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Udacity Machine Learning vs. Simplilearn Machine Learning - for your ML Career

Machine learning
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Machine learning

Highlights

Machine learning technology, a subfield of artificial intelligence, is making tremendous transformations in the way organizations are making use of data to gain insights for their core businesses.

Machine learning technology, a subfield of artificial intelligence, is making tremendous transformations in the way organizations are making use of data to gain insights for their core businesses. Machine learning skills offer an opportunity to pursue exciting career paths for professionals in the field, whether in machine learning engineering, data science, data analysis, or the more specialized machine learning roles. For this, pursuing a machine learning online course at the earliest opportunity makes sense. This is because, as time goes by, as demand for Machine Learning skills rises, there will be many more professionals joining the bandwagon as competition in the job market becomes stiffer. How about enjoying the fruit of taking an early leap into machine learning?



Today, we look at two leading online machine learning online training providers and what they have to offer.

Udacity Machine Learning Course

The Introduction to Machine Learning course offered by Udacity is an online course broken down into ten video lessons along with small practical projects at the end of each lesson. This course is designed to equip learners with knowledge in implementing prewritten machine learning algorithms for data representation using Python language.

This course is a part of Udacity's Data Analyst Nanodegree program. Like most of Udacity's courses, the introduction to machine learning course is self-paced and focuses on equipping professionals with hands-on experience. Thus, this course syllabus comes with several real-world practical exercises that learners undertake to test their understanding of the concepts they have learned.

Apart from preparing you to undertake Udacity's Data Analyst nanodegree program, this course also prepares you for a machine learning engineering career.

Mode of delivery

This is a self-paced learning course whose content is delivered through pre-recorded videos. It is packaged with real-world use cases, real-world practice exercises, and interactive quizzes at the end of each lesson.

To test your skills, you are required to complete a project at the end of the course. The final project involves developing models to identify persons of interest in the Enron email and financial dataset.

Course objective

You will learn how to investigate data for hidden trends and patterns using machine learning algorithms. By the end of this course, you should be able to analyze data using machine learning algorithms. You will be familiar with using Python language, prewritten Machine Learning algorithms, and various Python libraries to analyze data sets.

Lessons and skills you will acquire

This course consists of 10 lessons, including:

1) Welcome to machine learning

2) Naive Bayes

3)Support vector machines

4) Decision trees

5) Choose your own algorithm

6) Datasets and questions

7) Regression

8) Outliers

9) Clustering

10) Feature scaling

Course length

The introduction to machine learning course is a self-paced learning course that should run through ten weeks, taking a lesson or two consistently each week.

Intended for

While this class can be good for beginners, it is best for intermediates who already have foundational machine learning knowledge.

Prerequisites

To take this course, you should have a Python programming and basic statistics background.

The key takeaways

This course is focused on fainting practical experience, which leaves you with a deep understanding of machine learning foundational concepts. Secondly, it is self-paced, making it possible for the more aggressive learners to complete in a shorter time. Mentors are available to help you throughout your coursework.

Simplilearn Machine Learning Course

Simplilearn's machine learning certification course is designed to offer learners an in-depth understanding of the fundamental concepts and techniques of machine learning as well as the knowledge to draw predictions from data using machine learning models. You will gain a practical understanding of developing both supervised and unsupervised learning models, implementing classification, regression, and time-series models to work with datasets and real-time data using Python programming language.

The machine learning certification course is designed to prepare professionals for a career in machine learning engineering. Are you looking at not only undertaking a machine learning course but also acquiring an industry-recognized certification for it?

Simplilearn's machine learning certification course is the course to undertake.

Mode of delivery

There are two options for taking lessons for this course. You can enroll for the Flexi-Pass live online classes, giving you access to recorded instructor videos for your reference.

Alternatively, you can opt for the self-paced learning option that gives you access to pre-recorded videos for the 12 modules.

Throughout the course, learners will learn hands-on through four real-life industry projects and several exercises during the modules.

Course objectives

You will learn the concepts and techniques of machine learning and understand how machine learning is transforming the world. This course comes packaged with several real-world use cases as well as real-world practical illustrations that will help a professional to build the skills such as regression, clustering, classification, and time-series modeling required to thrive in a machine learning role.

By the end of this course, you should be able to develop machine learning models to apply to data sets to gain insights and make predictions.

Lessons

The course consists of 12 modules, including:

1) Course introduction

2) Introduction to AI and machine learning

3) Data processing

4) Supervised learning

5) Feature engineering

6) Supervised learning classification

7) Unsupervised learning

8) Time series modeling

9)Ensemble learning

10) Recommender systems

11) Text mining

12) Project highlights

Skills and techniques you will learn

1) Supervised and unsupervised learning

2) Time series modeling

3) Linear and logistic regression

4) Kernel SVM

5) KMeans clustering

6) Naive Bayes

7) Decision tree

8) Random forest classifiers

9) Boosting and Bagging techniques

10) Deep Learning fundamentals

Course length

You will receive 58 hours of applied instructor-led training.

To earn the certification, you should attend a full batch of online training and submit a completed project for the flexi-pass learners or complete at least 85% of the course and submit one completed project for the self-paced learners.

Intended for

The machine learning certification course by Simplilearn is designed for learners with intermediate-level machine learning knowledge and skills in various roles, including business analysis, data analysis, information architecture, data science, machine learning, and others.

Prerequisites

To take this course, you need a college-level understanding of statistics and mathematics as well as Python programming knowledge.

The key takeaways

Simplilearn offers a blended learning approach that gives learners access to both live instructor-led training and recorded-videos. Training is flexible as professionals can also opt for the self-paced mode of learning.

Two courses, mathematics refresher and statistics essentials for data science, are offered for free upon enrolment in this course. They give learners a solid foundation for the machine learning course and therefore should be taken before taking the machine learning course.

Finally, Simplilearn offers 24/7 responsive teaching assistance and support to its learners.

Conclusion

The machine learning market is expected to grow exponentially to reach $8.81 billion by 2022. With such high adoption rates of machine learning technology in businesses, there is bound to be a sharp rise in demand for machine learning professionals.

Do you intend to launch a career in machine learning?

There is no good or bad course of these two as your choice depends entirely on your requirements and individual goals. You are probably looking at a more hands-on approach to learning, a certification to demonstrate your skills, or even a launchpad for a career in machine learning engineering. Perhaps you will be comfortable with available and supportive students' assistance. These are a few factors that will help you make a choice that befits your needs.

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