Best Course to Study in Malaysia to Work in Machine Learning Career

What is Machine Learning? How Machine Learning Works and How to Work in this Career in  Malaysia?

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Machine Learning is an application of Artificial Intelligence (AI) as it gives devices the ability to learn from their experiences and improve their self without doing any coding. Machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information. Processing large volumes of information would be Data Science. Data scientists primarily deal with huge chunks of data to analyse the patterns, trends and more. These analysis applications formulate reports which are finally helpful in drawing inferences.

While the terms Data science, Artificial Intelligence (AI) and Machine learning fall in the same domain and are connected to each other, they have their specific applications and meaning. There may be overlaps in these domains every now and then, but essentially, each of these three terms has unique uses of their own. To work in Machine Learning, the basic qualification would be a degree in Artificial Intelligence (Ai), Data Science or Computer Science.

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What is Machine Learning and the Best Course to Study to Work in this Job?

Arthur Samuel coined the term Machine Learning in the year 1959. He was a pioneer in Artificial Intelligence and computer gaming, and defined Machine Learning as “Field of study that gives computers the capability to learn without being explicitly programmed”.

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

But, using the classic algorithms of machine learning, text is considered as a sequence of keywords; instead, an approach based on semantic analysis mimics the human ability to understand the meaning of a text.

Machine learning is also a data analytics technique that teaches computers to do what comes naturally to humans and animals: learn from experience. Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model. The algorithms adaptively improve their performance as the number of samples available for learning increases.

Machine learning algorithms find natural patterns in data that generate insight and help you make better decisions and predictions. They are used every day to make critical decisions in medical diagnosis, stock trading, energy load forecasting, and more. For example, media sites rely on machine learning to sift through millions of options to give you song or movie recommendations. Retailers use it to gain insight into their customers’ purchasing behavior.

With the rise in big data, machine learning has become a key technique for solving problems in areas, such as:

• Computational finance, for credit scoring and algorithmic trading
  Image processing and computer vision, for face recognition, motion detection, and object detection
• Computational biology, for tumor detection, drug discovery, and DNA sequencing
• Energy production, for price and load forecasting
• Automotive, aerospace, and manufacturing, for predictive maintenance
• Natural language processing, for voice recognition applications

What is Machine Learning Used For?

Machine learning is used in internet search engines, email filters to sort out spam, websites to make personalised recommendations, banking software to detect unusual transactions, and lots of apps on our phones such as voice recognition.

What are the Applications of Machine Learning?

Machine Learning algorithms help in building intelligent systems that can learn from their past experiences and historical data to give accurate results. Many industries are thus applying machine learning solutions to their business problems, or to create new and better products and services. Healthcare, defense, financial services, marketing, and security services, among others, use Machine Learning in their applications and processes.

Here is a Look of some Applications of Machine Learning:

Facial recognition/Image recognition

The most common application of machine learning is Facial Recognition, and the simplest example of this application is the iPhone X. There are a lot of use-cases of facial recognition, mostly for security purposes like identifying criminals, searching for missing individuals, aid forensic investigations, etc. Intelligent marketing, diagnose diseases, track attendance in schools, are some other uses.

Automatic Speech Recognition (ASR)

Automatic speech recognition is used to convert speech into digital text. Its applications lie in authenticating users based on their voice and performing tasks based on the human voice inputs. Speech patterns and vocabulary are fed into the system to train the model. Presently ASR systems find a wide variety of applications in the following domains:

• Medical Assistance
• Industrial Robotics
• Forensic and Law enforcement
• Defence & Aviation
• Telecommunications Industry
• Home Automation and Security Access Control
• IT and Consumer Electronics

Financial Services

Machine learning has many use cases in Financial Services. Machine Learning algorithms prove to be excellent at detecting frauds by monitoring activities of each user and assess that if an attempted activity is typical of that user or not.
Financial monitoring to detect money laundering activities is also a critical security use case of machine learning.

Machine Learning also helps in making better trading decisions with the help of algorithms that can analyse thousands of data sources simultaneously. Credit scoring and underwriting are some of the other applications.

The most common application in our day to day activities is the virtual personal assistants like Siri and Alexa.

Marketing and Sales

Machine Learning is improving lead scoring algorithms by including various parameters such as website visits, emails opened, downloads, and clicks to score each lead. It also helps businesses to improve their dynamic pricing models by using regression techniques to make predictions.

Sentiment Analysis is another essential application to gauge consumer response to a specific product or a marketing initiative. Machine Learning for Computer Vision helps brands identify their products in images and videos online. These brands also use computer vision to measure the mentions that miss out on any relevant text. Chatbots are also becoming more responsive and intelligent with the help of machine learning.

Healthcare

A vital application of Machine Learning is in the diagnosis of diseases and ailments, which are otherwise difficult to diagnose. Radiotherapy is also becoming better with Machine Learning taking over.

Early-stage drug discovery is another crucial application which involves technologies such as precision medicine and next-generation sequencing. Clinical trials cost a lot of time and money to complete and deliver results. Applying Machine Learning based predictive analytics could improve on these factors and give better results.

Machine Learning technologies are also critical to make outbreak predictions. Scientists around the world are using these technologies to predict epidemic outbreaks.

How is Machine Learning Related to Ai?

Machine Learning is a subset of Artificial Intelligence. Machine Learning is the study of making machines more human-like in their behaviour and decisions by giving them the ability to learn and develop their own programs. This is done with minimum human intervention, i.e., no explicit programming.

The learning process is automated and improved based on the experiences of the machines throughout the process. Good quality data is fed to the machines, and different algorithms are used to build ML models to train the machines on this data. The choice of algorithm depends on the type of data at hand, and the type of activity that needs to be automated.

Machine learning is also a data analytics technique that teaches computers to do what comes naturally to humans and animals: learn from experience. Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model. The algorithms adaptively improve their performance as the number of samples available for learning increases.

How is Machine Learning Different from Programming?

Now you may wonder, how is it different from traditional programming? Well, in traditional programming, we would feed the input data and a well written and tested program into a machine to generate output. When it comes to machine learning, input data along with the output is fed into the machine during the learning phase, and it works out a program for itself. To understand this better, refer to the illustration below:

Why Should You Learn Machine Learning?

Machine Learning can automate many tasks, especially the ones that only humans can perform with their innate intelligence. Replicating this intelligence to machines can be achieved only with the help of machine learning.

With the help of Machine Learning, businesses can automate routine tasks. It also helps in automating and quickly create models for data analysis. Various industries depend on vast quantities of data to optimize their operations and make intelligent decisions.

Machine Learning helps in creating models that can process and analyze large amounts of complex data to deliver accurate results. These models are precise and scalable and function with less turnaround time. By building such precise Machine Learning models, businesses can leverage profitable opportunities and avoid unknown risks.

Image recognition, text generation, and many other use-cases are finding applications in the real world. This is increasing the scope for machine learning experts to shine as a sought after professionals.

How does Machine Learning work?

Machine learning is a form of artificial intelligence (AI) that teaches computers to think in a similar way to how humans do: Learning and improving upon past experiences. It works by exploring data and identifying patterns, and involves minimal human intervention.

The types of machine learning algorithms are mainly divided into four categories: Supervised learning, Un-supervised learning, Semi-supervised learning, and Reinforcement learning. Supervised learning: All materials are “labeled” to tell the machine the corresponding value to make it predict the correct value.

What is Supervised Learning?

Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.

The supervised learning model has a set of input variables (x), and an output variable (y). An algorithm identifies the mapping function between the input and output variables. The relationship is y = f(x).

The learning is monitored or supervised in the sense that we already know the output and the algorithm are corrected each time to optimise its results. The algorithm is trained over the data set and amended until it achieves an acceptable level of performance.

We can group the supervised learning problems as:

• Regression problems – Used to predict future values and the model is trained with the historical data. For example, Predicting the future price of a product.
• Classification problems – Various labels train the algorithm to identify items within a specific category. For example, Disease or no disease, Apple or an orange, Beer or wine.

What is Unsupervised Learning?

Unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.

This approach is the one where the output is unknown, and we have only the input variable at hand. The algorithm learns by itself and discovers an impressive structure in the data. The goal is to decipher the underlying distribution in the data to gain more knowledge about the data.

We can group the unsupervised learning problems as:

• Clustering: This means bundling the input variables with the same characteristics together. For example, grouping users based on search history
• Association: Here, we discover the rules that govern meaningful associations among the data set. For example,  People who watch ‘X’ will also watch ‘Y.’

What is Semi-supervised Learning?

Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning, since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy.

Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it / learn from it. Otherwise, acquiring unlabeled data generally doesn’t require additional resources.

In semi-supervised learning, data scientists train model with a minimal amount of labelled data and a large amount of unlabelled data. Usually, the first step is to cluster similar data with the help of an unsupervised machine learning algorithm.

The next step is to label the unlabelled data using the characteristics of the limited labelled data available. After labelling the complete data, one can use supervised learning algorithms to solve the problem.

What is Reinforcement Learning?

Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning.

This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.

In this approach, machine learning models are trained to make a series of decisions based on the rewards and feedback they receive for their actions. The machine learns to achieve a goal in complex and uncertain situations and is rewarded each time it achieves it during the learning period.

Reinforcement learning is different from supervised learning in the sense that there is no answer available, so the reinforcement agent decides the steps to perform a task. The machine learns from its own experiences when there is no training data set present.

Which Language is Best for Machine Learning?

Python is hands down the best programming language for Machine Learning applications due to the various benefits mentioned in the section below. Other programming languages that could to use for Machine Learning Applications are

• R
• C++
• JavaScript
• Java
• C#
• Julia, Shell
• TypeScript
• Scala

Python is famous for its readability and relatively lower complexity as compared to other programming languages. Machine Learning applications involve complex concepts like calculus and linear algebra which take a lot of effort and time to implement. Python helps in reducing this burden with quick implementation for the ML engineer to validate an idea.  Another benefit of using Python in Machine Learning is the pre-built libraries.

Learning Python for Machine Learning

Python provides flexibility in choosing between object-oriented programming or scripting. There is also no need to recompile the code; developers can implement any changes and instantly see the results. You can use Python along with other languages to achieve the desired functionality and results.

Python is a versatile programming language and can run on any platform, including Windows, MacOS, Linux, Unix, and others. While migrating from one platform to another, the code needs some minor adaptations and changes, and it is ready to work on the new platform.

Difference Between Machine Learning and Artificial Intelligence and Data Science

Artificial Intelligence or AI manages more comprehensive issues of automating a system utilizing fields such as cognitive science, image processing, machine learning, or neural networks for computerization. On the other hand, Machine Learning influences a machine to gain and learn from the external environment. The external environment could be anything such as external storage devices, sensors, electronic segments among others.

Also, artificial intelligence enables machines and frameworks to think and do the tasks as humans do. While machine learning depends on the inputs provided or queries requested by users. The framework acts on the input by screening if it is available in the knowledge base and then provides output.

At its core, data science is a field of study that aims to use a scientific approach to extract meaning and insights from data. Data Science is a combination of information technology, modeling, and business management. Machine learning, on the other hand, refers to a group of techniques used by data scientists that allow computers to learn from data.

What is the Job Demand & Salary for Machine Learning in Malaysia?

Because Machine Learning falls under Artificial Intelligence (Ai) and Data Science, so we will look at the job demand and salaries for these 2 in Malaysia.

AI specialists mostly work at research centres of universities, small AI development companies, banking sectors, automotive industries, healthcare facilities and government agencies.

Many of the popular recent applications of AI in industry have been based on Machine Learning (ML), which gives computers the ability to learn, improve business decisions, increase productivity, detect disease, forecast weather, etc.

Malaysia’s national ICT agency Multimedia Development Corporation (MDeC) has unveiled a plan, supported by seven public and private institutes of higher learning (IHLs), to increase the number of local data scientists from the current 80 to 2000 by the year 2020.

Statistics show that by the year 2020, there will be about two million job openings for data professionals and that the demand for people with this knowledge and skill will outstrip supply by a ratio of two to one. It’s a global phenomena which is already in motion and Malaysia has set its sights on developing 20,000 data professionals and 2,000 data scientists by 2020.

Data science fresh graduates can demand starting pay in the range of RM4,000-RM8,000 — making it the highest paid entry level job in the country today.

An experienced professional in the field can demand up to RM15,000 a month.

2020 Hays Salary Guide Malaysia – Digital Technology – AI (Salaries are yearly in ‘000 RM)

• AI Developer (Java/C/C++/Python) 80 – 192
• AI Global Solution Architect (Java/C/C++/Python) 180 – 360
• Deep Learning Project Manager (Java/C/C++/Python) 180 – 360
• Machine Learning Engineer (Python/Algorithms) 80 – 192
• AI Developer (Math/Vectors/Matrices/Linear Algebra) 144 – 216
• AI Global Solution Architect (Math/Vectors/Matrices/Linear Algebra) 180 – 360
• Deep Learning Project Manager (Math/Vectors/Matrices/Linear Algebra) 180 – 360

Which Degree Courses Should I Study in Malaysia to Work in Machine Learning?

Machine Learning is a subset of Artificial Intelligence (Ai) therefore, choosing a top private university in Malaysia offering this degree programme will equip you with the necessary knowledge and skills.

Other related degree programmes are Data Science and Computer Science. Students tend to get confused and look at the title of the degree programme when they should actually look at the subjects of the degree  as to what they would study.

Students who are not sure which course or university that offers them the learning opportunities in Machine Learning can contact EduSpiral Consultant Services. We do our research in order to advise students on choosing the right course and best university that will help them have a successful future career.