About This Course

The objective of our AI (Artificial Intelligence) courses and bootcamps is to enable students to harness the transformative power of artificial intelligence in various domains.

We aim to equip students with the knowledge and practical skills necessary to design, develop, and deploy AI solutions that can solve complex problems and drive innovation. Our programs cover a wide range of AI topics, including machine learning, deep learning, natural language processing, computer vision, and reinforcement learning.

Term: Flexible courses with 2-hour sessions over 6-8 instances and rigorous 2-day bootcamps to suit your needs.

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Courses

Description

Combine the capabilities of IoT with AI.  You'll discover how to combine AI algorithms with IoT hardware to build intelligent decision-making and real-time data analysis systems. You will be guided by practical projects as you develop IoT apps driven by AI for a variety of sectors.

Term

2 Hours/Day for two days per week for 8 weeks

Learning Objective

  1. Learn how to integrate AI capabilities into IoT devices, enabling intelligent data processing and decision-making at the edge.
  2. Understand how AI models can make real-time decisions based on IoT data, enabling automation and optimization of IoT systems.
  3. Apply the knowledge gained through practical projects, including building AI-powered IoT prototypes and solutions.

Course Content

  • Understanding the fundamentals of the Internet of Things (IoT) and Artificial Intelligence (AI)
  • Exploring the convergence of IoT and AI technologies
  • Learning how to connect and configure IoT devices
  • Setting up sensors and actuators for data collection
  • Collecting and preprocessing sensor data
  • Introduction to data cleaning and feature extraction
  • Basics of machine learning algorithms for IoT applications
  • Building predictive models for IoT data
  • Leveraging AI for real-time decision-making in IoT systems
  • Implementing automation and control logic
  • Addressing security and privacy concerns in IoT
  • Introduction to edge computing and edge AI
  • Using cloud platforms for centralized data storage and processing
  • Strategies for scaling AI and IoT solutions
  • Discussing ethical issues in AI and IoT
  • Presenting final projects, including AI-powered IoT prototypes

Description

Dive deep into the fascinating world of deep learning in this course. You'll explore neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and advanced deep learning techniques. Through hands-on coding exercises and projects, you'll gain proficiency in building and training deep learning models for tasks like image recognition, natural language processing, and more.

Term

 2 Hours/Day for two days per week for 6 weeks

Learning Objective

  1. Gain a deep understanding of the fundamental building blocks of neural networks, including neurons, layers, and activation functions.
  2. Dive into the concepts of deep learning, including deep neural networks, deep architectures, and their advantages over shallow models.
  3. Master the backpropagation algorithm to train neural networks effectively, adjusting weights and biases to minimize errors.
  4. Explore CNNs and their application in image and video analysis tasks, including image classification and object detection.
  5. Learn about RNNs and their ability to handle sequential data, enabling applications like natural language processing and time series prediction.
  6. Discover how deep learning is used in NLP tasks, including sentiment analysis, text generation, and language translation.
  7. Apply deep learning to tasks like object detection and image segmentation, which are crucial in computer vision applications.

Course Content

  • Introduction to Neural Networks
  • Deep Neural Networks (DNNs)
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Advanced Architectures
  • Transfer Learning and Model Fine-Tuning

Description

Dive into the world of data science and machine learning with this in-depth course. Learn data analysis, data visualization, statistical modeling, and machine learning techniques using Python and popular libraries like TensorFlow and sci-kit-learn. Graduates are equipped to work on data-driven projects across various industries.

Term

2 Hours/Day for two days per week for 10 weeks

Learning Objective

  1. Providing students with the abilities and information necessary to effectively handle and visualize data. 
  2. Master data collecting, cleaning, and transformation, preparing them to efficiently handle datasets from the real world.
  3. Become proficient in using Python for data analysis and visualization, making them important assets in decision-making based on data in a variety of industries.

Course Content

  • Introduction to Data Science and its Applications.
  • Python basics: syntax, data structures, and control structures.
  • Data cleaning and preprocessing techniques.
  • Data visualization with Matplotlib and Seaborn.
  • Linear Regression for regression tasks.
  • Logistic Regression for classification tasks.
  • K-Means Clustering for grouping data.
  • Principal Component Analysis (PCA) for dimensionality reduction.
  • Support Vector Machines (SVM) for classification.
  • Time Series Analysis and Forecasting.
  • Introduction to Neural Networks.
  • Natural Language Processing basics and text analysis.
  • Model deployment and interpretability.
  • Big Data processing using frameworks like Spark.
  • Ethics in Data Science and AI.

Description

Our NLP bootcamp will help you unleash the power of language. You'll learn how to handle and analyze text data in this curriculum, as well as how to create chatbots, sentiment analysis models, and language translation tools. You'll be equipped after completing the bootcamp to work on innovative NLP projects and further the field's quick development.

Term

8 hours/day for 2 days

Learning Objective

  1. Become familiar with popular NLP libraries and frameworks like NLTK, spaCy, and Transformers.
  2. Be capable of text preprocessing, cleaning, normalizing and text classification
  3. Understand NER and its applications
  4. Learn how to generate text using techniques like Markov chains, recurrent neural networks (RNNs), and transformers.
  5. Develop skills in text summarization
  6. Build chatbots and conversational AI systems using NLP techniques
  7. Analyze and extract sentiments, opinions, and emotions from text data.

Course Content

  • Introduction to NLP and its Applications
  • Key NLP challenges and tasks
  • Text cleaning, tokenization, and stemming
  • Stopword removal and special character handling
  • Understanding text classification
  • Building a basic text classifier
  • Introduction to NER and its use cases
  • NER models and libraries
  • Exploring sentiment analysis and sentiment lexicons
  • Introduction to text generation techniques
  • Creating text using RNNs or transformers
  • Topic modeling with LDA or NMF
  • Text summarization methods (extractive and abstractive)