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Artificial Intelligence, Machine Learning & Deep Learning<\/h5>\n

Course Overview\r\nThis comprehensive course provides in-depth knowledge and hands-on experience in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). \r\nIt is designed to build a solid foundation in AI concepts, equip students with the necessary skills to develop machine learning models and delve into the complexities of deep learning architectures. \r\nThe course focuses on practical applications, including automation, predictive analytics, natural language processing, computer vision, and robotics. It aims to prepare students to tackle real-world challenges and innovate solutions for various industries.\r\n________________________________________\r\nCourse Duration:\r\n\u2022 Total Duration: 6 months\r\n\u2022 Weekly Commitment: 10-12 hours (2-3 hours of lectures, 3-4 hours of practical labs, 4-5 hours of self-study)\r\n\u2022 Modules: 10 Modules + 1 Capstone Project\r\n________________________________________\r\nCourse Objectives:\r\n1. Understand the fundamental concepts of AI, ML, and DL.\r\n2. Develop practical skills in designing, implementing, and evaluating AI\/ML models.\r\n3. Master various ML algorithms such as supervised, unsupervised, and reinforcement learning.\r\n4. Gain expertise in deep learning architectures, including neural networks, CNNs, RNNs, and transformers.\r\n5. Learn to deploy and scale AI models using cloud-based solutions.\r\n6. Understand the ethical considerations and societal impact of AI technologies.\r\n7. Build industry-relevant projects that solve real-world problems.\r\n________________________________________\r\nModules:\r\nModule 1: Introduction to Artificial Intelligence\r\n\u2022 Overview of AI and its history\r\n\u2022 Types of AI: Narrow AI, General AI, Superintelligent AI\r\n\u2022 Applications of AI in various industries\r\n\u2022 AI Ethics and Society\r\n\u2022 Tools & Platforms: Python, Jupyter Notebook, TensorFlow\r\nModule 2: Programming Foundations for AI & ML\r\n\u2022 Python Programming for AI\r\n\u2022 Libraries: NumPy, Pandas, Matplotlib, Scikit-Learn\r\n\u2022 Data Structures and Algorithms\r\n\u2022 Introduction to Git and Version Control\r\n\u2022 Hands-on exercises\r\nModule 3: Fundamentals of Machine Learning\r\n\u2022 Supervised Learning (Regression, Classification)\r\n\u2022 Unsupervised Learning (Clustering, Dimensionality Reduction)\r\n\u2022 Reinforcement Learning (Q-learning, Deep Q Networks)\r\n\u2022 Model Evaluation and Validation Techniques (Cross-validation, Confusion Matrix)\r\n\u2022 Technologies: Scikit-Learn, PyTorch, TensorFlow\r\nModule 4: Data Preprocessing & Feature Engineering\r\n\u2022 Data Cleaning and Transformation\r\n\u2022 Handling Missing Data and Outliers\r\n\u2022 Feature Selection and Feature Engineering\r\n\u2022 Data Visualization Techniques (Seaborn, Plotly)\r\n\u2022 Data Pipelines with Pandas and Scikit-Learn\r\nModule 5: Advanced Machine Learning Techniques\r\n\u2022 Ensemble Methods (Random Forests, Gradient Boosting, XGBoost)\r\n\u2022 Support Vector Machines (SVM)\r\n\u2022 Time Series Analysis and Forecasting\r\n\u2022 NLP Basics (Tokenization, Bag of Words, TF-IDF)\r\n\u2022 Text Classification using NLP models\r\nModule 6: Deep Learning Basics\r\n\u2022 Introduction to Neural Networks\r\n\u2022 Backpropagation and Gradient Descent\r\n\u2022 Activation Functions and Optimization Techniques\r\n\u2022 Regularization: Dropout, Batch Normalization\r\n\u2022 Technologies: Keras, TensorFlow\r\nModule 7: Convolutional Neural Networks (CNNs)\r\n\u2022 Understanding CNN Architecture\r\n\u2022 Image Processing and Augmentation\r\n\u2022 Building CNN models for Image Classification and Object Detection\r\n\u2022 Transfer Learning using Pre-trained Models (e.g., VGG16, ResNet)\r\n\u2022 Hands-on with TensorFlow and Keras\r\nModule 8: Recurrent Neural Networks (RNNs) & LSTM\r\n\u2022 Sequence Modeling with RNNs\r\n\u2022 Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)\r\n\u2022 Applications in Time Series Forecasting and Text Generation\r\n\u2022 Introduction to Transformers and Attention Mechanism\r\n\u2022 Tools: TensorFlow, PyTorch\r\nModule 9: Natural Language Processing & Generative AI\r\n\u2022 Advanced NLP Techniques (Transformers, BERT, GPT)\r\n\u2022 Sentiment Analysis and Chatbot Development\r\n\u2022 Generative Adversarial Networks (GANs)\r\n\u2022 Text-to-Image and Image-to-Text Models\r\n\u2022 Fine-tuning Pre-trained Models using Hugging Face Transformers\r\nModule 10: AI Deployment & Scaling\r\n\u2022 Model Deployment using Flask and FastAPI\r\n\u2022 AI on Cloud: AWS, Google Cloud, Azure for Model Hosting\r\n\u2022 Monitoring and Maintaining AI Models\r\n\u2022 Introduction to MLOps (Machine Learning Operations)\r\n\u2022 Deployment using Docker and Kubernetes\r\nCapstone Project:\r\n\u2022 Design and develop an AI\/ML\/DL-based project that solves a real-world problem. Examples include predictive maintenance for machinery, customer sentiment analysis, or a computer vision model for medical imaging.\r\n\u2022 The project will include data collection, model building, deployment, and a presentation of findings.\r\n________________________________________\r\nTechnologies Covered:\r\n\u2022 Programming Languages: Python, R\r\n\u2022 Libraries & Frameworks: TensorFlow, PyTorch, Keras, Scikit-Learn, OpenCV, Hugging Face Transformers\r\n\u2022 Development Tools: Jupyter Notebook, Google Colab, Git, Docker, Flask\r\n\u2022 Cloud Platforms: AWS, Google Cloud Platform (GCP), Microsoft Azure\r\n\u2022 MLOps Tools: MLflow, Kubeflow, TensorBoard\r\n________________________________________\r\nTarget Audience:\r\n\u2022 Students and professionals aiming to build a career in AI, ML, or DL.\r\n\u2022 Data Scientists, Software Engineers, and IT professionals seeking to upskill.\r\n\u2022 Enthusiasts and researchers interested in exploring AI applications and innovations.\r\n________________________________________\r\nStudents' Expectations:\r\n1. Gain hands-on experience in AI, ML, and DL model development.\r\n2. Develop proficiency in using cutting-edge libraries and frameworks.\r\n3. Acquire skills to build and deploy AI solutions.\r\n4. Work on practical, industry-relevant projects.\r\n5. Understand how to approach AI problems, from data collection to model deployment.\r\n6. Receive mentorship and feedback from industry experts and peers.\r\n________________________________________\r\nCourse Purpose:\r\nThe purpose of this course is to:\r\n\u2022 Provide learners with a robust understanding of AI, ML, and DL principles.\r\n\u2022 Equip students with practical skills for real-world AI applications.\r\n\u2022 Encourage innovative problem-solving approaches using AI technologies.\r\n\u2022 Prepare learners for careers as AI engineers, data scientists, machine learning engineers, and AI consultants.\r\n________________________________________\r\nAssessment and Certification:\r\n\u2022 Quizzes: After each module to assess comprehension.\r\n\u2022 Assignments: Weekly practical tasks to reinforce learning.\r\n\u2022 Project Work: Capstone project evaluated by peers and instructors.\r\n\u2022 Certification: Participants will receive a certification of completion after successfully completing the course and capstone project.\r\n________________________________________\r\nThis course aims to be a comprehensive pathway for learners to dive into the rapidly growing field of AI, enabling them to contribute to industry advancements and solve complex problems with innovative AI solutions.\r\n<\/p>\n <\/a>\n <\/div>\n <\/td>\n

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