Machine Learning vs Deep Learning

Machine learning and deep learning are increasingly popular these days and both of these skills are under a broader term called Artificial Intelligence (AI).

Machine and deep learning skills are highly sought after in the tech industry. Based on the World Economic Forum’s Future of Jobs 2020 Report, 93% of companies believe AI will be a pivotal technology to drive growth and innovation. Due to its high demand, it is also evident that machine learning engineer jobs are often open for hire.

On the other hand, not many may know about deep learning or how it differs from machine learning. Hence, we will be breaking down what machine and deep learning are about and introducing some machine learning examples!

What is Machine Learning

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As its name suggests, machine learning is described as teaching a machine to learn so that it can take actions and make decisions without the need for human intervention. This is extremely useful as it helps to automate tasks faster than humans can.

For example, businesses make use of machine learning applications to recognise certain patterns and trends to make predictions. These predictions may include finding out what appeals to their customers or how a product can be improved.

Types of Machine Learning

However, machine learning is also a very broad term and there are in fact, many types of machine learning.

You may ask ‘what are the types of machine learning?’

Listed below are the main types of machine learning:

1.) Supervised learning

Supervised learning can be described as a task-driven learning process, where the machine is trained to learn by labelled data to predict outcomes accurately.

One supervised machine learning example is where your inbox is smart enough to classify spam emails and would move them directly into the spam folder, separating them from your primary inbox.

2.) Unsupervised learning

Unsupervised learning is described to be a data-driven learning process. It analyses and clusters unlabelled datasets through an algorithm that discovers hidden patterns or data groupings.

This also means that this method of machine learning is incredibly helpful for things like exploratory data analysis, cross-selling strategies and even image recognition.

In short, the goal of unsupervised learning is to find differences and similarities between the data.

3.) Reinforcement learning

Reinforcement learning is a process where the machine learns in an interactive environment via trial and error.

Unlike supervised and unsupervised learning, reinforcement learning works like a reward and punishment system. Upon acquiring a negative result, the algorithm will be forced to reiterate until it finds a better result.

One application of reinforcement machine learning is Youtube recommendations. For instance, after watching a video, Youtube will recommend other videos that may similarly interest you. In turn, if you start watching a video and end up not finishing it, Youtube will learn what you dislike and would not recommend such videos in future.

What is Deep Learning?

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Now that we’ve covered some machine learning examples and the types of machine learning, let’s talk about deep learning.

Deep learning is actually a subset of machine learning. It is a process that attempts to mimic the human brain via the use of machine learning neural networks. It is also capable of learning from a large amount of data.

Additionally, you may or may not have heard of what a ‘deep learning model’ is. A deep learning model is a file that is saved after you run a machine learning algorithm on training data.

Based on that data, the model is able to make certain predictions. This means that you no longer have to painstakingly code everything!

Building a deep learning model can help us achieve things more efficiently and most of the time, more accurately than humans! Coding everything will take us a long time, especially to run it!

Types of Deep Learning

So, what are the types of deep learning?

Deep learning uses artificial neural networks to run algorithms, they can be classified into 3 main types:

1.) Convolutional neural network (CNN)

CNN is the most common technique used in deep learning and is known for image processing applications or analysing visual imagery.

Personally, I worked on a deep learning image recognition group project using CNN, during the final year of my polytechnic studies.

My groupmates and I developed a model using CNN to create a computer-aided diagnosis system for an eye condition – Diabetic Retinopathy. The purpose of this was to provide a second opinion to eye specialists in their diagnosis.

This is one such example of the CNN technique in deep learning, where the model is trained with a large amount of image datasets to learn and predict the different severity stages of the condition.

2.) Recurrent neural network (RNN)

RNN is a technique that works with time series or sequential data. Unlike CNN where the input and output are independent of each other, the output of this technique is dependent on its previous inputs.

One notable use of RNN is Google Translate! It uses the RNN technique and acts as an enabler for the translation process to occur!

In short, RNN is used for time-related and sequential problems!

Differences between machine learning and deep learning

So, what are the differences between machine learning and deep learning?

Machine learning focuses on enabling computers to perform certain tasks without explicit programming, whereas deep learning is simply algorithms that are structured in layers to create an artificial neural network to make intelligent decisions.

To know more about how they differ, we have broken down and summarised more factors below!

Differences

Machine learning

Deep learning

Subset of artificial intelligence

Subset of Machine learning

Can train on a small dataset

(Typically seconds to hours)

Requires a large dataset to train

(Can take up to weeks)

Trains on CPU

Requires GPU to train

Takes less time to train from data

Takes a long time to train from data

Limited tuning capabilities

Can be tuned in many different ways

 

Machine learning programming languages & applications

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In this section, we will be listing down the top programming languages that are most used in machine and deep learning.

However, do note that there isn’t a ‘best’ programming language in this case since different programming languages have different purposes in machine learning.

1.) Python

Python is no doubt the most common programming language out there. It is also very popular due to the fact that it’s easier to learn compared to the other programming languages.

Python also has useful libraries such as Numpy, pandas, matplotlib, TensorFlow and sklearn, which come in handy for machine learning.

Some examples of Python machine learning applications include:

  • Web mining
  • Sentiment analysis
  • Chatbots/Natural language processing

If you have no knowledge in machine learning or programming, learning Python should be the first step for you!

2.) C and C++

C is one of the oldest programming languages in history and C++ only came out a couple of years after C was invented. Both languages carry more syntax rules and are more commonly known to be used for game developments and large systems. However, they are also known to be used for some applications in machine learning.

Here are a few examples of C and C++ machine learning applications:

  • Robot locomotion
  • AI in games
  • Network security and cyber-attack detection

 

 

3.) Java

Java is also another popular programming language that is used for machine learning and data science. It has useful libraries for machine learning, which include weka, mallet, apache mahout, deeplearning4j and many more!

Here’s a few examples of Java machine learning applications:

  • Customer support management
  • Network security and cyber-attack detection
  • Bioengineering/Bioinformatics

4.) R

R programming language is a great programming language in the machine learning community and it is commonly used for statistical computing, analysis and visualisation purposes.

The following are some examples of R machine learning applications:

  • Sentiment analysis
  • Fraud detection
  • Bioengineering/Bioinformatics

 

5.) JavaScript

With JavaScript, developers can bring AI into the web and create more intelligent web applications. JavaScript machine learning is also guaranteed to run on most electronic devices, granting access to most users! An important use of JavaScript machine learning is also model customisation.

And here are more examples of JavaScript machine learning applications:

  • Customer support management
  • Search Engine
  • Industrial maintenance diagnostics

 

Machine and deep learning courses

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Are you keen to pursue a career in tech that involves machine and deep learning? Doing courses online is great as they give you a better sense of what they are really about. Ultimately, it’ll help you to make an informed decision.

Since deep learning is a subset of machine learning, we strongly advise taking a course on machine learning first, before diving into its subset.

Here are some of the best machine and deep learning courses by far (2022)!

1.) Coursera

Course Title: Machine Learning

Offered by: Stanford

Cost: S$108 (Free to audit)

Ratings: 4.9/5.0

Estimated duration to complete: 61 hours

Prerequisites: None, Beginner level

Currently, approximately 4.5 million people have enrolled into the course. One notable fact is that 11% of them have started a new career upon completing the course, while 15% them have received tangible career benefit from this course.

Course Title: Deep Learning Specialization

Offered by: Deeplearning.AI

Cost: S$67 per month (Free to audit)

Ratings: 4.9/5.0

Estimated duration to complete: 5 months (8h per week)

Prerequisites: None, Beginner level

For this deep learning specialisation course, approximately 650,000+ people have enrolled and 6% of them have started a new career upon completion!

2.) Google AI

Course Title: Machine Learning Crash Course

Offered by: Google

Cost: Free

Estimated duration to complete: 15 hours

Prerequisites: A decent level of programming

There will be 25 lessons, 30+ exercises and real-world case studies for you to try out! If you aren’t particularly interested in getting a certificate, then this will be the best course for you.

3.) Fast.ai

Course Title: Introduction to Machine Learning for Coders!

Offered by: University of San Francisco

Cost: Free

Ratings: 4.9/5.0

Estimated duration to complete: 24 hours in 12 Weeks (8h per week)

Prerequisites: 1 year of coding experience and high school level Mathematics

This machine learning course will be great for students who already have prior experience in coding. This free course will assume that learners are familiar with mathematical topics, which include linear algebra, probability and calculus.

 

4.) Udemy

Course Title: Deep Learning A-Z™: Hands-On Artificial Neural Networks

Offered by: Udemy

Cost: S$108.98 (You are eligible to use your Skillsfuture credits)

Estimated duration to complete:

Prerequisites: basic Python and machine learning knowledge, and high school level Mathematics.

Similar to the previous course, this deep learning course will be great for those who have basic Python knowledge, a good understanding of high school level Mathematics and basic machine learning knowledge.

5.) Edx

Course Title: Deep Learning Professional Certificate

Offered by: IBM

Cost: USD 525.60 for full program experience (Free to audit)

Estimated duration to complete: 8 months (2 to 4 hours per week)

Suitable for: Advanced level programmers who are serious about a career in deep learning

This course is extremely costly and is definitely for those who are passionate about a career in tech. You could always attempt the full course and consider paying for the certification later.

Conclusion

It’s a good idea to learn machine and deep learning skills if you’re keen to pursue a career in tech. Technology is constantly evolving, you will need to constantly stay up to date with new tools, software and skills in order to remain relevant in the field.

Although a career in tech is likely to guarantee a fat paycheck, it is not a career for everyone. Always do your research or invest some time into relevant courses to decide if a career in tech is truly for you.

And if you are a Polytechnic or JC graduate, you might want to check out: Which local university to apply for? NUS, NTU, SMU, SIT, SUTD or SUSS?

Lastly, we hope that this article has helped you to understand machine and deep learning better or clear some misconceptions that you may have had.

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