Artificial Intelligence Master Course Algorithms and Applications

    Artificial Intelligence Master Course

    Algorithms and Applications 

    A 15-Weeks Guided Online Course 

    Sri Amit Ray Compassionate AI Lab

    In this 15-week online course, you will learn and understand the main algorithms and approaches to artificial intelligence and deep learning. You will learn the techniques to improve your AI and machine learning model building skills and algorithm development skills. It includes total 15 online one-on-one classes, for real-world case studies, and hands-on practices and exercises.

    The aim of the course is to:

    • be able to apply the algorithms for different applications and interpret the results
    • be able to build models and algorithms and adjust parameters
    • understand the applicability of the algorithms to different types of data and problems along with their strengths and limitations

    The case studies share real world stories from teams who have designed AI-driven products using human-centered AI based practices. Primarily you can use R programming or Python programming for practicing the examples and projects.

    The Course Contents

    The course structure varies, depending on individuals' experiences, needs, and interests. However, there will be a total of 15 classes. The general content of the modules of the course are as follows:

    Module 1: Artificial Intelligence Fundamentals

    1. Human Intelligence vs Artificial Intelligence
    2. Human brain vs Artificial Intelligence
    3. General Artificial Intelligence vs Ethical Artificial Intelligence
    4. Ethical Artificial Intelligence vs Compassionate Artificial Intelligence
    5. Difference between Artificial Intelligence, Machine Learning, and Deep Learning
    6. Recent trends of AI from robots to humanoids
    7. Machine Learning Frameworks
    8. Future challenges of Artificial Intelligence 

    Module 2: Basic Machine Learning Algorithms and Practices

    1. Hypothesis Testing
    2. Linear Regression
    3. Logistic Regression
    4. Clustering
    5. Analysis of Variance (ANOVA)
    6. Principal Component Analysis
    7. Naive Bayes
    8. Decision Tree
    9. Random Forest
    10. Support Vector Machines
    11. K Nearest Neighbors
    12. Gradient Boosting algorithms 
    13. Neural Networks
    14. Ensemble Methods

    Module 3: Feature Engineering and Model Building

    1. Data preparation
    2. Min-max Scaling, Standardization, Log Transformation, One hot Encoding, etc.
    3. Data visualization
    4. Dimensionality reduction
    5. Model selection
    6. Model training
    7. Model performance evaluation  - Confusion matrix, ROC, AUC etc.
    8. Parameter initialization
    9. Avoiding overfitting and underfitting
    10. Loss functions and Hyper-parameter optimization

    Module 4: Deep Learning Algorithms and Practices

    1. Neural Network Fundamentals 
    2. Gradient Descent, Backpropagations, and Optimization Algorithms
    3. Deep Neural Network (DNN)
    4. Convolutional Neural Network (CNN)
    5. Recurrent Neural Network (RNN)
    6. Long Short Term Memory (LSTM)
    7. Restricted Boltzmann Machine (RBM)
    8. Generative Adversarial Networks –GANs

    Module 5: Transfer Learning Fundamentals and Practices

    1. Transfer learning based on CNN models
    2. Transfer learning based on NLP models
    3. Building Transfer learning new models
    4. Transfer learning accuracy measurements and parameter adjustments

    Module 6: Reinforcement Learning

    1. Markov Models and Markov Decision Process
    2. Model Free Reinforcement Learning - SARSA Learning
    3. Model Free Reinforcement Learning - Q-Learning
    4. Deep Neural Network Q-Learning
    5. Model Based Reinforcement Learning 

    Module 7: Artificial Intelligence Projects

    You will be doing your home assignments and projects with Python depending on your needs, experience, interest, and the availability of your computing facilities and timings.

    Some of our popular Python Machine Learning Projects are:

    1. Sentiment Analysis
    2. Detecting Parkinson's Disease
    3. Detection of Fake News
    4. Next word prediction
    5. Movie Recommender
    6. Customer Segmentation
    7. Speech Emotion Recognition

    The common datasets used for the projects and the home assignments are as follows:

    1. MNIST digits classification dataset
    2. CIFAR10 small images classification dataset
    3. CIFAR100 small images classification dataset
    4. IMDB movie review sentiment classification dataset
    5. Reuters newswire classification dataset
    6. Fashion MNIST dataset
    7. Boston Housing price regression dataset

     

    Course Fees $750 USD

     

                                         Details
    Course Fees: $750 USD (Rs. 50,000/- INR)
    Duration: 15 Weeks (Total 15  One Hour Sessions)
    Location: Online via Skype / ZOOM/ WhatsApp
    Benefits: You will learn the algorithms, theory, and practices of modern artificial intelligence and deep learning.
    Study Material: You will get Ten PDF Files as Study Materials.

     

    Register Now

     

    Course Certificates

    Artificial Intelligence Algorithms

    E-Certificate 

    After completing  the  Artificial Intelligence and Deep Learning Algorithms and Applications Master Course successfully  you can obtain the E-certificate from Sri Amit Ray Compassionate AI Lab. 

    How it Works

    After you register and submit your payment for an online session, you are notified by email and offered a set of choices for the dates and times for the online session. You choose one, and confirm by email. You can always email us, if you have a special preference.

    Then the session follows - a session designed for you to learn the AI course tailored to your needs, and experiences.  

    The online classes are provided by Sri Amit Ray, as one-to-one online Skype / Zoom / WhatsApp sessions at mutually convenient date and time. The duration of each session is about an hour. The course includes guided instructions, discussions, and study material.

    Upon completion of the online registration and payment process, you will be informed by email, about the date and time of your meditation classes, which are mutually adjustable.

    Register Now

    Extra Classes

    After the completion of 15 classes for this course, you can request extra classes for in-depth understanding or discussion of a particular topic of AI, which can be arranged for a fees of $50 USD, per extra class.

     

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