Fundamentals of Machine Learning
Fundamentals of Machine Learning
Last updated 6/2022
Duration: 8h 40m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 3.91 GB
Genre: eLearning | Language: English
Last updated 6/2022
Duration: 8h 40m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 3.91 GB
Genre: eLearning | Language: English
This course will start your career in data science.
What you'll learn
- Learn about the fundamental principles of machine learning
- Build customized models to use for different data science projects
- Build customized Deep Learning models to start your own data science career
- Start your data science career and connect with the tutor in industry
Requirements
- No prior mathematical or programming knowledge required. Some python programming experience is helpful.
Description
This is an introduction course of machine learning. The course will cover a wide range of topics to teach you step by step from handling a dataset to model delivery. The course assumes no prior knowledge of the students. However, some prior training in python programming and some basic calculus knowledge is definitely helpful for the course. The expectation is to provide you the same knowledge and training as that is provided in an intro Machine Learning or Artificial Intelligence course at a credited undergraduate university computer science program.
The course is comparable to the Introduction of Statistical Learning, which is the intro course to machine learning written by none other than the greatest of all:Trevor HastieandRob Tibshirani! The course was modeled from the "Introduction to Statistical Learning" from Stanford University.
The course is taught by Yiqiao Yin, and the course materials are provided by a team of amazing instructors with 5+ years of industry experience. All instructors come from Ivy League background and everyone is eager to share with you what they know about the industry.
The course has the following topics:
Introduction
Basics in Statistical Learning
Linear Regression
Clasification
Sampling and Bootstrap
Model Selection & Regularization
Going Beyond Linearity
Tree-based Method
Support Vector Machine
Deep Learning
Unsupervised Learning
Classification Metrics
The course is composed of 3 sections:
Lecture series <= Each chapter has its designated lecture(s). The lecture walks through the technical component of a model to prepare students with the mathematical background.
Lab sessions <= Each lab session covers one single topic. The lab session is complementary to a chapter as well as a lecture video.
Python notebooks <= This course provides students with downloadable python notebooks to ensure the students are equipped with the technical knowledge and can deploy projects on their own.
Who this course is for:
- Beginners in python programming, machine learning, and data science.
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