The Machine Learning Foundation: Your Gateway to the Exciting World of AI

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The Machine Learning Foundation: Your Gateway to the Exciting World of AI

Are you interested in exploring the field of artificial intelligence and machine learning but don't know where to start? Look no further! The Machine Learning Foundation course is designed for individuals who are new to this field and want to gain a strong foundation in its concepts and applications. In today's digital world, machine learning has become a key player in solving complex problems and driving innovation. From image and speech recognition to sentiment analysis and predictive analytics, the applications of machine learning are endless.

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  • (0 Reviews)
  • 5 students enrolled
  • Free
  • Course Includes
  • Machine Learning Basic
  • Supervised Learning
  • Unsupervised Learning


Whatlearn

  • In this Machine Learning (ML) Foundations course, you will be introduced to the main subareas of ML.
  • You will also learn how to construct the dataset for ML model training, how to build the various ML models
  • In addition, you will learn the essential steps for developing various ML pipelines.

CourseContent

7 sections • 9 lectures •
A Gentle Introduction to Machine Learning
14.9mb
A Gentle Introduction to Machine Learning
min
Basic Terminology 1
mb
Basic Terminology 2
Intro Pandas usage
mb
Simple Machine Learning Algorithms for Classification
mb
A Tour of Machine Learning Classifiers Using scikit-learn (part 1)
mb
A Tour of Machine Learning Classifiers Using scikit-learn (Part 2)
mb
Building Datasets
mb
Homework Assignment 1: Titanic Survival Prediction
Homework Template
mb

Requirements

  • English Language Proficiency (Intermediate), Basic Python, Basic Liner Algebra, Basic Probability

Description

In this course, you will learn the following:

  1. The main subareas of machine learning: You will be introduced to the different subareas of
    machine learning, such as supervised learning, unsupervised learning, and reinforcement
    learning (which will not be covered in this course).
  2. How to construct a dataset for machine learning model training: You will learn how to
    select and prepare the data that will be used to train a machine learning model. This
    includes steps such as data cleaning and preprocessing, feature selection, and splitting the
    data into training and testing sets.
  3. How to build various machine learning models: You will learn how to build and implement
    different types of machine learning models, including supervised and unsupervised models.
    You will also learn how to tune the hyperparameters of these models to optimize their
    performance.
  4. How to test and evaluate the performance of machine learning models: You will learn how
    to use various metrics to evaluate the performance of machine learning models, including
    accuracy, precision, recall, and F1 score. You will also learn how to use techniques such as
    cross-validation to accurately assess the performance of a model.
  5. The essential steps for developing machine learning pipelines: You will learn how to build
    end-to-end machine learning pipelines, including steps such as data preparation, model
    training, model evaluation, and model deployment. You will also learn how to deploy
    machine-learning models in a production environment.

Textbooks

Python Machine Learning - Third Edition | Packt Mathematics for Machine Learning | Companion webpage to the book “ Mathematics for Machine Learning”. Copyright 2020 by Marc Peter Deisenroth,  A. Aldo Faisal, and Cheng Soon Ong. Published by Cambridge University Press. Pattern Recognition and Machine Learning (Information Science and  Statistics): Bishop, Christopher M.: 9780387310732: Amazon.com: Books

 

 

 

 

 

 

 

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About Instructor

instructor
About Instructor

Currently, I am working at Mid Sweden University as an Associate Senior Lecturer (Assistant Professor). I am also a former postdoctoral researcher in the Networking Intelligence Lab, Department of Computer Science and Engineering at Kyung Hee University, South Korea. I worked as a team leader for the Software Star-LAB project, which aims to develop an autonomous system capable of generating the most appropriate domain-specific deep learning model for the edge computing environment. Besides, I was involved in many different impactful machine learning projects and collaboration with various organizations, including the “Institute for Information and Communications Technology Promotion” (Korea) and “Electronics and Telecommunications Research Institute” (Korea). Reliable and self-disciplined educator, I worked as a teaching assistant in Machine Learning and AI Networking class, where I engaged in lecture preparation, reviewing student assignments, and class projects. I supervised several graduate students for their research direction, publications, and thesis. Currently, I am collaborating with Trinity Healthcare Enterprise to build an integrated one-stop solution for the Myanmar healthcare industry. Moreover, I am working with PIT Technology to develop the Unified Agricultural Platform for Myanmar’s agricultural industry. Dedicated and self-motivated, I am striving to make a meaningful contribution to AI-related research and development projects. With over 45 research publications in well-known journals and conferences related to intelligent network caching and machine learning, a Korean company acquired one of my patents related to the Deep Learning model generation framework in 2019. I am also very enthusiastic about collaborating with international research teams to develop more innovative concepts and emerging next-generation products.

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