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Artificial Intelligence Course in Toronto, Canada

82,124 Ratings

Our artificial intelligence course in Toronto is an industry-designed course to learn TensorFlow, CNN, RNN, machine learning, Git, etc. through real-world case studies and projects. Get the best online artificial intelligence training from artificial intelligence certified experts.

Ranked #1 Artificial Intelligence Course by India TV

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AI Course Key Highlights

50+ Live sessions across 7 months
218 Hrs Self-paced Videos
200 Hrs Project & Exercises
Learn from IIT Faculty & Industry Practitioners
1:1 with Industry Mentors
Resume Preparation and LinkedIn Profile Review
24*7 Support
No-cost EMI Option
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Artificial Intelligence Course in Toronto Overview

What will you learn in this best Artificial Intelligence course in Toronto?

Through this artificial intelligence course in Toronto, Canada, you will able to master the below-mentioned skills:

  • Introduction to artificial intelligence
  • Various components of AI and its subsets
  • Application of convolutional neural networks
  • Numerical computing using TensorFlow
  • Tensor Processing Unit
  • Various machine learning methodologies
  • Applications of AI in real-world scenarios
  • Professionals in analytics, data science, e-commerce, and search engine domains
  • Software professionals looking for a career switch
  • Fresh graduates

Anybody can take up this artificial intelligence course in Toronto with placement, regardless of their prior skills.

Toronto is the financial and commercial capital of Canada. It has some of the biggest and best enterprises that are deploying artificial intelligence technologies at scale. If you are trained in artificial intelligence along with deep learning, then you can benefit from the huge number of job opportunities in artificial intelligence available in Toronto, Canada.

Toronto is a top technology hub in Canada, and futuristic technologies like artificial intelligence are extensively deployed in this city, making it the hotbed for some really good AI start-ups and also well-entrenched players. Getting the right training in AI can help you make the best use of this boom in the AI market in Toronto.

Today, artificial intelligence has conquered almost every industry. Within a year or two, nearly 80% of emerging technologies will be based on AI. Machine learning, especially deep learning, the most important aspect of artificial intelligence, is used by AI-powered recommender systems (chatbots) and search engines for online customer recommendations. Therefore, to remain relevant and gain expertise in this emerging technology, enroll in AI course in Toronto.
 
Here are a few reasons why artificial intelligence is a great career option:
 

Country Job Opportunities Average Annual Salary (USD)
United States 35,000+ $114,000
Canada 50,000+ $255,422
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Artificial Intelligence would be the ultimate version of Google - Larry Page
The global Artificial Intelligence market size is expected to grow at a compound annual growth rate (CAGR) of 42.2% from 2020 to 2027 - Grand View Research

Career Transition

57% Average Salary Hike

$1,14,000 Highest Salary

12000+ Career Transitions

300+ Hiring Partners

Career Transition Handbook

*Past record is no guarantee of future job prospects

Meet the Artificial Intelligence Mentors

Skills Covered

Python

Data Science

Data Analysis

AI

Git

MLOps

Data Wrangling

SQL

Storytelling

Machine Learning

Prediction Algorithms

NLP

PySpark

Model

Data Visualization

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Tools Covered

pyspark python jupyter Scipy numpy pandas matplotlib tensorflow SQL tableau excel git SparkSQL
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Artificial Intelligence Course Fees in Toronto

Online Classroom Preferred

  • Everything in Self-Paced Learning, plus
  • 50+ Live sessions across 7 months of Instructor-led Training
  • One-to-one doubt resolution sessions
  • Attend as many batches as you want for Lifetime
  • Job Assistance
30 Mar

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

20 Apr

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

18 May

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

$1,229 10% OFF Expires in

Corporate Training

  • Customized Learning
  • Enterprise grade learning management system (LMS)
  • 24x7 Support
  • Enterprise grade reporting

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Artificial Intelligence Course Content in Toronto

Live Course Self Paced

Module 1 – Programming Essentials For Artificial Intelligence

Preview
  • Installation and Setup
    Introduction to Python programming, Installing Python programming language, IDLE, Pycharm, Jupyter notebook, Google Colab notebooks for Python IDE, Python programming 101 – Hello world program.
  • Python Programming Basics
    Variables in Python, naming conventions in Python, operators in Python, data types such as Lists, Tuples, Sets, and Dictionary including CRUD operations, List and Dictionary comprehension, and in-built and user-defined functions in Python.
  • Object-Oriented Programming Using Python Programming
    What are classes and objects, what is the difference between procedural and object-oriented programming, what are OOP concepts, what is inheritance, what is encapsulation, what is data abstraction, what is polymorphism, and understand how to implement OOP concepts using Python programming language
  • Hands-on for Practice
    Coding challenges for interview preparation, hangman game using Python and OOPs, calculator app using Python and OOPs, etc.
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Module 2 – Data Transformation & Management Using SQL

Preview
  • Introduction to SQL
    What is SQL, why use SQL, SQL clauses, variables in SQL, how to create tables, operators in SQL, CRUD operations in SQL, Joins in SQL, etc.
  • Advanced SQL Queries and Constructs
    User-defined functions in SQL, built-in functions in SQL, aggregate functions in SQL, String functions in SQL, pattern matching using SQL, date and time functions in SQL, rules, views, and nested queries in SQL, Inline table values using SQL, tables with multi-statements, stored procedures using SQL, rank, rollup, and other miscellaneous functions in SQL.
  • Performance and Optimization Using SQL
    Searching and sorting operations using SQL for optimizing faster data retrieval, grouping, and clustered indexes in SQL, CTEs, and their uses in SQL.
  • Hands-On for Practice
    Student performance management data to analyze and assess the performance of students, and hotel management data to analyze and assess future influx and trends in guest behavior.
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  • Loading the Data
    How to load data from various sources using pandas in Python, loading data from .csv files, loading data from .xlsx files, loading data from web APIs, etc.
  • Processing the Data
    Numpy arrays and matrices, multi-dimensional arrays in NumPy, NumPy operations for reading, writing, updating, and deleting arrays, NumPy in-built functions for operations such as merging, concatenating, etc.
  • Data Manipulation
    Data frames and series in Pandas, Pandas operations on data frames and series for reading, writing, updating, and deleting values in pandas series and data frames, inbuilt functions in Pandas for concatenation, stacking operations, insertion and deletion operations, etc. Joins and merging operations on Pandas series and data frames.
  • Data Visualization
    Understanding the basics of visual plots to assess and analyze data points, various kinds of plots like bar plots, count plots, scatter plots, line plots, etc using matplotlib and seaborn using Python programming.
  • Hands-On for Practice
    The EDA case study covers each stage of data processing to gather insights based on the analysis. The data will cover segments from the human resources and FMCG domains.
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  • Probability Basics
    Introduction to probability, why use probability for artificial intelligence, understanding events and probabilities, introduction to conditions probability, conditional probability, and Bayes theorem
  • Descriptive Statistics for Summary
    What is descriptive statistics, what are mean, median, and mode, what significance does the measure of central tendency give, what are variance, and standard deviation, what significance do measures of spread give, how do you measure the skewness in the data, what are range and other measures to describe data
  • Inferential Statistics for Evidence-Based Analysis
    Introduction to inferential statistics, sampling, p-value, alpha value, types of errors, hypothesis testing, one-tailed tests, two-tailed tests, one-sample tests, two-sample tests, hypothesis tests for mean, variance, etc. Feature selection, feature engineering, and feature scaling techniques are used to prepare data for training.
  • Hands-On for Practice
    Advanced evidence-based statistical findings on sales and marketing data, where evidence-based findings can substantiate plans, inventory management, and other contingency planning to increase efficiency and revenue.
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  • Introduction to Machine Learning
    What is machine learning in simple words, what are the applications and use cases of machine learning, how are industries using machine learning in their processes and workflows, what is the process of creating a machine learning model, what is the machine learning life cycle, real-life examples of machine learning implementation
  • Supervised Learning vs. Unsupervised Learning
    What is supervised learning and unsupervised learning, how do supervised and unsupervised learning work, what are the major differences between supervised and unsupervised machine learning, what are various applications of supervised and unsupervised learning
  • Classification Using Machine Learning
    What is classification, Introduction to classification problems in machine learning, machine learning algorithms for classification problems such as logistic regression, decision tree, random forest, etc. metrics to measure the efficiency of classification models such as classification report, and hyperparameter tuning to optimize solutions
  • Regression Using Machine Learning
    What is regression, introduction to regression problems in machine learning, machine learning algorithms for regression problems such as linear regression, decision trees, random forest, etc. hyperparameter tuning on regression models, evaluation metrics to check the efficacy of regression models such as MSE (mean squared error), MAPE  (mean absolute percentage error), MAE (mean absolute error), etc.
  • Clustering Using Machine Learning
    What are clustering problems in machine learning, machine learning algorithms for clustering such as K-Means clustering, hierarchical clustering, etc. Evaluation metrics such as silhouette score, inter and intra-cluster variance, etc. Hyperparameter tuning on clustering problems.
  • Forecasting Using Machine Learning
    Forecasting in machine learning, what are time series, various components of time series such as trends, seasonality, stationarity, etc. Time series forecasting using AR, MA, ARMA, ARIMA, and seasonal ARIMA models.
  • Advanced Machine Learning Algorithms
    Introduction to advanced machine learning algorithms such as – XGBoost algorithm, AdaBoost algorithm, Gradient Tree Boosting algorithm, Bootstrap Aggregation algorithm, Ordinary Least Square OLS algorithm,  Markov Chains, Naive Bayes algorithm, Stochastic Gradient Descent algorithm, Gaussian Mixture Model GMM, Singular Value Decomposition SVD (recommendation engines)
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  • Introduction to Deep Learning
    What is deep learning, the deep learning project life cycle, real-life applications of deep learning, the future of deep learning, ML vs. DL, etc. Introduction to TensorFlow, setup, and installation.
  • Neural Networks in Deep Learning
    Single-cell perceptrons, What is a neural network, how does a neural network work, applications of neural networks, fully connected neural networks, how to train neural networks, how to evaluate neural networks, what are weights and biases, what are activation functions, what are layers in a neural network, and building a neural network from scratch using NumPy. Introduction to Keras and sequential modeling.
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  • Convolutional Neural Networks
    Why use CNN or convolutional neural networks, significance and usage of Convolutional neural networks, the curse of flattening, the Math of Filter/ Kernel, Reduction, Pooling layers, Batch Normalization & Dropout. Process of augmentation, testing the neural network models, learning from the pre-built models, transfer learning, VGG16, fine-tuning, etc.
  • Recurrent Neural Networks
    What is context, introduction to Recurrent Neural Networks, understanding RNN and how it works, usage and applications of RNN, working and implementation of RNN, forecasting problems with RNN, text data, and RNN.
  • Image Classification
    What is image classification, how to perform image classification, implementation of classification model using image data, introduction to object detection, using pre-trained model weights for image classification, etc.
  • Text Classification
    Introduction to text processing and classification using deep neural networks; usage and implementation of text classification using TensorFlow; sentiment analysis using deep neural networks, etc.
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  • Text Processing
    Introduction to NLP, what is text processing, what is text mining, what is tokenization, how are tokens used in NLP, what are lexicons, what is lemmatization, POS tagging, and its usage, etc.
  • Text Classification and Analysis
    What is text classification and sentiment analysis, how do you perform feature engineering in NLP, classification models for NLP, what is vectorization, what is IDF, etc
  • Language Modeling
    What is language modeling, what are sequences, how do you predict sequences, what is chunking, context-free grammar, ER modeling, etc
  • Chatbots Using Natural Language
    Building recommendation engines and sentiment analysis chatbots using Natural Language Processing.
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  • Introduction to Computer Vision
    What is computer vision, how do images get processed in deep learning, what is image processing, how do you use computer vision, what are various applications of computer vision, implementation of computer vision
  • Object Detection Using Deep Neural Networks
    Object detection using OpenCV Python, object detection on images, object detection in real-time, object detection on video, object detection on a pre-trailed model weights on custom dataset.
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Electives:

  • LSTM
    Introduction to LSTM, applications of LSTM, why use LSTM, and working and implementation of LSTM.
  • Transformers
    What are transformers, why do we use transformers, How do transformers work, implementation of transformers, transformer Encoder-decoder architecture, self-attention, etc
  • BERT
    What are language models, how does a language model work, what is BERT, an introduction to working on BERT, how can you use BERT, etc.
  • GPT
    What are generative pre-trained models, how can you fine-tune GPTs for your applications, real-life examples and applications of GPTs, how does a GPT work, etc.
  • LLM
    What are large language models, how is LLM different than BERT, how do you use an LLM, what are the limitations and advantages of LLM, LLM implementation, using LLM on custom data, etc
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  • Introduction to Prompt Engineering
    What is prompt engineering, the science behind prompt engineering, usage and implementation of prompt engineering, prompt engineering hacks, etc
  • Prompt Engineering for Developers
    ChatGPT in development, using Bito AI Code Assistant for development, ChatGPT for front end and back end
  • Prompt Engineering for E-commerce
    ChatGPT to generate leads, ChatGPT to qualify leads, ChatGPT to close deals, creating engaging product descriptions, personalizing your customer experience, and boosting your e-commerce sales.
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  • Introduction to Reinforcement Learning
    What is reinforcement learning, applications and usage of reinforcement learning, implementation of reinforcement learning, agent taxonomy, and learning process in reinforcement learning
  • Learning Methods in Reinforcement Learning
    Monte Carlo learning method, temporal difference learning methods
  • Markov Decision and Bandit
    Introduction to the bandit algorithm, what is the Markov process, rewards in the Markov process, decisions in the Markov process, etc.
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The artificial intelligence capstone project will assess your approach to solving complex business problems using deep neural networks. You should perform the following in your project:

  1. Identify the problem and the approach to solving it using deep learning networks. 
  2. Data processing and feature engineering for the problem. 
  3. Training of the neural network, input layers, output layers, etc
  4. Evaluation and optimization of the deep learning model based on the efficiency of the predictions made by the model.
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  • Strategy building for the job search
  • Highlighting your professional expertise with an effective resume
  • Showcasing your skills and expertise on LinkedIn
  • Top interview preparation tips from industry experts
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Artificial Intelligence Assignments and Projects

Peer Learning

Via Intellipaat PeerChat, you can interact with your peers across all classes and batches and even our alumni. Collaborate on projects, share job referrals & interview experiences, compete with the best, make new friends — the possibilities are endless and our community has something for everyone!

class-notifications
Hackathons
career-services
major-announcements
collaborative-learning

Career Services

Career Services

Career Oriented Sessions

Throughout the course

Over 10+ live interactive sessions with an industry expert to gain knowledge and experience on how to build skills that are expected by hiring managers. These will be guided sessions that will help you stay on track with your upskilling.

Resume & LinkedIn Profile Building

After 80% of the course completion

Get assistance in creating a world-class resume & LinkedIn Profile from our career services team and learn how to grab the attention of the hiring manager at the profile shortlisting stage

Mock Interview Preparation

After 80% of the course completion.

Students will go through several mock interviews conducted by technical experts who will then offer tips and constructive feedback for reference and improvement.

1 on 1 Career Mentoring Sessions

After 90% of the course completion

Attend one-on-one sessions with career mentors on how to develop the required skills and attitude to secure a dream job based on a learner’s educational background, past experience, and future career aspirations.

Placement Assistance

Upon movement to the Placement Pool

Placement opportunities are provided once the learner is moved to the placement pool upon clearing the Placement Readiness Test (PRT)

Exclusive access to Intellipaat Job portal

After 80% of the course completion

Exclusive access to our dedicated job portal and apply for jobs. More than 400 hiring partners’ including top start-ups and product companies hiring our learners. Mentored support on job search and relevant jobs for your career growth.

Artificial Intelligence Certification in Toronto

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IITM Pravartak has initiated various programs in partnership with NASSCOM. The courses offered by them aim to upskill millions of students and professionals in trending technologies through a blend of theoretical and hands-on knowledge and are taught by leading academicians.

Upon completion of this AI course in Toronto, you will:

  • Receive an Advanced Certification in Data Science and AI IIT Madras.
  • Receive live lectures from IIT Madras faculty & Industry Experts

Key achievements of IIT Madras:

  • NIRF Rank 1 for the last 3 years
  • Ranked 50 in Asia by the QS World University Rankings in 2020
  • Ranked 63 in Emerging Economic University Rankings in 2020

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Artificial Intelligence Course in Toronto FAQs

What is best artificial intelligence course in Toronto, Canada, like?

Our artificial intelligence online training involves the participation of both learners and instructors in an online environment. As a learner, you can log in to our applied AI course sessions from anywhere and attend the class without having to be present physically. Also, we record the proceedings of all our AI classes to further enhance your learning process. Upon the completion of this online AI training in Toronto, your experience will be equivalent to that of a professional who has worked for 6 months in the industry.

Intellipaat is the leading provider of artificial intelligence courses in Toronto. The courses such as machine learning, data science, data analytics, R programming language, and others help you become job-ready by focusing on the practical implementations of the concepts in real-time live projects.

Intellipaat is offering 24/7 query resolution, and you can raise a ticket with the dedicated support team at any time. You can avail of email support for all your queries. If your query does not get resolved through email, we can also arrange one-on-one sessions with our support team. However, 1:1 session support is provided for a period of 6 months from the start date of your course.

Intellipaat provides placement assistance to all learners who have successfully completed the training and moved to the placement pool after clearing the PRT( Placement Readiness Test) More than 500+ top MNC’s and startups hire Intellipaat learners. Our Alumni works with Google, Microsoft, Amazon, Sony, Ericsson, TCS, Mu Sigma, etc.

Apparently, no. Our job assistance is aimed at helping you land in your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final decision on hiring will always be based on your performance in the interview and the requirements of the recruiter.

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