If you want a quick crash course in Machine Learning then this article is perfect for you. Here I’m providing you with two free Machine Learning crash courses with a net duration of fewer than 3 hours. These courses are best for someone who is interested in ML but doesn’t know where to start with and feels overwhelmed by other tutorials. The second one on our list also provides a certificate of completion. So let’s briefly look into the details of these courses.
By Udemy
Features:
The free version of this Learn Keras: Build 4 Deep Learning Applications course provides only learning content. Whereas its paid version has a 30-day money-back guarantee and Certificate of Completion with Instructor direct Q&A facility. The primary focus of this course is to get you up and running with deep learning as quickly as possible. The reason behind using Keras in this course is that it’s one of the earliest libraries to learn for deep learning. The course covers different machine learning algorithms and their use cases in each video but their primary focus is on- Linear Regression, dense Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. In short, this course is for those looking for a quick intro to Deep Learning.
Course Duration:
The course contains one and a half hours of on-demand video.
Prerequisites:
It requires basic knowledge of Python and familiarity with Data Science and NumPy.
Modules:
The course contains 3 sections with 8 lectures as follows:
- Introduction
- Introduction
- Getting Set Up with Colab
- Traditional Machine Learning
- AI vs Machine Learning vs Deep Learning
- What is Linear Regression
- Linear Regression in Keras
- Neural Networks
- Kears Deep Neural Network (DNN)
- Keras Convolutional Neural Network (CNN)
- Keras Recurrent Neural Network (LSTM)
By Great Learning
Features:
Our second course is Basics of Machine Learning provided by Great Learning. This course gives an introduction to Machine Learning along with brief knowledge about Supervised Machine Learning, Linear Regression, Pearson’s Coefficient, and Coefficient of Determinant. Moreover, you will learn and understand the purpose of its algorithms and go through its practical use case and you’ll be introduced to the mathematical space where ML occurs. Later on, you will move to the critical concepts like Supervise Mchine Learning, Linear Regression, and so on. At the end of this course, you will look through a case study to understand these concepts better. So grab the certificate shown below by completing the course and a Quiz at the end.
Course Duration:
The course has 2.5 hours of video content and 1 quiz.
Prerequisites:
As this is a beginner-level crash course hence needs no special prerequisites and is the same as mentioned in the above course.
Modules:
- Introduction to Machine Learning
- Linear Regression
For Complete Machine Learning Specialization Course Click Here.
