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
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 uses the concept of AI to help the systems in learning, performing, and improving the given tasks. The aim of the field is to remove any human intervention. These systems are able to automatically analyze the data and learn. The training and learning of the system depends on the datasets provided to the system. All the major ML algorithms can be divided into two categories:
This category of ML algorithms use labeled data to train the model. They take the information from the past data and then apply this information to predict outcomes in the future. During this whole process, the algorithms help in inferring a function and predicting the outcome.
In the unsupervised machine learning algorithms, unstructured data is fed into the system for training the model. The systems are designed in such a way that they can process the unlabeled data. This model will only be able to draw the inference from the past data and describe hidden patterns in the data.
Machine Learning has become an integral part of our society. It helps in developing and training the systems that are now used by several organizations for making crucial marketing decisions. Machines are faster than humans. They can solve a problem easily and efficiently. The field of machine learning has been useful in several applications. This is the reason why most of the industries have begun incorporating machine learning like banking, health care, nursing, etc.
According to a study revealed by Toronto Global, Toronto generated more tech jobs in the previous year than New York City or the San Francisco Bay Area combined. News of Samsung Research launching its AI Centre in downtown Toronto and Etsy announcing plans to set up a Machine Learning Centre of Excellence in Toronto last year indicate consistent investment in the AI ecosystem.
Machine Learning is a huge field in which the best is yet to be explored. As more and more data gets generated every single day, importance of ML is also increasing. Organizations have now started to understand the benefits of data-driven decision making and are now actively hiring machine learning engineers. Here are a few benefits of Machine Learning that you should know -
Here are a few steps that will help you learn machine learning skills:
Here is a plan to help you get started in Machine Learning as an absolute beginner-
Leading tech companies like Uber, Facebook, Google, Facebook have set up core AI research labs which collaborate with various universities across Canada. To become a successful Machine Learning Engineer, you need to have the following skill sets:
To execute a successful Machine Learning project using Python, you need to follow the below-mentioned steps:
Algorithms are an integral part in the field of Machine Learning. As a machine learning engineer, you must have an in-depth knowledge and hands-on experience of algorithms. Here is how you can learn the top essential ML algorithms:
The most important machine learning algorithm for beginners is K-Nearest Neighbors algorithm. This simple, uncomplicated algorithm is used for predicting the class of a data point. Here is how it works:
Whether you need to know algorithms to learn machine learning depends on why you are using it:
Here are the top different types of algorithms categorized on the basis of the learning method used-
The simplest algorithm is the k-nearest neighbor algorithm. Here is why it is so simple but still used extensively for solving basic, real-life problems:
Here are a few tips that will help you choose the right machine learning algorithm to use for a specific problem statement:
Follow the below-mentioned steps for designing and implementing the machine learning algorithm using Python:
Here are the most essential topics of Machine Learning that you need to master:
The median salary of a Machine Learning Engineer in Toronto is CA$106K/yr. The range differs from CA$51,800 to as high as CA$1,35,000.
The average salary of a machine learning engineer in Toronto compared with Vancouver CA is $106K/yr. whereas, in Vancouver, it’s CA$85,000/yr.
Canada is one of the most developed countries in the world. It attracts companies and professionals from all over the world due to the fact that there are various opportunities for them to sustain and grow. Toronto is one of Canada’s largest and most developed cities. Considering the interest that Canada shows and offers opportunities in the technology space, machine learning has a high demand in this city because the need of a skilled engineer in ML is constantly increasing, thus increasing the demand.
Toronto is home to numerous new businesses and organizations. An ongoing report has uncovered that despite the fact that Data science is hailed as the ‘Sexiest job of the 21st century’, the potential of a career as a machine learning engineer is enormous as it is developing with a rate of more than 340%.
Considered as the dream job for maximum engineering graduates, here are the reasons why it is a dream job apart from the fancy salary it offers -
Although there are quite many companies offering jobs to Machine Learning Engineers in Toronto, following are the prominent companies -
Machine Learning And Market For Intelligence
|24 October 2019|
Rotman School of Management, University of Toronto,105 St. George Street
12 - 13 June, 2019
North Building, 255, Front Street, West Toronto, Ontario
|3.||Toronto Machine Learning Society Annual Conference And Expo||21 November, 2019|
The Carlu, 444 Yonge Street, Toronto, Canada
|1.||Toronto Machine Learning Summit||2-3 November, 2017||Daniels Spectrum, 585 Dundas Street East, Toronto, Canada|
1.Toronto Machine Learning Summit, Toronto
The responsibilities of a Machine Learning Engineer in Toronto include the following:
Known as Canada’s start-up capital, Toronto is home to 2,100 and 4,100 active tech startups. The startup ecosystem of Toronto is seeing a rise in innovative solutions around areas such as AR, VR, AI, ML, etc. More and more companies are shifting towards data-driven decision making. For this, they need machine learning engineers.
The top professional groups for a Machine Learning Engineer in Toronto are:
Here are some of the job roles in the field of Machine Learning that are in demand in 2019:
To get started with mastering Machine Learning using Python, you need to follow the below-mentioned steps:
Here are the top essential Python libraries that are used for implementing Machine Learning using Python:
Here are the 5 best tips for learning Python programming as a beginner:
If you want to learn to use Python for machine learning, you need to familiarize yourself with the following Python libraries:
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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 with Python Training in Toronto
A city with a glittering skyline and blessed with green spaces and recreational areas, Toronto is a bustling confluence of humanity and a sprawling urban center of Canada. One of North America's most important cultural and economic centers, some of the many famous attractions of Toronto are the Toronto Zoo, the CN Tower and the City Hall. The town's financial district is home to some of the biggest companies in the region. The Toronto Stock Exchange is the seventh largest in the world by market capitalization and the city's location in an important industrial manufacturing belt makes it an important wholesale and distribution point. Canada's most populous city has one of the most innovative and talent-filled business cultures and with this backdrop, KnowledgeHut presents the Data Analysis using Python course in Toronto.
About the Course
Python is a favored language amongst serious programmers because of its many helpful features and easy learning curve. The many qualities of the language are an emphasis on syntax clarity and easy readability and comprehension. KnowledgeHut's Machine Learning using Python course in Toronto is an intensive online training program that will help imbibe the fundamentals of this framework in you. This course will help you learn a language that is used in many sciences, business and engineering applications including web apps and games. A 5-day affair, the online classes will help you pass any exam based on data analysis using Python. On completion of this Machine Learning using Python course in Toronto, you will receive a course completion certification. The Machine Learning using Python course in Toronto will include many exercises and lessons that will teach you to manipulate, manage, study and organize data using powerful tools and libraries. The training will begin with an introduction to data analysis using Python and its application scenarios. Amongst the many design methodologies in data analysis that will be taught at this course in Toronto, parallel processing and data classification are just two. There will be a section on Matplotlib and our course instructors will also coach you on important Python packages like Pandas, Scipy, scikit-learn and statsmodels. Another important section of this course is how to integrate the Python language into a Hadoop landscape. As part of this online program, you will also receive a downloadable e-book that will offer you extra guidance.
The KnowledgeHut Advantage
With a presence in cities across 70 countries, KnowledgeHut has helped thousands of participants get a career push with its wide range of impactful professional e-learning programs. Our Machine Learning training using Python is delivered using the latest cutting-edge methods that combine easy access and a live classroom environment. The course is available at a great price. This Data Analysis training using Python in Toronto is perfect for programmers, webmasters, scientists, analysts, professional software developers, entrepreneurs and others looking to understand Python and its data analysis applications.