Algorythm™| Intro to Machine Learning

Algorythm™| Intro to Machine Learning

Predicting the future is not magic, it's artificial intelligence. – Dave Waters

By Kat Usop, MSHI

Select date and time

Wednesday, November 6 · 7 - 10pm CST

Location

Algorythm Online Classroom

. . Chicago, IL 00000

Refund Policy

Refunds up to 7 days before event
Eventbrite's fee is nonrefundable.

About this event

Learning the fundamentals of machine learning is essential for any professional who wants to activate a career in data science, artificial intelligence, or related fields. Machine learning is the process of creating systems that can learn from data and make predictions or decisions based on that data. By understanding the basic concepts and techniques of machine learning, you will be able to apply them to various problems and domains, such as natural language processing, computer vision, recommender systems, and more.

You will also be able to evaluate the performance and limitations of different machine learning models, and choose the most appropriate one for your task.

Learning the fundamentals of machine learning will help you keep up with the latest developments and innovations in this fast-growing and dynamic field.

By the end of the Course, the student will be able to:

  • Understand the basic concepts and terminology of machine learning, such as supervised, unsupervised, and reinforcement learning, classification, regression, clustering, etc.
  • Apply appropriate machine learning techniques to solve real-world problems
  • Implement and use common machine learning algorithms and frameworks
  • Analyze and interpret the results and limitations of machine learning models
  • Explore the ethical and social implications of machine learning applications, such as fairness, privacy, accountability, etc.

Rotational Q&A forums about these specific models:

  • Supervised learning vs Unsupervised learning
  • Learning and logic regression
  • K-means clustering
  • Decision Tree
  • Boosting and bagging algorithm
  • Time series modeling
  • Kernel SVM
  • Naive Bayes
  • Random forest classifiers

WHO IS THIS #ALGORYTHM FOR?

  • (Non-tech) Entrepreneurs who want to build AI startups
  • Career switchers from non-tech background
  • Students exploring AI space

Machine learning is an exciting and rapidly evolving field that offers many opportunities for personal and professional growth. Whether you want to enhance your career prospects, solve real-world problems, or simply satisfy your curiosity, joining an #Algorythm course in machine learning can help you achieve your goals in applying this powerful technology.

Exciting times ahead!

Reading Appetizers:

ALGORYTHM | Happy Customers, AI-Powered Supermarkets?

ALGORYTHM | Machine learning, where is it headed?

    Frequently asked questions

    ROI: What is the average income of an entry-level machine learning engineer?

    Entry-level machine learning engineers earn an average of $96,000 annually, or anywhere between $70,000 and $132,000 (US)

    Organized by

    Hi! I am a nomad Eng'r in Health & AI space (NYIT, ex-Duke, ex-Mizzou). I create & share knowledge from real-world experiences. I build sustainable ventures while traveling long-term. PS If min. seats aint filled, 1:1 virtual lecture session is coordinated, sweet deal!

    From $77.77