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  • Level
  • Duration
  • Certificate
  • All Levels
  • 6–8 weeks (Adjustable)
  • Bharat Academy Education
  • Certified
  • - KHDA
    - AIAL
    - Royal Institute for Chartered Engineer - RICE (USA)
Course Info
Key Highlights
Target Audience
Final Capstone Project
  • Choose a real-world problem in any domain (finance, healthcare, robotics, etc.) and build an AI model to solve it.
  • Present findings, model performance, and future improvements.
Assessment Methods
  • Quizzes at the end of each module
  • Hands-on projects and assignments
  • Final capstone project presentation
Module 1: Introduction to Python and AI Fundamentals
1.1  Introduction to AI and Python's Role AI Development
    • Overview of AI applications in real-world problems.
    • Python libraries for AI (NumPy, Pandas, SciPy)
    • Setting up the environment (Anaconda, Jupyter Notebooks)
1.2 Python Basics for AI
    • Data types, loops, conditions, and functions
    • Object-oriented programming (OOP) essentials
    • Introduction to NumPy for efficient computations
      • Hands-on Project: Build a simple AI-based chatbot
Module 2: Data Handling and Preprocessing
2.1 Data Wrangling with Pandas
    • Working with datasets
    • Data cleaning and preparation
2.2 Data Visualization
    • Introduction to Matplotlib and Seaborn
    • Plotting and visualizing trends and relationships in data
2.3 Feature Engineering
    • Handling missing data
    • Feature scaling and normalization
      • Hands-on Project: Create an AI-ready dataset from raw data (e.g., sales or medical records)
Module 3: Machine Learning with Python
3.1 Introduction to Machine Learning
    • Types of ML: Supervised, Unsupervised, Reinforcement Learning
    • Introduction to Scikit-Learn
3.2 Regression Algorithms
    • Linear Regression
    • Decision Trees and Random Forest
3.3 Classification Algorithms
    • Logistics Regression
    • K-Nearest Neighbors (k-NN)
    • Support Vector Machines (SVM)
3.4 Clustering Algorithms
    • k-Means Clustering
    • Hierarchical Clustering
      • Hands-on Project: Develop a machine learning model to predict house prices or classify medical data
Module 4: Deep Learning with Python
4.1 Introduction to Neural Networks
    • Perception, activation functions, and forward/backpropagation
    • Introduction to TensorFlow and Keras
4.2 Building Deep Learning Models
    • Multi-layer perceptions (MLPs)
    • Hyperparameter tuning and optimization
4.3 Convolutional Neural Networks (CNNs)
    • CNN architecture for image processing
    • Applications in computer vision
4.4 Lesson 4.4: Recurrent Neural Networks (RNNs)
    • Time series and sequence prediction
    • Long Short-Term Memory (LSTM) networks
      • Hands-on Project: Build a deep learning model for image classification (e.g., handwritten digits recognition) or text classification (e.g., sentiment analysis)
Module 5: AI in Natural Language Processing (NLP)
5.1: NLP Basics and Text Preprocessing
    • Tokenization, stemming, and lemmatization
    • Word embeddings (TF-IDF, Word2Vec)
5.2: Sentiment Analysis and Text Classification
    • Building text classification models
    • Sentiment analysis with Scikit-Learn and TensorFlow
5.3: Chatbots and NLP Applications
    • Building simple AI-based chatbots with NLP processing
    • Neural network models for NLP (transformers, GPT)
      • Hands-on Project: Develop an AI-based sentiment analysis tool or chatbot
Module 6: AI in Computer Vision
6.1: Introduction to Computer Vision
    • Image processing techniques (OpenCV, PIL)
    • Basic image transformations and filters
6.2: Object Detection and Recognition
    • Applying CNNs for object recognition
    • Transfer learning and pre-trained models (VGG, ResNet)
      • Hands-on Project: Build an AI-based object detection system
Module 7: Reinforcement Learning with Python
7.1: Introduction to Reinforcement Learning
    • Key concepts: agents, environment, rewards, actions
7.2: Q-Learning and Policy Gradient Methods
    • Building RL agents with Python
    • Practical applications in game environments
      • Hands-on Project: Create an AI that learns to play a simple game (e.g., Tic-Tac-Toe)
Module 8: Ethical Considerations and AI Future
8.1: Ethical AI and Bias in AI Models
    • Addressing bias and fairness in AI systems
    • Privacy and data security concerns
8.2: The Future of AI
    • AI trends and innovations
    • AI in different industries (Healthcare, Finance, Robotics)
  • Basic knowledge of Python
  • Understanding of high school-level mathematics (algebra, statistics)

Courses

  • Artificial Intelligence
  • Robotics
  • Healthcare Services
  • Information Technology
  • Accounts and Legal
  • Cyber Security | Ethical Hacking
  • Engineering
  • Finance
  • Hotel and Hospitals
  • Satellite Communication
  • Fire and Safety
  • JCI
  • Amadeus GDS

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Working Hours

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Office Phone Number

(+971) 54 206 1051

Bharat Academy Email

marketing@bharat-academy.com

Campus Building

5007, Rigga Business Center, Ibis Hotel, Al Rigga Metro, Dubai

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