Machine learning Course Introduction
Machine learning, which has deep roots in statistics, is quickly evolving as one of the most fascinating and dynamic areas in computer science. Among the many applications of machine learning models in daily life are spam filtering, chatbots, search engines, ad serving, and fraud detection. However, it can be a little overwhelming for a beginner who doesn’t know where to start. This article lists the seven best machine learning courses online from the best platform to learn machine learning that can help you master ML in different fields.
1.Machine Learning: Coursera
Andrew Ng, a Stanford professor, co-founder of Google Brain, Coursera, and the vice president who expanded Baidu’s AI team to hundreds of scientists, is the instructor and creator of this best ML online course.
The course employs the open-source language Octave instead of Python or R for the assignments.
Topics covered in this course:
- Linear Regression with one variable
- Octave/Matlab Tutorial
- Linear Algebra Review
- Linear Regression with multiple variables
- Neural Networks; Learning
- Neural Networks; Representation
- Logistic Regression
- Regularization
- Anomaly Detection
- Support Vector Machines
Why this course?
Ng’s explanations of the course subject are incredibly thorough and natural.
- Each algorithm’s mathematical requirements are thoroughly discussed, along with essential calculus explanations and a review of linear algebra.
- Although the course is mainly self-contained, it would be beneficial to have some prior knowledge of linear algebra.
Who is this course for?
This is the best machine learning course for beginners to get the hang of the basics of Machine Learning.
Course rating?
This course has a rating of 4.9 from 3,831 learners.
Duration:
Thirty-three hours needed to complete this course.
2.Deep Learning Specialization: Coursera
Anyone interested in learning about neural networks and deep learning and how they work should take this specialization, which is a more advanced course series.
Topics covered in this course:
- Introduction to Machine Learning
- Shallow Neural Networks
- Neural Network Basics
- Improving Neural Networks; Hyperparameter tuning, Regularization, and optimization
- Sequence Models
- Structuring Machine learning projects
Why this course?
- Each course’s assignments and lectures use the TensorFlow framework for neural networks and the Python programming language.
- Given that you’ll receive a similar lecture format but now be exposed to utilizing Python for machine learning, this is a great follow-up to Ng’s Machine Learning course.
Who is this course for?
This course is for people who have a basic understanding of Machine Learning.
Course rating:
The rating is 4.9, given by 126,005 learners.
Duration:
Five months are needed to complete the course. It means 9 hours/week.
3.Introduction to Artificial Intelligence Course: Simplilearn
The Introduction to AI is the best machine learning course that gives students a complete understanding of the fundamentals of AI workflows and concepts while providing a thorough view of the artificial intelligence environment.
Topics covered in this course:
- Basics of deep learning and machine learning
- Designing of Machine learning workflows
- Performance metrics while devising algorithms
Why this course?
You’ll get a thorough introduction to artificial intelligence (AI) and what it means to build your own clever models.
Who is this course for?
All facets of artificial intelligence are covered in this comprehensive course, which places particular emphasis on machine learning ideas, including supervised and unsupervised learning. Although a prior understanding of ML ideas is not required, you should be familiar with Python programming and statistics.
Duration:
Two weeks are needed to complete this course.
4.Machine Learning with Python: Coursera
Another introductory course, but this one is entirely concerned with the most basic machine learning algorithms. The combination of the instructor, slide animations and algorithm explanations works quite well to give you a natural sense of the fundamentals. This is one of the best courses for machine learning with Python.
Topics covered in this course:
- Regression
- Introduction to Machine Learning
- Classification
- Final Project
- Clustering
- Recommender systems
Why this course?
This course makes use of Python and is less heavy on the algorithms’ underlying mathematics.
- You’ll have the opportunity to launch an interactive Jupyter notebook in your browser for each module to practice the new ideas you’ve just learned.
- Each notebook serves to solidify your understanding and provides step-by-step guidance for applying an algorithm to actual data.
Who is this course for?
This course is for individuals who possess a basic understanding of Machine learning. It will give you a working understanding of Python and methods for data analysis and visualization maths from at least high school.
Course ratings:
It has a rating of 4.7 from 13 064 learners.
Duration:
Thirteen hours are needed to complete the course.
5.Data Science and Machine Learning Bootcamp: Udemy
The Data Science & Machine Learning Bootcamp is one of the best machine learning courses providing a thorough machine learning certification course designed for students who want to understand machine learning, including classification, Regression, and the usage of Neural Networks.
You’ll learn how to use tools like Tensorflow and receive a thorough introduction to the practical aspects of developing machine learning algorithms with the help of this course.
Topics covered in this course:
- Linear Regression for predictive models
- Leveraging Tensorflow for handwritten ML Portfolio
- How to use neural networks
- Python programming for machine learning
Why this course?
Before building your own neural networks for deep learning projects, you’ll begin with a machine learning introduction. Additionally, you’ll gain a thorough understanding of programs like Matplotlib and NumPy.
This is one of the best machine learning courses that will give you a behind-the-scenes look at what you can do with ML algorithms over the course of 207 lectures and 13 educational modules.
Who is this course for?
The comprehensive Python programming and data science Bootcamp provide all the information newcomers want.
Course rating:
The rating is 4.6 from 39,459 learners.
Duration:
It will take 41 hours to complete this course.
6.Machine Learning for all: Coursera
The University of London’s “Machine Learning for All” program aims to increase everyone’s access to AI. The online course examines how machine learning (ML) technology enables the future of business analysis, self-driving cars, and facial recognition. Moreover, it is the best machine learning online course with certificate.
Topics covered in the course:
- Data representation
- Testing Machine learning projects
- Opportunities and dangers of Machine Learning
- Collecting datasets
Why this course?
Easy-to-use hands-on resources are available to help you improve your educational experience.
Who is this course for?
A great starting point for a technical career in machine learning is promised by this special machine learning training course. In addition, regardless of your level of technical expertise, you can engage with AI concepts.
Course rating:
4.7 rating from 2,999 learners.
Duration:
It will take 22 hours to complete the course.
7.Introduction to Machine Learning: Udacity
The “Introduction to Machine Learning” is one of the best online machine Learning courses for aspiring programmers since it provides a behind-the-scenes look at the computer science and statistics ideas that are crucial to machine learning.
Topics covered in this course:
- Use Naive Bayes and Scikit Learn
- Support vector machines and decision trees
- Clustering
- Feature scaling
- Choosing the suitable ML algorithm
Why this course?
This could be the answer for you if you’re seeking the best machine-learning course to improve your current Python programming skills.
Who is this course for?
The Introduction to Machine Learning course is designed for people who have some prior coding experience, despite being relatively simple to follow. In addition, you’ll need to be well-versed in data science and familiar with fundamental statistical techniques.
Duration:
Ten weeks are needed to complete this course.
Click here to browse the course.
Summary:
Machine learning focuses on devising algorithms that can absorb, store and learn from data. As a result, machine learning tools have increased in value over time for practically every business.
The correct solutions can aid everything from business analytics duties to financial forecasts. We hope this article could provide some great insight into the different machine learning courses available. Choose the course that suits your style of learning.
Enjoy the, earning!!
FAQs:
What is a Machine Learning course?
Machine learning is a field of artificial intelligence and computer science that encompasses the creation of software and algorithms that can generate predictions based on data. It also covers topics like supervised learning and unsupervised learning.
How long does it take to learn machine learning?
The duration of a machine learning engineering curriculum is roughly six months. It could take longer if someone is just starting out and has no prior experience with computer programming, data science, or statistics.
How to get a Machine Learning certificate?
In this area, there are numerous options for machine learning certification courses. Each one offers various levels of machine learning expertise.
How to learn Machine learning for beginners?
- Understand the prerequisites.
- From A to Z of ML Theory.
- Dive Deep Into the Crucial Subjects.
- Complete projects.
- Study and Use a Variety of ML Tools.
- Learn ML Algorithms From the Ground Up.
- Choose a course in machine learning.
- Request an internship.
Can you learn machine learning on your own?
Although the extensive range of ML tools and skills may appear overwhelming, it is undoubtedly possible to master ML on your own. Moreover, you can learn a lot about machine learning on your own, thanks to the abundance of free and paid resources available online.
