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Artificial Intelligence (AI) and Machine Learning (ML) Foundation Course

2k learners

The AI and ML foundation course is a complete beginner’s course with a blend of practical learning and theoretical concepts. This course offers you the basic fundamentals of AI and Machine Learning.

  • 1-year access to audio-video lectures

  • Course completion certificate

  • 100% money-back guarantee

USD 150

Group Training

Looking for a personalized training for a group (3 or more participants) at your preferred location?
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Course Overview

The most advanced phenomenon in the history of Computer generations is the technological development in the field of Artificial Intelligence(AI). Here in this tutorial course, we bring you the fundamentals. Provide basics of Deep learning by giving an insight of Convolutional Neural Networks and Recurrent Neural Networks. All this will be done using TensorFlow, the programming library for Deep Learning. This course is a must-have for all those who are exploring the field of AI and ML.
  • 1-year access to audio-video lectures
  • Course completion certificate

Course Outline

Skills

Gain a complete understanding of Convolutional Neural Networks, Recurrent Neural Networks, and other Deep Architectures along with their uses in complex raw data using TensorFlow. 


Alignment

This course has been designed by handpicked industry experts to develop your expertise through case-studies, practice modules, and real-time projects.


Outcome

At the end of the course, you will be ready to apply these concepts at work to handle Classification & Regression problems.


  • Understanding AI
    How do machines work?
    Can machines see the world?
    Google Photos: Machine with a Vision
    Understanding Natural Language
    Learning Complex Games
    What is Artificial Intelligence (AI)
    What is Machine Learning (ML)
    How Machine builds the logic
    Machine's Goal: Understanding Loss function
    The World of Gradient Descent - I
    The World of Gradient Descent - II
    Linear Regression Algorithm
    ML Math: Understanding Vector and Matrix
    What's Next
  • Using TensorFlow
    Pre-requisite: What to know before going further
    Components of Machine Learning
    Installation Instructions
    Building Hello World in TensorFlow
    Notebook: Hello World in TensorFlow
    Understanding Computational Graph
    Computational Graph for Linear Regression
    Exercise: Boston Housing Prices Predictor
    What is Data Normalization?
    Exercise: Boston Housing Prices with Data Normalization
    Notebook: Housing Price Predictor
    Assignment: Housing Predictor on Google Colab
    What's Next
  • Simplify ML using Keras
    Understanding role of Keras in TensorFlow
    Keras vs TensorFlow's Lower Level APIs
    Exercise: Building Linear Regression model in Keras
    Notebook: Boston Housing Predictor in Keras
    Exercise: Using ML model for Prediction
    Notebook: Predict Housing Prices using ML Model
    Assignment: How many Bikes are needed?
    Regression vs Classification
    Math in Classification
    Using SoftMax in Classification
    Loss and Accuracy in Classification
    Exercise: Classify Handwritten numbers - I
    Exercise: Classify Handwritten numbers - II
    Notebook: Hand-written digits Predictor with DL
    Mini-batching in ML
    Exercise: Mini-batching in ML
    Notebook: Mini-batching for MNIST Dataset
    Exercise: Prediction using Classification model
    Improving ML model - Hyperparameters
    What's Next
  • Deep Learning
    Problem with Linear Algorithm
    How to capture Complex Logic?
    What is Deep Learning?
    Exercise: Deep Learning on MNIST Classification
    Notebook: MNIST Classification with Deep Learning
    Using TensorBoard: Visualizing ML Model
    Notebook: Using TensorBoard
    Activation functions in Deep Learning
    Learning rate Decay
    Dropout for Overfitting
    Optimizers: Momentum & Nestrove Momentum
    Optimizers: Adam, Adagrad
    Hyper-parameters in Deep Learning
    Exercise: ReLU, Adam & Dropout
    Notebook: Applying ReLU, ADAM and Dropout
    Assignment: CIFAR-10 Classification
    What's Next
  • Recurrent Neural Networks Download full course agenda/ brochure

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Frequently asked questions

Faqs

1. Any hardware requirements or specifications needed to attend the training?
All that you need is a computer/laptop with a high-speed internet connection for distraction-free learning.



2. Do I need to download any software for the training?
We will be using OpenSource software like TensorFlow, Python, etc. You will be guided with instructions to install these during our training.
3. What if I miss one day of training?
Even if you miss live-online class, you will be able to go through the topics using video sessions which will be made available to you. You can always connect with us, by dropping an email to support@greycampus.com and we will be happy to assist you.
4. I do not have any technical experience, can I join?
This program does not require any technical or programming experience. Even if you are inclined towards programming or have some exposure, you are eligible to take up this training.

Additionally, this program is bundled with Python Programming self-learning course that will help you learn the basics of programming.

5. Does GreyCampus guarantee 100% money back?
Without BootCamp: Training sessions without BootCamps are governed by a 3-day trial policy. In the unlikely case of being dissatisfied with the course content, a delegate must reach out to the support team at support@greycampus.com within 3 days from the date of purchase. After verification, the entire amount will be credited in the original payment mode.
 
The refund policy mentioned above will stand void and thus unenforceable in case a delegate is found to be involved in any of the following scenarios:
Accessing more than 30% of the content available
Downloading eBook or any offline material
Attempting one or more mock tests
Using exam vouchers
BootCamp: BootCamp training refunds are subject to the 1st day of 1st training class refund policy. Upon finding the training unsatisfactory, the delegate must inform GreyCampus within 24 hours from the time when the first session began. All such communications should be directed to the support team reachable at support@greycampus.com. In such cases, the delegate will be refunded the entire amount paid in the original mode of payment.
 
The guarantee is valid for participants who have paid the entire enrollment fee.
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