Machine learning is not a new concept altogether. Researchers had documented various ML-related ideas whose implementation wasn’t possible back in those times. Fast-forward to today, exponentiation in the amount of data along with fascinating hardware advances have given life to machine learning ideas that earlier remained on the paper. AI and machine learning have gained a lot of traction from companies across the world and it is expected that AI can create a business value worth $4 billion by the year 2022.
Machine learning is one of the most sought-after career fields these days, and many professionals are seeking ways to gain the right skills and become job-ready. There are many job roles that ML aspirants can explore, like a machine learning engineer, data scientist, business intelligence analyst, NLP scientist, AI researcher, and so on. The best part – all of these job roles command high salaries in the job market. So, if you are looking for a challenging career, then machine learning might just be the field for you.
Many professionals who want to switch their careers to machine learning don’t have a clear idea of where to get started. You would be glad to know that you can simply enroll in an online machine learning course to learn all the ML concepts from basics to the advanced level and understand what it takes to be an ML engineer. The good news is that you can take an online machine learning course to learn all the concepts of ML from the basics to the advanced level, and understand what it takes to become an ML engineer. It is great to learn something quickly through coding bootcamps because they are completed in a short period of time. As a matter of fact, a lot of online resources are available that offer ML training courses, and it may be a daunting task to find the most reliable one. So, in this article, we have listed down 5 of the best machine learning courses that can be taken online and followed at a comfortable pace.
Let’s get started!
Machine Learning Certification Course
Training provider – Simplilearn
Willing to go into the depths of machine learning topics like supervised and unsupervised learning, classification, regression, and working with real-time data? If yes, then this course by Simplilearn is designed for you. The program has a cutting-edge curriculum that is designed in guidance with industry experts to make you ready for an ML job. With over 58 hours of applied learning approach, you will be able to explore time series modeling, K-Means clustering, decision trees, deep learning fundamentals, and boosting and bagging techniques.
The things unique to this course are the inclusion of more than 25 hands-on exercises, 4 real-life machine learning projects with integrated labs, and simulation test papers for self-assessment. The course is best suited for analytics managers, information architects, data scientists, and fresh graduates embarking on a career in data science.
Machine Learning A-Z: Hands-On Python and R in Data Science
Training Provider – Udemy
This course is designed to help learners gain expertise in machine learning on Python and R and making accurate predictions. You will get access to 44 hours of on-demand video where you will learn how to make robust machine learning models, handle advanced techniques like dimensionality reduction, reinforcement learning, NLP, and deep learning. The syllabus is divided into 45 sections with a total of 320 lectures that you can follow at your own convenience. This best-selling course on Udemy also offers 73 articles and 38 downloadable resources.
Become a Machine Learning Engineer Nanodegree Program
Training Provider – Udacity
This comprehensive training program by Udacity helps you learn advanced machine learning techniques and algorithms and how to package and deploy ML models to a production environment. The instructors will allow you to have practical knowledge using Amazon Sagemaker so as to deploy trained models to a web application and assess the performance of your models. The program syllabus is divided into the following sections:
- Software engineering fundamentals
- Machine learning in production
- Machine learning case studies
- Machine learning capstone
The special features of this nanodegree program include real-world projects from industry experts, technical mentor support, and access to resume support, LinkedIn profile optimization, and GitHub portfolio review. The course can be completed in 3 months if you plan to dedicate 10 hours per week to it.
Machine Learning Specialization
Training Provider – the University of Washington on Coursera
What more could you wish for than learning from the researchers at the University of Washington? Through a series of practical case studies, learners will gain applied experience in important areas of machine learning like prediction, clustering, classification, and information retrieval. This 7-months (3 hours per week) training program will help them create systems that adapt and improve over time, analyze large and complex datasets, and design intelligent applications that can make predictions from data.
Here are the course included in the specialization:
- Machine Learning Foundations: A Case Study Approach
- Machine Learning: Regression
- Machine Learning: Classification
- Machine Learning: Clustering & Retrieval
Machine Learning Fundamentals with Python
Training Provider – Datacamp
This course is great to get started if you want to be a part of the ML revolution. Throughout this 20-hours of in-depth training, you will learn about all the fundamental concepts of machine learning. Upon enrolling, you will experience true interactive learning by going through short video tutorials and hands-on coding exercises. You will understand how to apply your coding skills to a wide range of datasets to solve real-life problems in your browser. The training program is divided into six courses:
- Supervised learning with Scikit-Learn
- Unsupervised learning in Python
- Linear classifiers in Python
- Case study – School budgeting with machine learning in Python
- Introduction to deep learning in Python
- Machine learning fundamentals in Python
With so many good options to choose from, when are you going to start your machine learning journey?