We have researched well, and this article will give you an insight into the best machine learning courses you can learn.
Distance learning has taken the world by storm, and it is now easy to learn technical courses that could have cost you a fortune from a conventional university. All over the world, students had turned to online learning for skill acquisition in machine learning. Coursera is a world-renowned educational distance learning institute that partners with various leading universities and top IoT/IT Companies in providing flexible and affordable job-relevant online learning courses.
Do you know why the largest Silicon Valley companies favor Remote working and recruitment? Simple! The Competitive Edge. You don’t want to be left behind in the fast-paced jet age. While some individuals and businesses are making the mistake of hanging onto legacy processes and technologies mainly because they feel no urgency to learn and adopt trending technologies, many have taken the bull by the horn.
Big and small size businesses, AI practitioners, and non-technical professionals need to learn newer and best machine learning courses relevant in today’s highly dynamic labor market and incorporate this into their business structure environment to stand a chance to compete. Here, I will share the online best Machine Learning Courses available in Coursera.
Machine Learning Specialization (DeepLearning.AI and Stanford University)
We are excited to propose you the newest and best Machine Learning Courses!
This Specialization summarizes all the advancements made in the ten years that have passed since the original course was launched. It includes:
- Lectures and assignments that are graded to teach Python, instead of Octave/Matlab
- Three in-depth courses provide a comprehensive introduction to machine learning, unsupervised learning and supervised learning.
- There are dozens of interactive graphs and code notebooks that can be used to aid learners in understanding concepts.
DeepLearning.AI and Stanford Online collaborated to create the Machine Learning Specialization, an online foundational program. This program is easy to follow and will help you understand the basics of machine learning.
Andrew Ng is the instructor for this Specialization. He is an AI visionary who has conducted critical research at Stanford University as well as groundbreaking work at Google Brain Baidu and Landing.AI.
This 3-course Specialization is an updated version Andrew’s Machine Learning course. It has been rated 4.9 out 5 by learners who have taken it since 2012 and now we are excited for the new version.
This specialization provides an overview of modern machine learning. It includes supervised learning (multiple linear regressions, logistic regressions, neural networks and decision trees), unsupervised learn (clustering and dimensionality reductions, recommender systems), as well as some of the best practices in Silicon Valley for machine intelligence and machine learning innovation (evaluating models and taking a data-centric approach, improving performance, and many more).
This Specialization will equip you with the knowledge and skills to apply machine learning to real-world problems. The Machine Learning Specialization is the best place for you to begin if you are looking to get into AI and build a career in this field.
Applied Learning Project
You will be able to:
- Create machine learning models using Python’s popular machine learning libraries, NumPy or scikit-learn.
- Train and build supervised machine learning models to predict and classify binary classification tasks. This includes logistic regression, linear regression, and logistic regression.
- Train and build a neural network using TensorFlow for multi-class classification.
- Use best practices in machine learning development to ensure that your models can be generalized to real data and real tasks.
- Create and use decision trees, tree ensemble methods, random forests, and boosted trees.
- Unsupervised learning techniques can be used for unsupervised learning, including clustering or anomaly detection.
- Create recommender systems using a collaborative filtering method and a content-based deeplearning method.
- Create a deep reinforcement learning system.
Deep learning (DeepLearning.AI)
The Deep learning super course offered by DeepLearning.AI as a specialized course in Coursera comes with five (5) course suit packed with theoretical and technical hands-on experience to ensure that you are ready for the labor market. The knowledge gained by learning the deep learning specialized course offers numerous new career opportunities. It is a sort after skill in the technology sector, most especially software engineering.
This course introduces you to the foundation of deep learning and the trending technologies driving Deep Learning. The course outlines are easily arranged and developed to allow a steady rise in understanding the course’s critical aspect and equip you with the technical skill to build and apply connected deep neural networks.
As an enrolled student for the online course, you can learn at your speed, pause your learning or end your subscription at any time during the study.
Completing the course and final practical project earns you a certificate that can help you land that bug IT job.
There are 5 Courses in this Specialization. They are:
- Neural Networks and Deep Learning
- Improving Deep Neural Networks: Hyperparameter tuning, Regularization, and Optimization
- Structuring Machine Learning Projects
- Convolutional Neural Networks
- Sequence Models
Machine learning (University of Washington)
The machine learning course, a package course by the University of Washington via Coursera, offers tremendous opportunities for IT entry levels and a career path for students interested in machine learning.
Machine learning courses taught online by the University of Washington build your skillset in linear regression, ridge regression, statistics, and regression analysis, which are essential and widely used machine learning and statistical tools. Upon completing the course, you will apply your knowledge in predicting given available data, stock prices, formulate a simple regression model, and fit the model using the optimization algorithm.
The course consists of four modules:
- Welcome which is an introduction to Machine learning
- Simple Linear Regression builds on the foundation knowledge.
- Assessing Performance widens your technical know-how and ability to examine both theoretical and practical aspects of machine learning.
- Ridge Regression module examined how the performance of a model varies with increasing model complexity
Machine Learning for All (University of London)
If you’re a beginner, the Machine learning course taught online by the University of London will be right for you. The distant learning course boasts of tutorials, videos and sample questions, and case studies that will develop your understanding of using data to train statistical algorithms. You will learn how Machine learning affects our lives with breakthroughs in AI Technologies like facial recognition, self-driving cars, Robotics, and many more. However, this course only introduce you to the machine learning technology and how it works but does not cover any programming sessions for machine learning like Python and TensorFlow.
The module included in the course outline are:
- Machine learning
- Data features
- Machine Learning in Practice
- Your Machine Learning Project
How Google does Machine Learning (Google Cloud)
This interesting distance learning crash course taught by google will broaden your perspective of machine learning and its real-life approach in today’s technologies. Google simplifies the learning so that anyone with a basic knowledge of computers can easily understand, thereby gaining a skillset in application programming interphase (API), Inclusive ML, Machine Learning, Google Cloud Platform, and Biqquery. The course, which has enrolled thousands of students, qualifies you for a google b certificate, which can land you an excellent job in the IT sector.
This course is packed with the following three (3) modules:
- How Google does ML
- Inclusive ML
- Python Notebooks in the cloud
Diabetic Retinopathy Detection with Artificial Intelligence (Coursera Project Network)
The Diabetic Retinopathy Detection with Artificial Intelligence is a new online course offered at Coursera Project Network. It is an ideal course for Medical practitioners to help detect blindness before it occurs. This course introduces students to the theory and intuition behind Deep Neural Networks, Residual Nets, and Convolutional Neural Networks (CNNs). You will be trained in using this AI technique to detect the type of Diabetic Retinopathy from images.
Below are what you will learn from this course:
- Understand the Problem Statement and Business Case
- Import Libraries and Datasets
- Perform Data Exploration and Visualization
- Perform Data Augmentation and Create Data Generator
- Understand the Theory and Intuition Behind Convolutional Neural Networks
- Build a ResNet Deep Neural Network Model
- Compile and Train the Deep Neural Network Model
- Assess the Performance of the Trained Model
Machine Learning with TensorFlow on Google Cloud (Google Cloud)
As a professional in technical learning, this course is one of my favorites. If you are interested in full training in Machine learning, Machine Learning with TensorFlow on Google Cloud is highly recommended because of what you will gain. This course will give you an insight into what machine language is. It also helps you master and learns how to write distributed machine learning models that scale in Tensorflow with high-performance predictions and the ability to use the google cloud for a hands-on lab.
You earn a certificate upon completion of the course and hands-on project, which you can share with companies in need of such skill in their workplace.
There are 5 Courses in this Specialization:
- How Google does Machine Learning
- Launching into Machine Learning
- Introduction to TensorFlow
- Feature Engineering
- Art and Science of Machine Learning
Advanced Machine Learning (HSE University)
With over 300k students register for this course and still counting, this course’s popularity in Coursera cannot be overemphasized. The advanced machine learning course will introduce you to deep learning natural language and the Bayesian method with theoretical and real-life scenarios. Skills you will get from this course but are not limited to are Tensorflow, deep learning, Bayesian optimization, feature extraction, Xgboost, and many more.
With these skills, you will be able to apply modern machine learning methods in enterprise development.
This specialization comes in seven (7) components:
- Introduction to Deep Learning
- How to Win a Data Science Competition: Learn from Top Kagglers
- Bayesian Methods for Machine Learning
- Practical Reinforcement Learning
- Deep Learning in Computer Vision
- Natural Language Processing
- Addressing Large Hadron Collider Challenges by Machine Learning
IBM Machine Learning Professional Certificate
This course in the Coursera catalog offered and delivered by IBM is a rich syllabus covering all the vital areas of machine learning, like deep learning and reinforcement learning. This course is intended for students interested in pursuing a career in Machine learning.
The course is a six (6) module content suited for an intermediate student with minimum Python programming and algebra background.
Throughout this Professional Certificate, you will gain exposure to a series of tools, libraries, cloud services, datasets, algorithms, assignments, and projects to provide you with practical skills applicable to Machine Learning jobs.
After completing the course, you will earn a professional certificate from Coursera and a digital badge from IBM. Successful students also stand a chance of getting jobs related to modern AI applications.
Getting Started with AWS Machine Learning (Amazon Web Services)
Choosing Amazon web service machine learning is a fantastic choice if you want to have insights from experts in the field and put the concepts into practice.
In this, you will learn how to build real-life intelligent applications using Amazon AI services like Amazon Comprehend, Amazon Rekognition, Amazon Translate, and others. Apart from that, you will learn how to deploy a model using Amazon SageMaker with built-in algorithms and Jupyter Notebook instance.
Skills you gain from this course include:
- Artificial intelligence
- Machine learning
- Amazon SageMaker
- Natural Language Processing (NLP)
- Computer Vision
The following are the course outline:
- Introduction to Machine Learning
- Machine Learning Pipeline
- Amazon AI Services: Computer Vision
- Amazon AI Services: NLP
- Introduction to Amazon SageMaker
Analyzing Unstructured Data using MongoDB and PySpark
Analyzing Unstructured Data using MongoDB and PySpark is another highly engaging course offered by Coursera Project Network.
Whether you’re looking to start a new career or change your current one, this Certificate course on Coursera helps you become job-ready. You can learn at your own pace from top companies and universities, apply your new skills to hands-on projects that showcase your expertise to potential employers, and earn a career credential to kickstart your new career.
By the end of this project, you will learn how to analyze unstructured data stored in MongoDB using PySpark and connect a MongoDB database with PySpark.
