With so many opportunities on the horizon, a career as a Machine Learning Engineer can be both satisfying and rewarding. A good workshop, such as the one offered by KnowledgeHut, can lead you on the right path towards becoming a machine learning expert.
So what is Machine Learning? Machine learning is an application of Artificial Intelligence which trains computers and machines to predict outcomes based on examples and previous experiences, without the need of explicit programming.
Our Machine learning course will help you to solve data problems using major Machine Learning algorithms, which includes Supervised Learning, Unsupervised Learning, Reinforcement Learning and Semi-supervised Learning algorithms. It will help you to understand and learn:
The Machine Learning Course with Python by KnowledgeHut is a 48 hour, instructor-led live training sessions course, with 80 hours of MCQs and assignments. It also includes 45 hours of hands-on practical session, along with 10 live projects.
Our Machine Learning course with Python will help you get hands-on experience of the following:
Machine Learning is an application of Artificial Intelligence that allows machines and computers to learn automatically to predict outcomes from examples and experiences, without there being any need for explicit programming. As the name suggests, it gives machines and computers the ability to learn, making them similar to humans.
The concept of machine learning is quite simple. Instead of writing code, data is fed to a generic algorithm. The generic algorithm/machine will build a logic which will be based on the data provided. The provided data is termed as ‘training data’ as they are used to make decisions or predictions without any program to perform the task.
1) Stanford defines Machine Learning as:
“Machine learning is the science of getting computers to act without being explicitly programmed.”
2) Nvidia defines Machine Learning as:
“Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.”
3) McKinsey & Co. defines Machine Learning as:
“Machine learning is based on algorithms that can learn from data without relying on rules-based programming.”
4) The University of Washington defines Machine Learning as:
“Machine learning algorithms can figure out how to perform important tasks by generalizing from examples.”
5) Carnegie Mellon University defines Machine Learning as:
“The field of Machine Learning seeks to answer the question “How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?”
Today, algorithms of machine learning enable computers and machines to interact with humans, write and publish sport match reports, autonomously drive cars, and find terrorist suspects as well. Let’s peek through the origins of machine learning and its recent milestones.
Alan Turing created a ‘Turing Test’ in order to determine if a computer has real intelligence. A computer should fool a human into believing that it is also a human to pass the test.
The first computer learning program was written by Arthur Samuel. The program was a game of checkers. The more that the IBM computer played the game, the more it improved at the game, as it studied the winning strategies and incorporated those moves into programs.
The first neural network for computers was designed by Frank Rosenblatt. It stimulates the thought process of the human brain.
The ‘nearest neighbour’ was written. It allowed computers to use basic pattern recognition.
Explanation-Based Learning was introduced, where a computer analyses the training data and creates a general rule which it can follow by discarding the unimportant data.
The approach towards the work on machine learning changes from a knowledge-driven approach to machine-driven approach. Programs were now created for computers to analyze a large amount of data and obtain conclusions from the results.
IBM’s Deep Blue beat the world champion in a game of chess.
Geoffrey Hinton coined the term ‘deep learning’ that explained new algorithms that let the computer distinguish objects and texts in videos and images.
The Microsoft Kinect was released, which tracked 20 human features at a rate of 30 times per second. This allowed people to interact with computers via gestures and movements.
IBM’s Watson beat its human competitors at Jeopardy.
Google Brain was developed. It discovered and categorized objects similar to the way a cat does.
Google’s X Labs developed an algorithm that browsed YouTube videos and identified those videos that contained cats.
Facebook introduced DeepFace. It is an algorithm that recognizes and verifies individuals on photos.
Microsoft launched the Distributed Machine Learning Toolkit, which distributed machine learning problems across multiple computers.
An artificial intelligence algorithm by Google, AlphaGo, beat a professional player at a Chinese board game Go.
The algorithm of machine learning is trained using a training data set so that a model can be created. With the introduction of any new input data to the ML algorithm, a prediction is made based on the model.
The accuracy of the prediction is checked and if the accuracy is acceptable, the ML algorithm is deployed. For cases where accuracy is not acceptable, the Machine Learning algorithm is trained again with supplementary training data set.
There are various other factors and steps involved as well. This is just an example of the process.
Various industries work with Machine Learning technology and have recognized its value. It has helped and continues to help organisations to work in a more effective manner, as well as gain an advantage over their competitors.
Machine Learning technology is used in the financial industry due to two key reasons: to prevent fraud and to identify important insights in data. This helps them in deciding on investment opportunities, that is, helps the investors with the process of trading, as well as identify clients with high-risk profiles.
Machine learning has various sources of data that can be drawn used for insights. It also helps in detecting fraud and minimizes identity theft.
Machine Learning in the health care sector has introduced wearable devices and sensors that use data to assess a patient’s health in real time, which might lead to improved treatment or diagnosis.
There are numerous use cases for the oil and gas industry, and it continues to expand. A few of the use cases are: finding new energy sources, predicting refinery sensor failure, analyzing minerals in the ground, etc.
Websites use Machine Learning to recommend items that you might like to buy based on your purchase history.
What is the future of Machine Learning?
Machine learning has transformed various sectors of industries including retail, healthcare, finance, etc. and continues to do so in other fields as well. Based on the current trends in technology, the following are a few predictions that have been made related to the future of Machine Learning.
Understand the behavior of data as you build significant models
Learn about the various libraries offered by Python to manipulate, preprocess and visualize data
Learn about Supervised and Unsupervised Machine Learning.
Learn to use optimization techniques to find the minimum error in your machine learning model
Learn various machine learning algorithms like KNN, Decision Trees, SVM, Clustering in detail
Learn the technique to reduce the number of variables using Feature Selection and Feature Extraction
Understand Neural Network and apply them to classify image and perform sentiment analysis
Learn to use multiple learning algorithms to obtain better predictive performance
For Machine Learning, it is important to have sufficient knowledge of at least one coding language. Python being a minimalistic and intuitive coding language becomes a perfect choice for beginners.
Sign up for this comprehensive course and learn from industry experts who will handhold you through your learning journey, and earn an industry-recognized Machine Learning Certification from KnowledgeHut upon successful completion of the Machine Learning course.
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Hands-on: No hands-on
Hands-on: No hands-on
Hands-on: No hands-on
With attributes describing various aspects of residential homes, you are required to build a regression model to predict the property prices using optimization techniques like gradient descent.
This dataset classifies people described by a set of attributes as good or bad credit risks. Using logistic regression, build a model to predict good or bad customers to help the bank decide on granting loans to its customers.
Biodegradation is one of the major processes that determine the fate of chemicals in the environment. This Data set contains 41 attributes (molecular descriptors) to classify 1055 chemicals into 2 classes - biodegradable and non-biodegradable. Build Models to study the relationships between chemical structure and biodegradation of molecules and correctly classify if a chemical is biodegradable
In marketing, if you’re trying to talk to everybody, you’re not reaching anybody. This dataset has social posts of teen students. Based on this data, use K-Means clustering to group teen students into segments for targeted marketing campaigns.
Wine comes in various types. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).
Machine Learning, simply put, is the application of systems that imbibes Artificial Intelligence concepts and provides the systems the ability to learn, perform and improve upon set tasks on their own, without needing reprogramming or human input. The process begins with the use of data in various ways like direct experience. Then, the program looks for patterns in said data with the aim to make better decisions in the future.There are various methods to perform Machine Learning, broadly categorized into 2 categories:
Kuala Lumpur is home to several leading tech companies, such as IBM, Samsung, HP, Dell, etc. Machine learning is helping these companies enhance business scalability and boost business operations for companies across the globe.
Machine Learning deals with computers and systems. These systems take huge amounts of data, analyze and solve real-life problems through training on that data. Machine Learning provides a way for humans to solve problems without having to actually understand the problem.
Machines work faster than human brains and thus, are able to solve problems faster as well. If, for instance, there are a million approaches to a problem, a machine can systematically work out, resolve and simultaneously evaluate all the options and obtain the best outcome.
Machine Learning has so many practical uses in real life. It helps businesses save time and money. It allows people to get things done efficiently and effectively. Every industry from health care to customer service and financial institutions are enjoying the benefits of Machine Learning. It makes it an indispensable part of today’s society.
All organizations from startups to MNCs in Kuala Lumpur function on the immense amounts of data generated every day. Data is reshaping technology and business and will continue to do so in the future.
Machine Learning is a new field of work and research as of now. Tech experts have been making use of it increasingly over the years. Walmart product recommendations, Social media feeds (Facebook and Instagram), Google Maps, etc are all examples of it - enabled by powerful Machine Learning algorithms without human interference.
We partake in machine learning even when we do not know about it. Learning about it is an inevitable next step that any professional - especially someone involved in the field of Information Technology and Data Science - must take so as to not become irrelevant.
Some of the benefits of learning Machine Learning in Kuala Lumpur are:
The best way to learn Machine Learning is through a course. In Kuala Lumpur, there are several institutions that offer courses in Machine Learning including
Machine learning is a big field and is only expanding with time. However, self-learning ML is an effective way to keep oneself relevant. Keep these pointers in mind:
You can follow these steps:
If you are a beginner in Machine learning, networking with other professionals will help you get a clear understanding of Machine learning concepts and the current trends. Here are a few meetups organized in Kuala Lumpur for Machine Learning professionals:
Follow these 5 steps:
Kuala Lumpur is the hub for several tech companies that are willing to pay handsomely to skilled Machine Learning professionals. These organizations include Novartis, Echobox, Metapair, SR Technics UK, ResMed, Cognizant Technology Solutions, Celcom Axiata Berhad, Comfort Works, Mindvalley, Micro Focus, Topdanmark, etc. You will need the following 5 technical skills:
Follow these 6 steps to execute an ML project with Python:
Algorithms are an integral part of Machine Learning. Here’s how you can effectively learn them:
The K Nearest Neighbours algorithm is a simplistic Machine Learning algorithm. Given a multiclass dataset to be worked on with the aim to predict the class of the data point, use the K Nearest Neighbour algorithm to reach a solution.
In spite of its simplicity, the K Nearest Neighbour algorithm has proven to be a successful and useful solution to a number of regression problems, not to mention, classification problems.
This depends on what you intend to do with Machine Learning as a field:
Machine Learning Algorithms can be broadly classified into 3 categories -
The simplest of machine learning algorithms are the ones that solve the easiest ML problems (simple recognition). We can base it on the following criteria:
K-nearest neighbor algorithm is the simplest ML algorithm. Here is why:
ML has loads of tools, algorithms, and models that you can choose from. But one has to keep in mind certain things while selecting the algorithm for your project.
Follow these 5 steps:
ML is a growing field with unlimited applications and uses. There are various training centres in Kuala Lumpur offering ML courses.
You can try the following
The following are some essential topics of Machine Learning that every learner should be acquainted with:
The average salary of Machine Learning Engineer in Kuala Lumpur is RM 31,243/yr.
The average salary of a machine learning engineer in Kuala Lumpur compared with is Portland RM 31,243/yr whereas, in Singapore, it’s $73,193/yr.
According to Eset, Malaysia is the most cyber-savvy nation in Asia and its capital Kuala Lumpur is renowned for various tech organizations, including Ably Resources, Tele-Temps Sdn Bhd, iPrice gathering and others. These organizations have realised the significance of ML and thus the demand for ML engineers is growing at a faster pace.
Following are the advantages of arriving into the desirable work' of the engineering alumni -
A career in machine learning is critical and involves skills in Python, SQL programming, and others important for machine engineer profile. As more and more companies are now investing in machine learning and looking for ML experts for cutting-edge research. And according to a study revealed by Gartner, AI is projected to create 2.3 million jobs by 2020. So, it is safe to say that machine learning will remain an in-demand skill.
Some of the companies that are hiring Machine Learning Engineer in Kuala Lumpur-
International Conference on Data Mining, Statistics and Machine Learning Techniques ICDMSMLT
December 05-06, 2019
2nd International e-Symposium on Information Science and Technology 2019
August 30, 2019
The 13th Multi-disciplinary International Conference On Artificial Intelligence
November 17-19, 2019
December 8 - 10, 2019
Annual Summit on Artificial Intelligence and Machine Learning
November 25-26, 2019
International Conference on Digital Circuits, Systems, and Signal Processing
December 7 - 8, 2020
International Conference On Computer Science, Machine Learning And Artificial Intelligence (ICCSMLAI - 2019)
19th Jul, 2019
Flamingo by the lake, Kuala Lumpur, No.5, Tasik Ampang, Jalan Hulu Kelang, 68000 Ampang, Selangor Darul Ehsan
RiTA 2018 The 6th International Conference on Robot Intelligence Technology and Applications
16th to 18th December 2018
Kuala Lumpur, Malaysia
IMAN 2018: 6th International Conference on Islamic Applications in Computer Science and Technologies
20th to 23rd December 2018
Kuala Lumpur, Malaysia
MySEC 2018: Malaysian Software Engineering Conference
7th to 8th August 2018
Kuching, Sarawak, Malaysia
ICDMSMLT 2018: International Conference on Data Mining, Statistics and Machine Learning Techniques
5th to 6th December 2018
Kuala Lumpur, Malaysia
2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET 2018)
8th to 9th November 2018
Kota Kinabalu Kota Kinabalu, SBH, MY
Programmatic Malaysia 2018 Conference
26th to 28th September 2018
Petaling Jaya, Selangor. New World Petaling Jaya Hotel
Next Big Tech Asia 2018
2nd to 8th October 2018
Kuala Lumpur, Malaysia
ROBIO 2018 - IEEE International Conference on Robotics and Biomimetics
12th to 15th December 2018
Kuala Lumpur Convention Centre, Kuala Lumpur, Malaysia
GCMT'18 - 2nd Global Conference on Computing and Media Technology
14th to 15th November 2018
Kuala Lumpur, Malaysia
The responsibilities of a Machine Learning Engineer include:
Kuala Lumpur is not only home to several leading MNCs but also has a very good start-up climate. There are more than 800 Tech startups in Kuala Lumpur and new startups are popping up almost every month. All these companies and startups are looking for ML experts to help them extract meaningful information from a huge set of raw data.
Some of the ML job roles in demand are:
If you want to get hired fast in Kuala Lumpur, referrals work best. You can create your network with other Machine Learning Engineers through the following:
A Machine Learning Engineer in Kuala Lumpur earns around RM144,017 in a year.
Here's how you can do so:
Some very useful libraries are listed below:
Below are the steps required to execute a successful Machine Learning project with Python-
The following are some tips to help you learn basic Python skills:
Here, we have compiled a list of Python libraries that are best for machine learning:
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Machine learning came into its own in the late 1990s, when data scientists hit upon the concept of training computers to think. Machine learning gives computers the capability to automatically learn from data without being explicitly programmed, and the capability of completing tasks on their own. This means in other words that these programs change their behaviour by learning from data. Machine learning enthusiasts are today among the most sought after professionals. Learn to build incredibly smart solutions that positively impact people’s lives, and make businesses more efficient! With Payscale putting average salaries of Machine Learning engineers at $115,034, this is definitely the space you want to be in!
By the end of this course, you would have gained knowledge on the use of machine learning techniques using Python and be able to build applications models. This will help you land lucrative jobs as a Data Scientist.
There are no restrictions but participants would benefit if they have elementary programming knowledge and familiarity with statistics.
Yes, KnowledgeHut offers this training online.
On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.
Your instructors are Machine Learning experts who have years of industry experience.
Any registration cancelled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of written request for refund. Kindly go through our Refund Policy for more details.
In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.
Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor.
Machine Learning Course with Python in Kuala Lumpur
The contemporary skyline of the Kuala Lumpur is dotted with the world famous 451m tall Petronas Towers. It is the capital of Malaysia and is packed with lush parks, historical monuments, steel dressed skyscrapers, trendy nightspots, massive shopping malls, and vibrant street markets. Kuala Lumpur, in terms of economy, is a fast-growing metropolitan region in South East Asia. As the economic pivot of the country, it is no surprise that a large number of top-notch companies exist here. They create employment and want to hire skilled employees that can contribute effectively to their overall growth. Organizations are desirous of hiring individuals enrolled for attaining a certification in data analysis using Python course in Kuala Lumpur. For professionals, this is an opportunity and to make sure they can avail it, they must consider joining an e-learning program, conducted by KnowledgeHut, where the online classes will give them an in-depth understanding of the principles and concepts of data analytics and machine learning using Python. Python is used in the deployment of projects of any scale and is the preferred choice for the serious programmer. Python's inherent feature allows easy programming and companies use it to their advantage to build applications. Naturally, they require professionals who are fluent with the medium and hence it is advisable that they consider the machine learning using Python course in Kuala Lumpur. Completing this course will be highly advantageous as it will give professionals a valid and widely accepted credential. According to the Globalization and World Cities Study Group and Network, Kuala Lumpur is rated as an alpha city, and is the only global city in Malaysia. This makes it a hotbed for commercial activity and job seekers flock here for employment. The need for programmers with machine learning and Python capabilities has led to the availability of several online training programs that will arm individuals with the requisite skill sets to become proficient developers.
New Alternative - Python
Python is a high-level programming language used by developers worldwide to develop applications for any scale and size. Serious programmers prefer the use of this language in both machine learning and scientific computing. Along with this space, it is also a chosen medium for science, engineering and business applications. Python is easy to deploy and the design features syntax clarity along with simplified readability and comprehension.
Keeping Ahead of the Curve
A Machine Learning with Python training in Kuala Lumpur is a great investment for developers as it will reap many professional rewards and help them stay ahead of the curve. KnowledgeHut through its online classes facilitates seamless learning under the tutelage of an eminent panel of teachers. The online course will aid individuals in developing core-competence and become able developers. Knowledge Hut Empowers You The KnowledgeHut program on machine learning training using Python is available at an affordable price point in Kuala Lumpur. The online modules are created to enable programmers to appear for an exam with the same ease with which they develop applications.