Top 10 Courses For Machine Learning In 2023
Machine learning has become extremely significant and necessary in the tech sector in today’s fast-paced climate.
Companies all over the world are looking for someone who can use machine learning to tackle pressing problems.
Additionally, if you want to enhance your profession and earn well, the Machine Learning course will help you find suitable prospects.
The best 10 machine learning courses that will provide you a strong foundation in the area and a rewarding career are presented below.
Learning Machines Submitted by Andrew Ng
One of the most popular places to study anything for nothing online is Coursera. One of the most well-known and praised machine learning courses on the internet is Andrew NG’s on Coursera.
The course also covers the fundamentals of machine learning, such as logistic regression, linear regression, neural networks, and other related concepts.
Therefore, the most important aspect is that it gives students hands-on experience with ML applications that are used in the real world, like natural language processing and picture recognition.
Python-Specific Applied Data Science (Coursera)
The University of Michigan is offering a thorough online course called Applied Science with a Python specialisation on Coursera.
The course covers a wide range of topics, including machine learning, data analysis, and data visualisation.
Additionally, it provides many Python data science tools and libraries like Pandas, Numpy, and Matplotlib.
Python Introduction To Machine Learning
On Coursera, IBM is offering a beginner-friendly course. The course covers all the principles of machine learning, including classification, regression, clustering, and unsupervised or supervised learning.
But in addition, the course provides a variety of ML models and methods, including random forests, neural networks, and decision trees.
Nanodegree in Machine Learning Engineering from Udacity
Udacity’s Machine Learning Engineer Nanodegree programme includes all the essentials and is created to help you launch a lucrative career.
Also included in the course are the data preparation, model creation, and deployment stages of an ML life cycle.
Specialisation in Deep Learning (Coursera)
It is a branch of machine learning that concentrates on creating artificial neural networks.
Recurrent networks, convolutional networks, and generative models are all covered in-depth in this extensive Coursera course on deep learning.
(edX) Applied Machine Learning
It is a Columbia University-specific course offered on edX. Unsupervised and supervised learning, model selection, feature engineering, and evaluation are just a few of the subjects covered by the system.
The course also provides several machine learning models and methods, such as decision trees, neural networks, and k-nearest neighbours.
Bootcamp for Data Science and Machine Learning (Udemy)
It’s a thorough course offered by the dependable online learning community Udemy. The system provides information on a variety of subjects, such as deep learning, machine learning, data analysis, and data cleansing.
The course provides practical exposure to well-known ML technologies and packages including TensorFlow and Scikit-Learn.
Kelleher and Tierney's book Applied Machine Learning
The practical use of machine learning methods to address contemporary issues is the main subject of Kelleher and Tierney’s course Applied Machine Learning.
Additionally, it covers a wide range of subjects, such as model choice, evaluation, and implementation.
Students will also learn how to employ methods from the field of machine learning, such as regression, clustering, classification, and recommendation systems, to solve problems.
Google's Machine Learning Crash Course
It is a straightforward course that covers the fundamentals of ML, including model evaluation and featured engineering.
Andrew Ng's Advanced Machine Learning
It is specifically designed for students who are proficient in machine learning fundamentals and want to go further into more complex subjects.
Deep learning, neural networks, natural language processing, and reinforcement learning are some of the topics covered.