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Overview

Audience: Architects, developers and Engineers who want to get familiar with basic principles of machine learning.

Engineers who feel enthusiastic about diving into the world of various machine learning tools and frameworks

Course Outline

AI/ Machine Learning with python program delve deeper into the AI/Machine Learning fundamental concepts further explaining Machine Learning algorithms and their implementation. A serious head start and practical approach on building deployable machine learning models by offering an in-depth understanding of the three major types of machine learning algorithms, comprising of supervised, unsupervised, and reinforcement learning using the most widely used python programming language.

Pre-requisites

Programming: Basic Python programming skills, a capability to work effectively with data structures. Experience with IDE’s Jupyter Notebook/Spyder applications (Good to have)
 A basic understanding of Statistics, Probability, matrix vector operations and notation
Basic command line operations
Resources will be shared for the participants to self-study Math and Python

Course objectives

Learning how to use main troubleshooting techniques of machine learning
Going through the complete process of building machine learning systems
Appreciate the breadth & depth of AI/ML applications and use cases in real-world scenarios.
Import and wrangle data using Python libraries and divide them into training and test datasets
Deployment of machine learning models
Identify limitations of existing tools
Understand current machine learning trends and opportunities that they bring
Understand how to formulate new problems into machine learning terms

Benefits of the Bootcamp

  • 16 hours of AI Bootcamp in your city
  • Learning how to use main troubleshooting techniques of machine learning
  • Going through the complete process of building machine learning systems
  • Appreciate the breadth & depth of AI/ML applications and use cases in real-world scenarios.
  • Import and wrangle data using Python libraries and divide them into training and test datasets
  • Deployment of machine learning models
  • Identify the limitations of existing tools
  • Understand current machine learning trends and opportunities that they bring
  • Understand how to formulate new problems into machine learning terms

Skills That You Will Build

  • Data Exploration

  • Python Programming

  • Data Visualization

  • Machine Learning

  • Regression

  • Artificial Intelligence

Day 1

Day 1 – AI Explained (Morning Session)

  • Introduction to Artificial Intelligence
  • Applications, Industries, and growth
  • AI case-studies and use-cases
  • Techniques used for AI
  • Different methods used for AI
  • Frameworks used for AI
  • The future of AI
  • Real time Application Video

Day 1 – Machine Learning (Noon Session)

  • Introduction to Machine Learning
  • What is ML?
  • Applications of ML
  • Why ML? and its applications
  • Machine learning methods
  • Machine learning algorithms (Regression, Classification, Clustering, Association)
  • Brief introduction python libraries
  • Pandas, Numpy,Matplotlib,Scikit-learn
  • Creating a Machine Learning Model
  • Types of ML algorithms
  • Training and Testing Data
  • Importing the Libraries
  • Importing the Dataset
  • Demo: Creating a machine model

Day 2

Day 2 – Algorithms (Morning Session)

  • Supervised and Unsupervised Learning
  • Regression (With a Use Case)
  • What is regression
  • Applications of regression
  • Types of regression
  • Fitting the regression line
  • Classification (With a Use Case)
  • How is the classification used?
  • Applications of classification
  • Unsupervised Learning – Clustering (With a Use Case)
  • Application of Unsupervised learning, examples, and applications

Day 2 – Deep Learning (Noon Session)

  • Introduction to Deep Learning (with a Use Case )
  • Why deep learning?
  • Neural networks
  • Applications of neural networks
  • Biological Neuron vs Artificial Neuron
  • Artificial Neural networks, layers
  • Convolutional NN and Recurrent NN
  • Introduction to Natural Language Processing (NLP)- (with a simple usecase)
  • What is NLP?
  • Why NLP
  • Applications and Components of NLP
  • NLP techniques
  • Tensorflow, Keras etc.,
  • Generative Adversial Networks (GAN’s)
  • Deployment of Machine Learning Models
  • What is model deployment?
  • Options to implement Machine Learning models
  • Saving the Machine learning Model : Sterlization $ Descerilization.
  • Creating an API using Flask

25% Off

at 2 day AI Bootcamp in Boston

Details

Start:
March 11 @ 9:00 am
End:
March 12 @ 6:00 pm
Cost:
$893
Event Category:
Website:
http://35.175.39.72/

Venue

BABSON COLLEGE – BOSTON
Babson College - Boston High Street, Downtown
Boston, MA 02110-2321 United States

Organizer

NowaLabs
Email:
support@nowa.ai