Machine Learning with Python Training in Toronto, Canada

Build Python skills with varied approaches to Machine Learning

  • Supervised & Unsupervised Learning, Regression & Classifications and more
  • Advanced ML algorithms like KNN, Decision Trees, SVM and Clustering
  • Build and deploy deep learning and data visualization models in a real-world project
  • 250,000 + Professionals Trained
  • 250 + Workshops every month
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Grow your Machine Learning skills

In this four-week course, you will dive into the basics of machine learning using the well-known programming language, Python. Get introduced to data exploration and discover the various machine learning approaches like supervised and unsupervised learning, regression, and classifications and more.

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  • 48 Hours of Live Instructor-Led Sessions

  • 80 Hours of Assignments and MCQs

  • 45 Hours of Hands-On Practice

  • 10 Real-World Live Projects

  • Fundamentals to an Advanced Level

  • Code Reviews by Professionals

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Why Machine Learning?


Data Science has bagged the top spot in LinkedIn’s Emerging Jobs Report for the last three years. Thousands of companies need team members who can transform data sets into strategic forecasts. Acquire in-demand machine learning and Python skills and meet that need.  

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Our immersive learning approach lets you learn by doing and acquire immediately applicable skills hands-on. 

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Prerequisites for Machine Learning with Python training

  • Sufficient knowledge of at least one coding language is required.
  • Minimalistic and intuitive, Python is best-suited for Machine Learning training.

Who should attend this course?

Anyone interested in Machine Learning and using it to solve problems

Software or data engineers interested in quantitative analysis with Python

Data analysts, economists or researchers

Machine Learning with Python Course Schedules

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What you will learn in the Machine Learning with Python course


Python for Machine Learning

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


Fundamentals of Machine Learning

Learn about Supervised and Unsupervised Machine Learning.


Optimization Techniques

Learn to use optimization techniques to find the minimum error in your Machine Learning model.


Supervised Learning

Learn about Linear and Logistic Regression, KNN Classification and Bayesian Classifiers.


Unsupervised Learning

Study K-means Clustering  and Hierarchical Clustering.


Ensemble techniques

Learn to use multiple learning algorithms to obtain better predictive performance .


Neural Networks

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

Skill you will gain with the Machine Learning with Python course

Advanced Python programming skills

Manipulating and analysing data using Pandas library

Data visualization with Matplotlib, Seaborn, ggplot

Distribution of data: variance, standard deviation, more

Calculating conditional probability via Hypothesis Testing

Analysis of Variance (ANOVA)

Building linear regression models

Using Dimensionality Reduction Technique

Building Logistic Regression models

K-means Clustering and Hierarchical Clustering

Building KNN algorithm models to find the optimum value of K

Building Decision Tree models for both regression and classification

Hyper-parameter tuning like regularisation

Ensemble techniques: averaging, weighted averaging, max voting

Bootstrap sampling, bagging and boosting

Building Random Forest models

Finding optimum number of components/factors

PCA/Factor Analysis

Using Apriori Algorithm and key metrics: Support, Confidence, Lift

Building recommendation engines using UBCF and IBCF

Evaluating model parameters

Measuring performance metrics

Using scree plot, one-eigenvalue criterion

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Machine Learning with Python Training Curriculum

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Learning objectives
In this module, you will learn the basics of statistics including:

  • Basics of statistics like mean (expected value), median and mode 
  • Distribution of data in terms of variance, standard deviation, and interquartile range; and explore data and measures and simple graphics analyses  
  • Basics of probability via daily life examples 
  • Marginal probability and its importance with respect to Machine Learning 
  • Bayes’ theorem and conditional probability including alternate and null hypotheses  


  • Statistical Analysis Concepts  
  • Descriptive Statistics  
  • Introduction to Probability 
  • Bayes’ Theorem  
  • Probability Distributions  
  • Hypothesis Testing and Scores  


  • Learning to implement statistical operations in Excel

Learning objectives
In the Python for Machine Learning module, you will learn how to work with data using Python:

  • How to define variables, sets, and conditional statements 
  • The purpose of 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 with Python 
  • Data Visualization using Python libraries like matplotlib, seaborn and ggplot 


  • Python Overview  
  • Pandas for pre-Processing and Exploratory Data Analysis  
  • NumPy for Statistical Analysis  
  • Matplotlib and Seaborn for Data Visualization  
  • Scikit Learn 

Learning objectives
Get introduced to Machine Learning via real-life examples and the multiple ways in which it affects our society. You will learn:

  • Various algorithms and models like Classification, Regression, and Clustering.  
  • Supervised vs Unsupervised Learning 
  • How Statistical Modelling relates to Machine Learning 


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

Learning objectives
Gain an understanding of various optimisation techniques such as:

  • Batch Gradient Descent 
  • Stochastic Gradient Descent 
  • ADAM 
  • RMSProp


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

Learning objectives
In this module you will learn about Linear and Logistic Regression with Stochastic Gradient Descent via real-life case studies

  • Hyper-parameters tuning like learning rate, epochs, momentum, and class-balance 
  • The concepts of Linear and Logistic Regression with real-life case studies 
  • How KNN can be used for a classification problem with a real-life case study on KNN Classification  
  • About Naive Bayesian Classifiers through another case study 
  • How Support Vector Machines can be used for a classification problem 
  • About hyp


  • Linear Regression Case Study  
  • Logistic Regression Case Study  
  • KNN Classification Case Study  
  • Naive Bayesian classifiers Case Study  
  • SVM - Support Vector Machines Case Study


  • Build a regression model to predict the property prices using optimization techniques like gradient descent based on attributes describing various aspect of residential homes 
  • Use 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 based on the health metrics 
  • Use Naive Bayesian technique for text classifications to predict which incoming messages are spam or ham 
  • Build models to study the relationships between chemical structure and biodegradation of molecules to correctly classify if a chemical is biodegradable or non-biodegradable 

Learning objectives
Learn about unsupervised learning techniques:

  • K-means Clustering  
  • Hierarchical Clustering  


  • Clustering approaches  
  • K Means clustering  
  • Hierarchical clustering  
  • Case Study


  • Perform a real-life case study on K-means Clustering  
  • Use K-Means clustering to group teen students into segments for targeted marketing campaigns

Learning objectives
Learn the ensemble techniques which enable you to build machine learning models including:

  • Decision Trees for regression and classification problems through a real-life case study 
  • Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID 
  • Basic ensemble techniques like averaging, weighted averaging and max voting 
  • You will learn about bootstrap sampling and its advantages followed by bagging and how to boost model performance with Boosting 
  • Random Forest, with a real-life case study, and how it helps avoid overfitting compared to decision trees 
  • The Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis 
  • The comprehensive techniques used to find the optimum number of components/factors using scree plot, one-eigenvalue criterion 
  • PCA/Factor Analysis via a case study 


  • Decision Trees with a Case Study 
  • Introduction to Ensemble Learning  
  • Different Ensemble Learning Techniques  
  • Bagging  
  • Boosting  
  • Random Forests  
  • Case Study  
  • PCA (Principal Component Analysis)  
  • PCA 
  • Its Applications  
  • Case Study


  • Build a model to predict the Wine Quality using Decision Tree (Regression Trees) based on the composition of ingredients 
  • Use AdaBoost, GBM, and Random Forest on Lending Data to predict loan status and ensemble the output to see your results 
  • Apply Reduce Data Dimensionality on a House Attribute Dataset to gain more insights and enhance modelling.  

Learning objectives
Learn to build recommendation systems. You will learn about:

  • Association Rules 
  • Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift 
  • UBCF and IBCF including how they are used in Recommender Engines 


  • Introduction to Recommendation Systems  
  • Types of Recommendation Techniques  
  • Collaborative Filtering  
  • Content-based Filtering  
  • Hybrid RS  
  • Performance measurement  
  • Case Study


  • Build a Recommender System for a Retail Chain to recommend the right products to its customers 

FAQs on the Machine Learning with Python Course

Machine Learning with Python Training

KnowledgeHut’s Machine Learning with Python workshop is focused on helping professionals gain industry-relevant Machine Learning expertise. The curriculum has been designed to help professionals land lucrative jobs across industries. At the end of the course, you will be able to: 

  • Build Python programs: distribution, user-defined functions, importing datasets and more 
  • Manipulate and analyse data using Pandas library 
  • Visualize data with Python libraries: Matplotlib, Seaborn, and ggplot 
  • Build data distribution models: variance, standard deviation, interquartile range 
  • Calculate conditional probability via Hypothesis Testing 
  • Perform analysis of variance (ANOVA) 
  • Build linear regression models, evaluate model parameters, and measure performance metrics 
  • Use Dimensionality Reduction 
  • Build Logistic Regression models, evaluate model parameters, and measure performance metrics 
  • Perform K-means Clustering and Hierarchical Clustering  
  • Build KNN algorithm models to find the optimum value of K  
  • Build Decision Tree models for both regression and classification problems  
  • Use ensemble techniques like averaging, weighted averaging, max voting 
  • Use techniques of bootstrap sampling, bagging and boosting 
  • Build Random Forest models 
  • Find optimum number of components/factors using scree plot, one-eigenvalue criterion 
  • Perform PCA/Factor Analysis 
  • Build Apriori algorithms with key metrics like Support, Confidence and Lift 
  • Build recommendation engines using UBCF and IBCF 

The program is designed to suit all levels of Machine Learning expertise. From the fundamentals to the advanced concepts in Machine Learning, the course covers everything you need to know, whether you’re a novice or an expert. 

To facilitate development of immediately applicable skills, the training adopts an applied learning approach with instructor-led training, hands-on exercises, projects, and activities. 

This immersive and interactive workshop with an industry-relevant curriculum, capstone project, and guided mentorship is your chance to launch a career as a Machine Learning expert. The curriculum is split into easily comprehensible modules that cover the latest advancements in ML and Python. The initial modules focus on the technical aspects of becoming a Machine Learning expert. The succeeding modules introduce Python, its best practices, and how it is used in Machine Learning.  

The final modules deep dive into Machine Learning and take learners through the algorithms, types of data, and more. In addition to following a practical and problem-solving approach, the curriculum also follows a reason-based learning approach by incorporating case studies, examples, and real-world cases.

Yes, our Machine Learning with Python course is designed to offer flexibility for you to upskill as per your convenience. We have both weekday and weekend batches to accommodate your current job. 

In addition to the training hours, we recommend spending about 2 hours every day, for the duration of course.

The Machine Learning with Python course is ideal for:
  1. Anyone interested in Machine Learning and using it to solve problems  
  2. Software or Data Engineers interested in quantitative analysis with Python  
  3. Data Analysts, Economists or Researchers

There are no prerequisites for attending this course, however prior knowledge of elementary Python programming and statistics could prove to be handy. 

To attend the Machine Learning with Python training program, the basic hardware and software requirements are as mentioned below

Hardware requirements 

  • Windows 8 / Windows 10 OS, MAC OS >=10, Ubuntu >= 16 or latest version of other popular Linux flavors 
  • 4 GB RAM 
  • 10 GB of free space  

Software Requirements  

  • Web browser such as Google Chrome, Microsoft Edge, or Firefox  

System Requirements 

  • 32 or 64-bit Operating System 
  • 8 GB of RAM 

On adequately completing all aspects of the Machine Learning with Python course, you will be offered a course completion certificate from KnowledgeHut.  

In addition, you will get to showcase your newly acquired Machine Learning skills by working on live projects, thus, adding value to your portfolio. The assignments and module-level projects further enrich your learning experience. You also get the opportunity to practice your new knowledge and skillset on independent capstone projects. 

By the end of the course, you will have the opportunity to work on a capstone project. The project is based on real-life scenarios and carried-out under the guidance of industry experts. You will go about it the same way you would execute a Machine Learning project in the real business world.  

Workshop Experience

The Machine Learning with Python workshop at KnowledgeHut is delivered through PRISM, our immersive learning experience platform, via live and interactive instructor-led training sessions.  

Listen, learn, ask questions, and get all your doubts clarified from your instructor, who is an experienced Data Science and Machine Learning industry expert.  

The Machine Learning with Python course is delivered by leading practitioners who bring trending, best practices, and case studies from their experience to the live, interactive training sessions. The instructors are industry-recognized experts with over 10 years of experience in Machine Learning. 

The instructors will not only impart conceptual knowledge but end-to-end mentorship too, with hands-on guidance on the real-world projects. 

Our Machine Learning course focuses on engaging interaction. Most class time is dedicated to fun hands-on exercises, lively discussions, case studies and team collaboration, all facilitated by an instructor who is an industry expert. The focus is on developing immediately applicable skills to real-world problems.  

Such a workshop structure enables us to deliver an applied learning experience. This reputable workshop structure has worked well with thousands of engineers, whom we have helped upskill, over the years. 

Our Machine Learning with Python workshops are currently held online. So, anyone with a stable internet, from anywhere across the world, can access the course and benefit from it. 

Schedules for our upcoming workshops in Machine Learning with Python can be found here.

We currently use the Zoom platform for video conferencing. We will also be adding more integrations with Webex and Microsoft Teams. However, all the sessions and recordings will be available right from within our learning platform. Learners will not have to wait for any notifications or links or install any additional software.   

You will receive a registration link from PRISM to your e-mail id. You will have to visit the link and set your password. After which, you can log in to our Immersive Learning Experience platform and start your educational journey.  

Yes, there are other participants who actively participate in the class. They remotely attend online training from office, home, or any place of their choosing. 

In case of any queries, our support team is available to you 24/7 via the Help and Support section on PRISM. You can also reach out to your workshop manager via group messenger. 

If you miss a class, you can access the class recordings from PRISM at any time. At the beginning of every session, there will be a 10-12-minute recapitulation of the previous class.

Should you have any more questions, please raise a ticket or email us on and we will be happy to get back to you. 

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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 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

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:

  • Machine Learning TO Meetup
  • Toronto Women in Machine Learning and Data Science
  • Toronto – Machine Learning in Cloud
  • Toronto Artificial Intelligence and Deep Learning

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

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.

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.

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