Top 5 Best Online Machine Learning Courses for Beginners

Top 5 Best Online Machine Learning Courses for Beginners

Searching for best Machine Learning online Courses? Then you are in the right place. After a lot of research, we have listed out some of the top best machine learning courses for you. Let's explore this list

Course No 1: Machine Learning Specialization - Build Intelligent Applications. Master machine learning fundamentals in four hands-on courses.

From which University: University of Washington

Course Instructors:

a) Carlos Guestrin (Amazon Professor of Machine Learning) 

b) Emily Fox (Amazon Professor of Machine Learning)

Basic information about this course:

This Specialization from leading researchers at the University of Washington introduces you to the exciting, high - demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.

For more details about the course, check out the below-given link

Explore ML Course: ML Specialization

Course No 2: Machine Learning

From which University: Stanford

Course Instructor: 

Andrew Ng (CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist, Baidu and founding lead of Google Brain)

Basic information about this course:

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

For more details about the course, check out the below-given link

Explore ML Course: ML by Stanford

Course No 3: Distributed Machine Learning with Apache Spark (Learn the underlying principles required to develop scalable machine learning pipelines and gain hands-on experience using Apache Spark.)

From which University: UC Berkeley

Course Instructors: 

a) Ameet Talwalkar (Assistant Professor of Computer Science,
University of California, Los Angeles)
b) Jon Bates (Spark Instructor, Databricks)
Basic information about this course:
Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. Learning algorithms enable a wide range of applications, from everyday tasks such as product recommendation and spam filtering to bleeding edge applications like self-driving cars and personalized medicine. In the age of ‘big data’, with datasets rapidly growing in size and complexity and cloud computing becoming more pervasive, machine learning techniques are fast becoming a core component of large-scale data processing pipelines.
These statistics and data analysis course introduce the underlying statistical and algorithmic principles required to develop scalable real-world machine learning pipelines. We present an integrated view of data processing by highlighting the various components of these pipelines, including exploratory data analysis, feature extraction, supervised learning, and model evaluation. You will gain hands-on experience applying these principles using Spark, a cluster computing system well-suited for large-scale machine learning tasks, and its packages and spark.mllib. You will implement distributed algorithms for fundamental statistical models (linear regression, logistic regression, principal component analysis) while tackling key problems from domains such as online advertising and cognitive neuroscience.
For more details about the course, check out the below-given link
Explore ML Course: ML by Berkeley
Course No 4: Practical Machine Learning
From which University: Johns Hopkins University
Course Instructors:
a) Jeff Leek (Ph.D. Associate professor at Bloomberg School)
b) Roger D. Peng (Ph.D. Associate Professor at Bloomberg School)
c) Brian Caffo (Ph.D. Professor at Bloomberg School)
Basic information about this course:
One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide a basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model-based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.
For more details about the course, check out the below-given link
Explore ML Course: ML by Johns Hopkins

Course No 5: Introduction to Machine Learning
From which University: Data Camp
Course Instructors:
a) Filip Schouwenaars
b) Sebastian Perez Saaibi
Basic information about this course:
This online machine learning course is perfect for those who have a solid basis in R and statistics but are complete beginners with machine learning. After a broad overview of the discipline's most common techniques and applications, you'll gain more insight into the assessment and training of different machine learning models. The rest of the course is dedicated to a first reconnaissance with three of the most basic machine learning tasks: classification, regression, and clustering.
For more details about the course, check out the below-given link

Explore ML Course: ML by Data Camp

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