification
  
  

Duration of Course  : 6 months

Fee : 30000/-

Contact for Admission

Siddharth  Sharma

HOD, Department of Computer Engineering

Concept IT Solutions, Pune

Call:7219116540