Machine Learning with Python Training in Toronto, Canada

Know how Statistical Modeling relates to Machine Learning

  • 48 hours of Instructor led Training
  • Comprehensive Hands-on with Python
  • Covers Unsupervised learning algorithms such as K-means clustering techniques
  • Get introduced to deep learning techniques
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Description

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 basic concepts of the Python Programming language
  • About Python libraries (Scipy, Scikit-Learn, TensorFlow, Numpy, Pandas,)
  • The data structure of Python
  • Machine Learning Techniques
  • Basic Descriptive And Inferential Statistics before advancing to serious Machine learning development.
  • Different stages of Data Exploration/Cleaning/Preparation in Python

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.

Why Learn Machine Learning from Knowledgehut?

Our Machine Learning course with Python will help you get hands-on experience of the following:

  1. Learn to implement statistical operations in Excel.
  2. Get a taste of how to start work with data in Python.
  3. Understand various optimization techniques like Batch Gradient Descent, Stochastic Gradient Descent, ADAM, RMSProp.
  4. Learn Linear and Logistic Regression with Stochastic Gradient Descent through real-life case studies.
  5. Learn about unsupervised learning technique - K-Means Clustering and Hierarchical Clustering. Real Life Case Study on K-means Clustering.
  6. Learn about Decision Trees for regression & classification problems through a real-life case study.
  7. Get knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index, CHAID.
  8. Learn the implementation of Association Rules. You will learn to use the Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift. Further, you will learn what are UBCF and IBCF and how they are used in Recommender Engines.

What is Machine Learning?

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.

Practical Definition from Credible Sources:

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?”

Origin of Machine Learning through the years

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.

1950:
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.

1952:
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.

1957:
The first neural network for computers was designed by Frank Rosenblatt. It stimulates the thought process of the human brain.

1967:
The ‘nearest neighbour’ was written. It allowed computers to use basic pattern recognition.

1981:
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.

1990:
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.

1997:
IBM’s Deep Blue beat the world champion in a game of chess.

2006:
Geoffrey Hinton coined the term ‘deep learning’ that explained new algorithms that let the computer distinguish objects and texts in videos and images.

2010:
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.

2011:
IBM’s Watson beat its human competitors at Jeopardy.

2011:
Google Brain was developed. It discovered and categorized objects similar to the way a cat does.

2012:
Google’s X Labs developed an algorithm that browsed YouTube videos and identified those videos that contained cats.

2014:
Facebook introduced DeepFace. It is an algorithm that recognizes and verifies individuals on photos.

2015:
Microsoft launched the Distributed Machine Learning Toolkit, which distributed machine learning problems across multiple computers.

2016:
An artificial intelligence algorithm by Google, AlphaGo, beat a professional player at a Chinese board game Go.

How does Machine Learning work?

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.

Advantages of Machine Learning

  1. It is used in multifold applications such as financial and banking sectors, healthcare, publishing, retail, social media, etc.
  2. Machine learning can handle multi-variety and multi-dimensional data in an uncertain or dynamic environment.
  3. Machine learning algorithms are used by Facebook and Google to push advertisements which are based on the past search behaviour of a user.
  4. In large and complex process environments, Machine Learning has made tools available which provide continuous improvement in quality.
  5. Machine learning has reduced the time cycle and has led to the efficient utilization of resources.
  6. Source programs like Rapidminer have helped increase the usability of algorithms for numerous applications.    

Industries using Machine Learning

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.

  1. Financial services:

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.

  1. Government:

Machine learning has various sources of data that can be drawn used for insights. It also helps in detecting fraud and minimizes identity theft.

  1. Health Care:

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.

  1. Oil and Gas:

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.

  1. Retail:

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.

  1. Personalization algorithms of Machine Learning offer recommendations to users and attract them to complete certain actions. In future, the personalization algorithms will become more fine-tuned, which will result in more beneficial and successful experiences.
  2. With the increase in demand and usage for Machine Learning, the usage of Robots will increase as well.
  3. Improvements in unsupervised machine learning algorithms are likely to be observed in the coming years. These advancements will help you develop better algorithms, which will result in faster and more accurate machine learning predictions.
  4. Quantum machine learning algorithms hold the potential to transform the field of machine learning. If quantum computers integrate to Machine Learning, it will lead to faster processing of data. This will accelerate the ability to draw insights and synthesize information.

What You Will Learn

1. Statistical Learning

Understand the behavior of data as you build significant models

2. Python for Machine Learning

Learn about the various libraries offered by Python to manipulate, preprocess and visualize data

3. Fundamentals of Machine Learning

Learn about Supervised and Unsupervised Machine Learning.

4. Optimization Techniques

Learn to use optimization techniques to find the minimum error in your machine learning model

5. Machine Learning Algorithms

Learn various machine learning algorithms like KNN, Decision Trees, SVM, Clustering in detail

6. Dimensionality Reduction

Learn the technique to reduce the number of variables using Feature Selection and Feature Extraction

7. Neural Networks

Understand Neural Network and apply them to classify image and perform sentiment analysis

8. Ensemble Learning

Learn to use multiple learning algorithms to obtain better predictive performance

PREREQUISITES

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.

3 Months FREE Access to all our E-learning courses when you buy any course with us

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Who Should Attend?

  • If you are interested in the field of machine learning and want to learn essential machine learning algorithms and implement them in real life business problem
  • If you're a Software or Data Engineer interested in learning the fundamentals of quantitative analysis and machine learning

Knowledgehut Experience

Instructor-led Live Classroom

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.

Learn through Doing

Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.

Mentored by Industry Leaders

Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

Get reviews and feedback on your final projects from professional developers.

Certification Process

Learn Machine Learning in Toronto
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Machine Learning Engineer Salary in Toronto
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Machine Learning with Python Toronto

Curriculum

Learning Objectives:

In this module, you will visit the basics of statistics like mean (expected value), median and mode. You will understand the distribution of data in terms of variance, standard deviation and interquartile range; and explore data and measures and simple graphics analyses.Through daily life examples, you will understand the basics of probability. Going further, you will learn about marginal probability and its importance with respect to data science. You will also get a grasp on Baye's theorem and conditional probability and learn about alternate and null hypotheses.

Topics:
  • Statistical analysis concepts
  • Descriptive statistics
  • Introduction to probability and Bayes theorem
  • Probability distributions
  • Hypothesis testing & scores
Hands-on :
Learn to implement statistical operations in Excel.
Learning Objectives:

In this module, you will get a taste of how to start work with data in Python. You will learn how to define variables, sets and conditional statements, the purpose of having functions and how to operate on files to read and write data in Python. Understand how to use Pandas, a must have package for anyone attempting data analysis in Python. Towards the end of the module, you will learn to visualization data using Python libraries like matplotlib, seaborn and ggplot.

Topics:
  • Python Overview
  • Pandas for pre-Processing and Exploratory Data Analysis
  • Numpy for Statistical Analysis
  • Matplotlib & Seaborn for Data Visualization
  • Scikit Learn

Hands-on: No hands-on

Learning Objectives :

This module will take you through real-life examples of Machine Learning and how it affects society in multiple ways. You can explore many algorithms and models like Classification, Regression, and Clustering. You will also learn about Supervised vs Unsupervised Learning, and look into how Statistical Modeling relates to Machine Learning.

Topics:
  • Machine Learning Modelling Flow
  • How to treat Data in ML
  • Types of Machine Learning
  • Performance Measures
  • Bias-Variance Trade-Off
  • Overfitting & Underfitting

Hands-on: No hands-on

Learning Objectives:

This module gives you an understanding of various optimization techniques like Batch Gradient Descent, Stochastic Gradient Descent, ADAM, RMSProp.

Topics:
  • Maxima and Minima
  • Cost Function
  • Learning Rate
  • Optimization Techniques

Hands-on: No hands-on

Learning Objectives:

In this module you will learn Linear and Logistic Regression with Stochastic Gradient Descent through real-life case studies. It covers hyper-parameters tuning like learning rate, epochs, momentum and class-balance.You will be able to grasp the concepts of Linear and Logistic Regression with real-life case studies. Through a case study on KNN Classification, you will learn how KNN can be used for a classification problem. You will further explore Naive Bayesian Classifiers through another case study, and also understand how Support Vector Machines can be used for a classification problem. The module also covers hyper-parameter tuning like regularization and a case study on SVM.

Topics:
  • Linear Regression
  • Case Study
  • Logistic Regression
  • Case Study
  • KNN Classification
  • Case Study
  • Naive Bayesian classifiers
  • Case Study
  • SVM - Support Vector Machines
  • Case Study
Hands-on:
  • With attributes describing various aspect 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.
  • Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
  • We receive 100s of emails & text messages everyday. Many of them are spams. We would like to classify our spam messages and send them to the spam folder. We would also not like to incorrectly classify our good messages as spam. So correctly classifying a message into spam and ham is of utmost importance. We will use Naive Bayesian technique for text classifications to predict which incoming messages are spam or ham.
  • Biodegradation is one of the major processes that determine the fate of chemicals in the environment. This Data set containing 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 and non-biodegradable.
Learning Objectives:

Learn about unsupervised learning technique - K-Means Clustering and Hierarchical Clustering. Real Life Case Study on K-means Clustering

Topics:
  • Clustering approaches
  • K Means clustering
  • Hierarchical clustering
  • Case Study
Hands-on :
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. 
Learning Objectives:

This module will teach you about Decision Trees for regression & classification problems through a real-life case study. You will get  knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index,CHAID.The module covers basic ensemble techniques like averaging, weighted averaging & max-voting. You will learn about bootstrap sampling and its advantages followed by bagging and how to boost model performance with Boosting.
Going further, you will learn Random Forest with a real-life case study and learn how it helps avoid overfitting compared to decision trees.You will gain a deep understanding of the Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. It covers comprehensive techniques to find the optimum number of components/factors using scree plot, one-eigenvalue criterion. Finally, you will examine a case study on PCA/Factor Analysis.

Topics:
  • Decision Trees
  • Case Study
  • Introduction to Ensemble Learning
  • Different Ensemble Learning Techniques
  • Bagging
  • Boosting
  • Random Forests
  • Case Study
  • PCA (Principal Component Analysis) and Its Applications
  • Case Study
Hands-on:
  • Wine comes in various style. With the ingredient composition known, we can build a model to predict the the Wine Quality using Decision Tree (Regression Trees).
  • In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. In this case study, use AdaBoost, GBM & Random Forest on Lending Data to predict loan status. Ensemble the output and see your result perform than a single model.
  • Reduce Data Dimensionality for a House Attribute Dataset for more insights &  better modeling.
Learning Objectives: 

This module helps you to understand hands-on implementation of Association Rules. You will learn to use the Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift. Further, you will learn what are UBCF and IBCF and how they are used in Recommender Engines. The courseware covers concepts like cold-start problems.You will examine a real life case study on building a Recommendation Engine.

Topics:
  • Introduction to Recommendation Systems
  • Types of Recommendation Techniques
  • Collaborative Filtering
  • Content based Filtering
  • Hybrid RS
  • Performance measurement
  • Case Study
Hands-on:
You do not need a market research team to know what your customers are willing to buy.  Netflix is an example of this, having successfully used recommender system to recommend movies to its viewers. Netflix has estimated, that its recommendation engine is worth a yearly $1 billion.
An increasing number of online companies are using recommendation systems to increase user interaction and benefit from the same. Build Recommender System for a Retail Chain to recommend the right products to its users 

Projects

Predict Property Pricing using Linear Regression

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.

Classify good and bad customers for banks to decide on granting loans.

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.

Classify chemicals into 2 classes, biodegradable and non-biodegradable using SVM.

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

Read More

Cluster teen student into groups for targeted marketing campaigns using Kmeans Clustering.

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.

Read More

Predict quality of Wine

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).

Note: These were the projects undertaken by students from previous batches.  

Learn Machine Learning

Learn Machine Learning in Toronto, Canada

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:

  • Supervised Machine Learning Algorithms

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.

  • Unsupervised Machine Learning Algorithms

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 - 

  1. It reels in better job opportunities: Canadian Institute for Advanced Research (CIFAR) recently named 29 top AI researchers to the AI Chairs program as a part of its $125 Million Pan-Canadian Artificial Intelligence Strategy. So, it is clear that this will reel in better job opportunities for machine learning engineers.
  2. Machine Learning engineers earn a pretty penny: The report published by SimplyHired.com stated that the average annual salary of a machine learning engineer is $142,000. In Toronto, this average salary is $220,000. With experience, this number is only going to go up.
  3. Demand for Machine Learning skills is only increasing: There are not enough qualified machine learning engineers available to satisfy the demand in Toronto. So, if you are a machine learning engineer in Toronto, you will be able to grab a job that pays handsomely.

Here are a few steps that will help you learn machine learning skills:

  1. Structural Plan: Create a detailed plan that describes what topics you will be studying and from where.
  2. Prerequisite: Choose a programming language that you already know. Also, brush up your statistical and mathematics skills.
  3. Learning: Start learning according to the plan you created. There are tons of resources available online for free that will help you understand the concepts of machine learning.
  4. Implementation: Practice your skills as you learn. Create projects using the algorithms that you’re learning. You can search the web for datasets. You can also try competitions like kaggle that will help you practice your skills.

Here is a plan to help you get started in Machine Learning as an absolute beginner-

  1. Adjust your mindset: Machine Learning is a complex field and you can easily get frustrated soon. What you need to do is keep reminding yourself about why you started. Remember that the more you practice, the better your implementation skills will be.
  2. Pick a process that suits you best: Select a structured and systematic process that suits your needs in finding a solution to a given problem.
  3. Pick a tool: Next step is to select the tool that you will use to implement your ML skills. Here are a few examples:
    1. Beginners should use the tool, Weka Workbench
    2. Intermediate level learners should go for the Python Ecosystem
    3. Advanced level learners can use the R Platform
  4. Practice on Datasets: Practice as much as you can. There are several datasets that you can easily find online that will help you practice your data collecting and manipulating skills. You should try small, installed and real-world datasets.
  5. Build your own portfolio: You need to build your portfolio by creating ML projects.You can also join bootcamps in Toronto to get an experience of Real-World Machine Learning Projects.

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:

  1. Programming languages: Programming language is required for processing and analyzing the data. The most commonly used programming languages are Python, R, Scala, etc. Also, you need to familiarize yourself with the concepts of programming like data formats.
  2. Database skills: Database skills are required for accessing and communicating with the data. You need to have a complete knowledge of databases and SQL. This will help you in reading the data and converting it into a format that is compatible with the ML framework that you are using.
  3. Machine Learning visualization tools: You need to have an understanding of data visualization tools. Not only will it make the data more readable and understandable but it will also help in discovering patterns in the data.
  4. Knowledge of Machine learning frameworks: You need to have a thorough knowledge of ML frameworks like Apache Spark, TensorFlow, Scala, NLP, R, etc. These are used for implementing mathematical and statistical algorithms.
  5. Mathematical skills: You will need your mathematical skills for creating your ML models and processing and analyzing the data. For understanding the concepts of ML, you need to have an understanding of mathematics concepts like Bayesian Modeling, Calculus, Hypothesis testing, linear algebra, statistics, probability, optimization, graph theory, etc.

To execute a successful Machine Learning project using Python, you need to follow the below-mentioned steps:

  • Gathering data: The first step to gather the right data required for your ML project. It is important that the data is of good quality as it will affect the performance of your model.
  • Cleaning and preparing data: Next step is to clean the data. The data that you would have collected will be in a raw, unstructured form that couldn’t be injected into the model. You need to clean this data by taking out the irrelevant data. Next, you need to use feature engineering for converting the data into a format acceptable by the model. This data is then categorized into training and testing data.
  • Visualize the data: The next step is to visualize the data using graphs and charts. Visualization helps in making the data easy to comprehend and allows the machine learning engineer to see the correlation between the datasets. There are several tools available that can be used for data visualization.
  • Choosing the correct model: Next, you need to select the best algorithm and model for your problem. It is essential that you choose the right model as it will determine the algorithm’s performance.
  • Train and test: The next step is to train the prepared data by injecting into the model. After this, you will use the testing data to check the accuracy of the model.
  • Adjust parameters: Once you have tested your data, you can tune the parameters to get a more accurate model.

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:

  1. List the various Machine Learning algorithms: The first step is enlisting all the ML algorithms that you need to learn. Categorize these according to their types and classes. It will help you in getting familiar with the concepts of ML.
  2. Apply the Machine Learning algorithms that you listed down: Start practicing. Apply these algorithms in real-world datasets. This will help you easily grasp the concepts of ML. Use different datasets and problems to implement on your datasets.
  3. Describe these Machine Learning algorithms: In this step, you need to write down a description of the algorithms that you have learnt and implemented. Explore them and create a mini encyclopedia of ML algorithms.
  4. Implement Machine Learning Algorithms: Implement the algorithms in real-world projects. This will help you get an understanding of how machine learning concepts are implemented using the algorithms. 
  5. Experiment on Machine Learning Algorithms: The last step is to experiment with the algorithms. Once you are familiar with the parameters used in the algorithm, you will be able to customize it to suit your needs.

Machine Learning 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:

  • The first step is to define ‘k’ which is a predefined number that tells the number of training samples close to the new data point.
  • A label is assigned to the new data point. 
  • The K-nearest neighbor classifiers will have to determine a fixed constant for the count of neighbors.
  • It uses the concept of radius based classification where the samples are identified and classified under a fixed radius using the density of the neighboring data points.
  • After the vote is conducted among the neighbors of the unknown sample, classification is done.

Whether you need to know algorithms to learn machine learning depends on why you are using it: 

  • If you just want to use the ML algorithm and don’t need to customize it, you don’t need to learn the algorithms.
  • If you are working on a problem that requires customization of algorithms, you will be required to study them. You will have to familiarize yourself with concepts like accuracy of an algorithm, its complexity, what is the cost involved, how much time it takes, etc.

Here are the top different types of algorithms categorized on the basis of the learning method used-

  • Supervised Learning: In this, classified past data is used for mapping the input variables to the output variables. Here are the algorithms that follow supervised learning:
    • Linear Regression
    • Logistic Regression
    • Classification and Regression Trees (CART)
    • Naïve Bayes
    • K-Nearest Neighbors
  • Unsupervised Learning: In this, the output variables are not provided. It involves analyzing the dataset for revealing associations and clusters. Here are the examples of algorithms that use unsupervised learning:
    • Apriori
    • K-Means
    • Principal Component Analysis (PCA)
  • Ensemble Learning: In this type of learning, each learner’s results are used and combined to get the outcome’s representation. The following algorithms use Ensemble learning:
    • Bagging
    • Boosting

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:

  • It is the simplest supervised learning algorithm
  • It can be used for regression problems as well
  • It uses the similarity measure to perform classification
  • Labeled data is used for training the model
  • It can be used in the following real-life problems:
    • Detecting credit card usage pattern
    • Recognizing the number on the vehicle plate
    • Searching documents 

Here are a few tips that will help you choose the right machine learning algorithm to use for a specific problem statement:

  • Understanding your data: The first step is to understand the data on which you will be implementing your machine learning algorithm. Follow the below-mentioned steps:
    • Plot the data using graphs and charts
    • Find the relationship among the data
    • Remove the irrelevant data and gather the missing one
    • Perform feature engineering for converting data into a form acceptable by the ML model
  • Get an intuition about the task: Understand what your task is and how machine learning can be used to perform it. After this, you will be able to decide the learning method. The types of learning methods are:
    • Supervised learning
    • Semi-supervised learning
    • Unsupervised learning
    • Reinforcement learning
  • Understand your constraints: While selecting the machine learning algorithm, you need to take care of the following constraints:
    • Data storage capacity
    • Hardware constraints
    • Time constraints
  • Find available algorithms: If the algorithm fulfills all your requirements and follows the constraints, you can use it for your problem.

Follow the below-mentioned steps for designing and implementing the machine learning algorithm using Python:

  • Select the algorithm that you wish to implement: First step is to select the algorithm you will be implementing. You need to be precise in selecting the algorithm. You also have to determine the type of algorithm, the classes which you will be using, any special implementation of the algorithm and its description.
  • Select the problem you wish to work upon: Next, you need to figure out the problem dataset that you will be using for testing. The implementation and efficiency of the algorithm will depend on the problem.
  • Research the algorithm that you wish to implement: Research the selected algorithm. Go through the different available implementation methods, outlooks, and descriptions of the algorithm. This will help you in overcoming any roadblocks you might face while implementing the algorithm.
  • Undertake unit testing: The last step is developing and running a unit test for all the functions used in the algorithm. 

Here are the most essential topics of Machine Learning that you need to master:

  • Decision Trees: This supervised learning algorithm uses classification technique to select the features and conditions required for splitting. Here are the advantages of using this algorithm:
    • Understood, interpreted and visualized easily
    • Minimal to no efforts are required for preparing data
    • Can handle feature selection as well as variable screening
    • Can be used for solving problems with multiple outputs
    • Handles numerical and categorical data
    • Isn’t affected by the non-linear relationship of the parameters.
  • Support Vector Machines: This is a classification methodology that is used for providing high accuracy in classification problems. It can also be used for regression problem. The advantages of this include:
    • Offers guaranteed optimal solution
    • Makes feature mapping easy by using ‘kernel trick’
    • Used in linearly and nonlinearly separable data
  • Naive Bayes: Based on the Bayes theorem, this algorithm assumes that the different predictors are independent of each other. The benefits of using this algorithm include:
    • Easy to use
    • Highly scalable
    • Converges quickly
    • Less amount of training data is required
  • Random Forest algorithm: In this algorithm, a forest of decision trees is created and input is randomized. This is done so that no pattern is identified on the basis of the input’s order. Trained through bagging method, this algorithm offers the following advantages:
    • Easy to use
    • Can be used for classification as well as regression problems
    • Good prediction results are produced

Machine Learning Engineer Salary in Toronto, Canada

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 - 

  • Incentives and reward 
  • Acknowledgement
  • Promising career growth
  • Potential outcomes 

Although there are quite many companies offering jobs to Machine Learning Engineers in Toronto, following are the prominent companies - 

  • RBC
  • Ubisoft
  • Utilant LLC
  • Mojio
  • Microsoft
  • North

Machine Learning Conference in Toronto, Canada

S.NoConference nameDateVenue
1.

Machine Learning And Market For Intelligence

24 October 2019

Rotman School of Management, University of Toronto,105 St. George Street

2.AI Toronto

12 - 13 June, 2019

North Building, 255, Front Street, West Toronto, Ontario

3.Toronto Machine Learning Society Annual Conference And Expo21 November, 2019

The Carlu, 444 Yonge Street, Toronto, Canada

  1.  Machine Learning and market for Intelligence, Toronto
    1. About the Conference: The Creative Destruction Lab leverages the Rotman School's leading faculty and industry network to exchange ideas on machine learning and discussion on the business opportunities and economic implications arising from the recent advances in Artificial Intelligence. 
    2. Event date: 24 October, 2019
    3. Venue: Rotman School of Management, University of Toronto,105 St. George Street
    4. No of days: 1
    5. Registration cost: $450
    6. Major Sponsors: Creative Destruction Labs
    7. With whom you can network: CEOs and industry leaders, entrepreneurs, investors, academics, government officials, etc. 
  2. AI Toronto, Toronto
    1. About the conference: AI Toronto will focus on technical and practical verticals including use cases around Real-time analytics, AI regulations, NLP, Business Automation, Customer Service Automation, Machine Learning, Data Visualization, Recommendation Engines, Smart Home and IoT.
    2. Event date: 12-13 June 2019
    3. Venue: North Building, 255, Front Street, West Toronto, Ontario
    4. No of days: 2
    5. Registration cost: Free of Cost
    6. Major sponsors: IBM, Mastercard
    7. Speakers & Profile:
      1. Sahi Ahmed- Vice President, Bank of Montreal
      2. Rohaan Ahmed- Project Engineer, MDA Corporation
  3. Toronto Machine Learning Society Annual Conference and Expo, Toronto
    1. About the conference: The TMLS aims to celebrate the top achievements in AI and Machine Learning research and applications in various industries. This conference had different workshops and real case studies that were discussed by the scholars and experts at the event related to the field of Machine Learning.
    2. Event date : 21  November, 2019
    3. Venue:  The Carlu, 444 Yonge Street, Toronto, Canada
    4. No. of days: 1
    5. Purpose of the Conference: The TMLS initiative is dedicated to helping promote the development of AI/ML effectively, and responsibly across all Industries. 
    6. Registration cost : $247
    7. With whom can you network: Leaders of major leading companies in the Machine Learning and Artificial Intelligence industry.
S.NoConference nameDateVenue
1.Toronto Machine Learning Summit2-3 November, 2017Daniels Spectrum, 585 Dundas Street East, Toronto, Canada

1.Toronto Machine Learning Summit, Toronto

  1. About the conference: The TMLS was organised to celebrate the top achievements in AI and Machine Learning research and applications in various industries. This conference had different workshops and real case studies that were discussed by the scholars and experts related to the field of Machine Learning.
  2. Event date : 2-3  November, 2017
  3. Venue : Daniels Spectrum, 585 Dundas Street East, Toronto, Canada
  4. No. of days : 2
  5. Purpose of the Conference: The TMLS initiative is dedicated to helping promote the development of AI/ML effectively, and responsibly across all the industries. This conference was aimed to provide help to the data practitioners, researchers and students to fast-track their learning process and develop rewarding careers in the field of Machine Learning and AI.
  6. Registration cost : $247
  7. Who can you network with: ML PhD/deep learning researchers, various C level business leaders, and industry experts.

Machine Learning Engineer Jobs in Toronto, Canada

The responsibilities of a Machine Learning Engineer in Toronto include the following:

  • Design and develop systems used for machine learning and deep learning
  • Implement machine learning algorithm
  • Conduct experiments and tests on datasets
  • Find patterns and predict unseen instances using data modelling

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. 

Some of the companies in Toronto with Machine Learning open positions are:

  • Thomson Reuters
  • TD Bank
  • Google
  • SoundHound Inc.
  • Capgemini

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:

  • Machine Learning Engineer
  • Cloud Architect
  • Data Architect
  • Data Scientist
  • Data Mining Specialist
  • Cyber Security Analyst

Here is how you can network with other Machine Learning Engineers in Toronto:

  • Online platforms like LinkedIn
  • Meetups
  • Conferences and tech talks

A Machine Learning can earn about $220,000 per year in Toronto.

https://neuvoo.ca/salary/?job=Machine%20Learning%20Engineer 

Machine Learning with Python Toronto, Canada

To get started with mastering Machine Learning using Python, you need to follow the below-mentioned steps:

  1. Believe in yourself and stay focused and motivated
  2. Install Python SciPy kit and all its packages
  3. Go through all the available functions and their uses
  4. Load the dataset
  5. Perform data visualization and statistical summaries to get a better understanding of the data.
  6. Get an understanding of the ML concepts by implementing them on the dataset
  7. Create a project. Start with something simple and then move on to more complex projects.

Here are the top essential Python libraries that are used for implementing Machine Learning using Python:

  • Scikit-learn: It is used for mining and analyzing the data.
  • Numpy: This library offers high performance through N-dimensional arrays.
  • Pandas: This library is used for extracting and preparing data using high-level data structures.
  • Matplotlib: It is used for visualizing data by plotting graphs and charts.
  • TensorFlow: It is the best library for implementing concepts of deep learning in your project. It is used for training, setting up, and deploying artificial neural networks.

Here are the 5 best tips for learning Python programming as a beginner:

  • Consistency is Key: Commit to learning Python and practicing it every day. The more you practice, the better your programming skills will be. You can start with coding for 30 minutes a day to an hour.
  • Write it out: Writing notes from the beginning will help you retain concepts of programming better. 
  • Go interactive: Use the interactive Python shell. This will help you in understanding data structures of Python. To initialize the Python shell, follow these steps:
    • Open the terminal
    • In the command line, type ‘Python’
    • Press ‘Enter’
  • Assume the role of a Bug Bounty Hunter: Don’t worry if you get too many bugs in your code. Just calm down and solve each bug. The more bugs you solve, the better your programming skills will be.
  • Surround yourself with other people who are learning: Meet fellow Python developers and try to learn from their experience.

If you want to learn to use Python for machine learning, you need to familiarize yourself with the following Python libraries:

  • Scikit-learn: This library is used for data analysis, data science, and data mining.
  • SciPy: This library is used for manipulation of concepts of engineering and mathematics.
  • Numpy: It provides efficiency through vector and matrix operations.
  • Keras: It works with neural networks.
  • TensorFlow: It uses multi-layered nodes to train, setup, and deploy artificial neural networks.
  • Pandas: It performs data extraction and preparation using high-level data structures.
  • Matplotlib: Plots 2D graphs that are used for data visualization.
  • Pytorch: It is used for handling Natural Language Processing.

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Faqs

The Course

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!

You will:
  • Get advanced knowledge on machine learning techniques using Python
  • Be proficient with frameworks like TensorFlow and Keras

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.

Finance Related

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.

KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.

The Remote Experience

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.

Have More Questions?

Mail Us support@knowledgehut.com

Machine Learning with Python Course in Toronto

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.