4.9 (16,540) reviews C 4,270
C 2,951*
*Offer valid till 31st October 2026
Instructor Led Live Online
Self Learning + Live Mentoring
Customize Your Training
MODULE 1 : ARTIFICIAL INTELLIGENCE OVERVIEW
• Evolution Of Human Intelligence
• What Is Artificial Intelligence?
• History Of Artificial Intelligence
• Why Artificial Intelligence Now?
• Areas Of Artificial Intelligence
• AI Vs Data Science Vs Machine Learning
MODULE 2 : DEEP LEARNING INTRODUCTION
• Deep Neural Network
• Machine Learning vs Deep Learning
• Feature Learning in Deep Networks
• Applications of Deep Learning Networks
MODULE3 : TENSORFLOW FOUNDATION
• TensorFlow Structure and Modules
• Hands-On:ML modeling with TensorFlow
MODULE 4 : COMPUTER VISION INTRODUCTION
• Image Basics
• Convolution Neural Network (CNN)
• Image Classification with CNN
• Hands-On: Cat vs Dogs Classification with CNN Network
MODULE 5 : NATURAL LANGUAGE PROCESSING (NLP)
• NLP Introduction
• Bag of Words Models
• Word Embedding
• Hands-On:BERT Algorithm
MODULE 6 : AI ETHICAL ISSUES AND CONCERNS
• Issues And Concerns Around Ai
• Ai And Ethical Concerns
• Ai And Bias
• Ai:Ethics, Bias, And Trust
MODULE 1 : PYTHON BASICS
• Introduction of python
• Installation of Python and IDE
• Python Variables
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
MODULE 2 : PYTHON CONTROL STATEMENTS
• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements
MODULE 3 : PYTHON DATA STRUCTURES
• Basic data structure in python
• Basics of List
• List: Object, methods
• Tuple: Object, methods
• Sets: Object, methods
• Dictionary: Object, methods
MODULE 4 : PYTHON FUNCTIONS
• Functions basics
• Function Parameter passing
• Lambda functions
• Map, reduce, filter functions
MODULE 1 : OVERVIEW OF STATISTICS
• Introduction to Statistics
• Descriptive And Inferential Statistics
• Basic Terms Of Statistics
• Types Of Data
MODULE 2 : HARNESSING DATA
• Random Sampling
• Sampling With Replacement And Without Replacement
• Cochran's Minimum Sample Size
• Types of Sampling
• Simple Random Sampling
• Stratified Random Sampling
• Cluster Random Sampling
• Systematic Random Sampling
• Multi stage Sampling
• Sampling Error
• Methods Of Collecting Data
MODULE 3 : EXPLORATORY DATA ANALYSIS
• Exploratory Data Analysis Introduction
• Measures Of Central Tendencies: Mean,Median And Mode
• Measures Of Central Tendencies: Range, Variance And Standard Deviation
• Data Distribution Plot: Histogram
• Normal Distribution & Properties
• Z Value / Standard Value
• Empherical Rule and Outliers
• Central Limit Theorem
• Normality Testing
• Skewness & Kurtosis
• Measures Of Distance: Euclidean, Manhattan And Minkowski Distance
• Covariance & Correlation
MODULE 4 : HYPOTHESIS TESTING
• Hypothesis Testing Introduction
• P- Value, Critical Region
• Types of Hypothesis Testing
• Hypothesis Testing Errors : Type I And Type II
• Two Sample Independent T-test
• Two Sample Relation T-test
• One Way Anova Test
• Application of Hypothesis testing
MODULE 1: MACHINE LEARNING INTRODUCTION
• What Is ML? ML Vs AI
• Clustering, Classification And Regression
• Supervised Vs Unsupervised
MODULE 2: PYTHON NUMPY PACKAGE
• Introduction to Numpy Package
• Array as Data Structure
• Core Numpy functions
• Matrix Operations, Broadcasting in Arrays
MODULE 3: PYTHON PANDAS PACKAGE
• Introduction to Pandas package
• Series in Pandas
• Data Frame in Pandas
• File Reading in Pandas
• Data munging with Pandas
MODULE 4: VISUALIZATION WITH PYTHON - Matplotlib
• Visualization Packages (Matplotlib)
• Components Of A Plot, Sub-Plots
• Basic Plots: Line, Bar, Pie, Scatter
MODULE 5: PYTHON VISUALIZATION PACKAGE - SEABORN
• Seaborn: Basic Plot
• Advanced Python Data Visualizations
MODULE 6: ML ALGO: LINEAR REGRESSION
• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Modeling and Evaluation in Python
MODULE 7: ML ALGO: LOGISTIC REGRESSION
• Introduction to Logistic Regression
• How it works: Classification & Sigmoid Curve
• Modeling and Evaluation in Python
MODULE 8: ML ALGO: K MEANS CLUSTERING
• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works : K Means theory
• Modeling in Python
MODULE 9: ML ALGO: KNN
• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Modeling and Evaluation in Python
MODULE 1: FEATURE ENGINEERING
• Introduction to Feature Engineering
• Feature Engineering Techniques: Encoding, Scaling, Data Transformation
• Handling Missing values, handling outliers
• Creation of Pipeline
• Use case for feature engineering
MODULE 2: ML ALGO: SUPPORT VECTOR MACHINE (SVM)
• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Modeling and Evaluation of SVM in Python
MODULE 3: PRINCIPAL COMPONENT ANALYSIS (PCA)
• Building Blocks Of PCA
• How it works: Finding Principal Components
• Modeling PCA in Python
MODULE 4: ML ALGO: DECISION TREE
• Introduction to Decision Tree & Random Forest
• How it works
• Modeling and Evaluation in Python
MODULE 5: ENSEMBLE TECHNIQUES - BAGGING
• Introduction to Ensemble technique
• Bagging and How it works
• Modeling and Evaluation in Python
MODULE 6: ML ALGO: NAÏVE BAYES
• Introduction to Naive Bayes
• How it works: Bayes' Theorem
• Naive Bayes For Text Classification
• Modeling and Evaluation in Python
MODULE 7: GRADIENT BOOSTING, XGBOOST
• Introduction to Boosting and XGBoost
• How it works?
• Modeling and Evaluation of in Python
MODULE 1: TIME SERIES FORECASTING - ARIMA
• What is Time Series?
• Trend, Seasonality, cyclical and random
• Stationarity of Time Series
• Autoregressive Model (AR)
• Moving Average Model (MA)
• ARIMA Model
• Autocorrelation and AIC
• Time Series Analysis in Python
MODULE 2: SENTIMENT ANALYSIS
• Introduction to Sentiment Analysis
• NLTK Package
• Case study: Sentiment Analysis on Movie Reviews
MODULE 3: REGULAR EXPRESSIONS WITH PYTHON
• Regex Introduction
• Regex codes
• Text extraction with Python Regex
MODULE 4: ML MODEL DEPLOYMENT WITH FLASK
• Introduction to Flask
• URL and App routing
• Flask application – ML Model deployment
MODULE 5: ADVANCED DATA ANALYSIS WITH MS EXCEL
• MS Excel core Functions
• Advanced Functions (VLOOKUP, INDIRECT..)
• Linear Regression with EXCEL
• Data Table
• Goal Seek Analysis
• Pivot Table
• Solving Data Equation with EXCEL
MODULE 6: AWS CLOUD FOR DATA SCIENCE
• Introduction of cloud
• Difference between GCC, Azure,AWS
• AWS Service ( EC2 instance)
MODULE 7: AZURE FOR DATA SCIENCE
• Introduction to AZURE ML studio
• Data Pipeline
• ML modeling with Azure
MODULE 8: INTRODUCTION TO DEEP LEARNING
• Introduction to Artificial Neural Network, Architecture
• Artificial Neural Network in Python
• Introduction to Convolutional Neural Network, Architecture
• Convolutional Neural Network in Python
MODULE 1: DATABASE INTRODUCTION
• DATABASE Overview
• Key concepts of database management
• Relational Database Management System
• CRUD operations
MODULE 2: SQL BASICS
• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MY SQL workbench installation
MODULE 3: DATA TYPES AND CONSTRAINTS
• Numeric, Character, date time data type
• Primary key, Foreign key, Not null
• Unique, Check, default, Auto increment
MODULE 4: DATABASES AND TABLES (MySQL)
• Create database
• Delete database
• Show and use databases
• Create table, Rename table
• Delete table, Delete table records
• Create new table from existing data types
• Insert into, Update records
• Alter table
MODULE 5: SQL JOINS
• Inner join
• Outer join
• Left join
• Right join
• Cross join
• Self join
• Windows functions: Over, Partition , Rank
MODULE 6: SQL COMMANDS AND CLAUSES
• Select, Select distinct
• Aliases, Where clause
• Relational operators, Logical
• Between, Order by, In
• Like, Limit, null/not null, group by
• Having, Sub queries
MODULE 7: DOCUMENT DB/NO-SQL DB
• Introduction of Document DB
• Document DB vs SQL DB
• Popular Document DBs
• MongoDB basics
• Data format and Key methods
MODULE 1: GIT INTRODUCTION
• Purpose of Version Control
• Popular Version control tools
• Git Distribution Version Control
• Terminologies
• Git Workflow
• Git Architecture
MODULE 2: GIT REPOSITORY and GitHub
• Git Repo Introduction
• Create New Repo with Init command
• Git Essentials: Copy & User Setup
• Mastering Git and GitHub
MODULE 3: COMMITS, PULL, FETCH AND PUSH
• Code commits
• Pull, Fetch and conflicts resolution
• Pushing to Remote Repo
MODULE 4: TAGGING, BRANCHING AND MERGING
• Organize code with branches
• Checkout branch
• Merge branches
• Editing Commits
• Commit command Amend flag
• Git reset and revert
MODULE 5: GIT WITH GITHUB AND BITBUCKET
• Creating GitHub Account
• Local and Remote Repo
• Collaborating with other developers
MODULE 1: BIG DATA INTRODUCTION
MODULE 2: HDFS AND MAP REDUCE
MODULE 3: PYSPARK FOUNDATION
MODULE 4: SPARK SQL and HADOOP HIVE
MODULE 1: TABLEAU FUNDAMENTALS
• Introduction to Business Intelligence & Introduction to Tableau
• Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
• Bar chart, Tree Map, Line Chart
• Area chart, Combination Charts, Map
• Dashboards creation, Quick Filters
• Create Table Calculations
• Create Calculated Fields
• Create Custom Hierarchies
MODULE 2: POWER-BI BASICS
• Power BI Introduction
• Basics Visualizations
• Dashboard Creation
• Basic Data Cleaning
• Basic DAX FUNCTION
MODULE 3 : DATA TRANSFORMATION TECHNIQUES
• Exploring Query Editor
• Data Cleansing and Manipulation:
• Creating Our Initial Project File
• Connecting to Our Data Source
• Editing Rows
• Changing Data Types
• Replacing Values
MODULE 4 : CONNECTING TO VARIOUS DATA SOURCES
• Connecting to a CSV File
• Connecting to a Webpage
• Extracting Characters
• Splitting and Merging Columns
• Creating Conditional Columns
• Creating Columns from Examples
• Create Data Model
MODULE 1: NEURAL NETWORKS
• Structure of neural networks
• Neural network - core concepts(Weight initialization)
• Neural network - core concepts(Optimizer)
• Neural network - core concepts(Need of activation)
• Neural network - core concepts(MSE & RMSE)
• Feed forward algorithm
• Backpropagation
MODULE 2: IMPLEMENTING DEEP NEURAL NETWORKS
• Introduction to neural networks with tf2.X
• Simple deep learning model in Keras (tf2.X)
• Building neural network model in TF2.0 for MNIST dataset
MODULE 3: DEEP COMPUTER VISION - IMAGE RECOGNITION
• Convolutional neural networks (CNNs)
• CNNs with Keras-part1
• CNNs with Keras-part2
• Transfer learning in CNN
• Flowers dataset with tf2.X(part-1)
• Flowers dataset with tf2.X(part-2)
• Examining x-ray with CNN model
MODULE 4 : DEEP COMPUTER VISION - OBJECT DETECTION
• What is Object detection
• Methods of Object Detections
• Metrics of Object detection
• Bounding Box regression
• labelimg
• RCNN
• Fast RCNN
• Faster RCNN
• SSD
• YOLO Implementation
• Object detection using cv2
MODULE 5: RECURRENT NEURAL NETWORK
• RNN introduction
• Sequences with RNNs
• Long short-term memory networks(part 1)
• Long short-term memory networks(part 2)
• Bi-directional RNN and LSTM
• Examples of RNN applications
MODULE 6: NATURAL LANGUAGE PROCESSING (NLP)
• Introduction to Natural language processing
• Working with Text file
• Working with pdf file
• Introduction to regex
• Regex part 1
• Regex part 2
• Word Embedding
• RNN model creation
• Transformers and BERT
• Introduction to GPT (Generative Pre-trained Transformer)
• State of art NLP and projects
MODULE 7: PROMPT ENGINEERING
• Introduction to Prompt Engineering
• Understanding the Role of Prompts in AI Systems
• Design Principles for Effective Prompts
• Techniques for Generating and Optimizing Prompts
• Applications of Prompt Engineering in Natural Language Processing
MODULE 8: REINFORCEMENT LEARNING
• Markov decision process
• Fundamental equations in RL
• Model-based method
• Dynamic programming model free methods
MODULE 9: DEEP REINFORCEMENT LEARNING
• Architectures of deep Q learning
• Deep Q learning
• Reinforcement Learning Projects with OpenAI Gym
MODULE 10: Gen AI
• Gan introduction, Core Concepts, and Applications
• Core concepts of GAN
• GAN applications
• Building GAN model with TensorFlow 2.X
• Introduction to GPT (Generative Pre-trained Transformer)
• Building a Question answer bot with the models on Hugging Face
MODULE 11: Gen AI
• Introduction to Autoencoder
• Basic Structure and Components of Autoencoders
• Types of Autoencoders: Vanilla, Denoising, Variational, Sparse, and Convolutional Autoencoders
• Training Autoencoders: Loss Functions, Optimization Techniques
• Applications of Autoencoders: Dimensionality Reduction, Anomaly Detection, Image
AI is the future, and DataMites helped me step into it! The online Data Science course was easy to follow, and the tutors explained everything clearly. Even after completing my course, the team supported me with placements and I successfully cracked a job! Grateful to DataMites for shaping my career.
I joined DataMites to learn Data Science and gained strong skills in ML, DL, and Statistics. Through my internship at Rubixe AI Solutions, I worked on real-world projects, which helped me land job interviews and an offer. The trainers were very supportive and explained every concept clearly. Thanks to DataMites, my data science journey was smooth and successful.
My mentor, a DataMites alumnus, suggested I join the Data Science course, and it was one of my best decisions! The offline classes were interactive and full of guidance, and I got the chance to intern at Rubixe AI Solutions. The placement team was very supportive and helped me perfect my resume. Truly thankful to DataMites for this amazing learning journey.
I joined DataMites after completing my engineering in Computer Science and became a Certified Data Scientist. With their support, I completed an internship at Rubix AI Solutions and now work as an AI intern at Inventor Hub. DataMites made learning statistics and data science easy with clear guidance and great materials. I’m truly thankful for their support.
I joined DataMites for the Certified Data Scientist online course, and it was a great experience! The real-time projects helped me apply my learning practically, and the placement support added real value to my career restart after 8 years. DataMites is the perfect platform to learn, grow, and begin your data science journey.
I joined DataMites for the Data Science course and learned Python, SQL, Machine Learning, and Deep Learning through hands-on projects. The trainers are excellent at explaining concepts clearly. The placement team is very supportive and provides great opportunities thanks to DataMites for helping me start my data science journey.
I joined DataMites after my B.Tech when I developed an interest in Data Science and Machine Learning. The training was well-structured, and I even got an internship opportunity that helped me gain real experience. The placement team was super supportive and guided me through every step. Thanks to DataMites, I’m now placed at Horizon Broadband Pvt. Ltd.
I joined DataMites after completing my BCA, and it was the best decision for my data science career! I learned Python, SQL, Machine Learning, Deep Learning, and NLP, and worked on several capstone and client projects that gave me real-world experience. The trainers and team were always friendly and supportive, patiently clearing all my doubts. Thanks to DataMites, I’m confident and job-ready in data science
I started my data science journey with DataMites, and it’s been an amazing experience! The Certified Data Scientist Program helped me learn everything from scratch through hands-on projects and an internship. The placement team was incredibly supportive, always ready to clarify my doubts. Thanks to their guidance, I successfully got placed. Highly recommend DataMites to anyone starting their data science career
Enrolling at DataMites was one of the best decisions I made for my career. Coming from a freelance writing background with no prior technical experience, I was initially unsure about stepping into the field of data analytics. But DataMites made the transition smooth and achievable. I’d highly recommend them to anyone looking for a guided, industry-ready data analytics course. A true DataMites success story!
Eligibility for an artificial intelligence course in Toronto depends on the course level and curriculum. Beginner courses generally require basic computer skills, while advanced programmes may require knowledge of mathematics, statistics, programming, or a related academic subject.
An online AI course in Toronto is suitable for students, recent graduates, working professionals, entrepreneurs, and career changers interested in artificial intelligence. Beginners can start with introductory courses, while experienced learners can choose specialised training in machine learning, data science, or generative AI.
The duration of an online artificial intelligence course in Toronto depends on the course format and depth of study.
Introductory courses: A few hours to 8 weeks.
Intermediate courses: 2–6 months.
Professional certification programmes: 6–12 months or longer.
Short online AI courses may cost approximately CAD 50–500, while comprehensive professional programmes can range from CAD 1,000–8,000 or more. These are general estimates rather than official average fees, as costs vary according to duration, curriculum, practical projects, instructor support, and certification.
Completing an AI course can help learners develop skills relevant to several technology and business roles, depending on their previous experience and practical knowledge. Career options include AI developer, machine learning engineer, data scientist, AI solutions analyst, and generative AI specialist.
The average salary of an AI Engineer in Toronto is approximately CAD $110,000 per year in base pay, according to Glassdoor's 2026 salary data. Actual earnings vary depending on experience, technical skills, industry, and job responsibilities.
An online artificial intelligence course in Toronto can help you develop practical skills for working with AI applications and data-driven systems. Depending on the curriculum, these may include:
Python programming and data analysis.
Machine learning and statistical modelling.
Generative AI tools and prompt engineering.
AI project development, evaluation, and responsible use.
Yes, professionals can choose an online AI course with flexible schedules, recorded lessons, or self-paced modules to balance learning with their work commitments. Evening classes and weekend sessions may also be available for those who prefer structured learning.
No, prior programming knowledge is not always required for beginner-level AI courses that focus on fundamental concepts and practical applications. However, learning Python, basic mathematics, statistics, and data analysis can help you progress towards more technical AI roles.
Artificial intelligence uses computer systems to identify patterns, understand language, make predictions, and perform tasks that typically require human intelligence. It is commonly used in healthcare, banking, retail, manufacturing, transportation, and customer service for applications such as fraud detection, product recommendations, forecasting, and automated support.
Python is widely used for artificial intelligence because it supports machine learning, deep learning, and data analysis through extensive libraries. Other useful languages include R for statistical analysis, SQL for database management, Java and C++ for software systems, and JavaScript for AI-powered web applications.
Qualifications in computer science, software engineering, mathematics, statistics, and data science can provide a strong foundation for an AI career. Professional certificates, practical projects, and specialised training can also be valuable, while research-focused positions may require a master's degree or PhD.
AI-related roles in Toronto span software development, analytics, research, and business technology, although actual vacancies change over time. Common job titles include:
AI Engineer and Machine Learning Engineer.
Data Scientist and Data Analyst.
AI Application Developer and MLOps Engineer.
Generative AI Specialist and AI Solutions Consultant.
Toronto has an established technology sector, and Canada's continued investment in AI adoption supports opportunities across research, software development, data analytics, and business applications. However, hiring demand varies by occupation and experience level, so reviewing current vacancies and developing relevant technical skills are important when planning an AI career.
AI can be a promising career choice for people interested in programming, analytical thinking, data, and technology-driven problem-solving. Employment prospects depend on market demand, practical experience, technical specialisation, and the ability to adapt to evolving AI tools and workplace requirements.
Artificial intelligence is the broad field of creating computer systems that perform tasks associated with human intelligence. Machine learning is a branch of AI that enables systems to learn patterns from data, while deep learning uses multilayered neural networks to handle complex tasks such as speech recognition, image analysis, and language generation.
Generative AI helps businesses create text, images, code, summaries, and other content while supporting tasks such as customer service, marketing, document analysis, and software development. Toronto businesses can use these tools to improve workflows, but accuracy checks, data privacy, copyright considerations, and human oversight remain essential.
Building an AI career requires a combination of technical knowledge, practical experience, and communication skills. Focus on Python, mathematics, statistics, data analysis, machine learning, generative AI, cloud technologies, responsible AI practices, and hands-on projects that demonstrate your ability to solve real-world problems.
Our DataMites AI Engineer course in Toronto is suitable for learners who want to build skills in artificial intelligence, including students, graduates, and working professionals. A background in programming, mathematics, or statistics can be helpful, but we recommend contacting our team to confirm the specific eligibility requirements for your chosen course.
You can enrol in our DataMites Artificial Intelligence course in Toronto by contacting our team to discuss your learning goals, course options, and registration process. We will guide you through the available batches, fee details, and enrolment steps so you can choose an option that suits your schedule.
Our DataMites Artificial Intelligence course fee in Toronto ranges from CAD 2,990 to CAD 4,270, depending on the selected course option. Contact our team for a breakdown of the applicable fee, payment schedule, and inclusions before registering.
Our DataMites AI course in Toronto has a duration of 9 months, with 780+ learning hours to support structured learning and practical skill development. This learning schedule gives participants time to study AI concepts, work on projects, and strengthen their technical understanding.
Our DataMites AI training in Toronto includes course-related certification details that depend on the selected program and its completion requirements. NASSCOM FutureSkills Certification is available for eligible NRI learners; contact our team to confirm the certification options and eligibility criteria applicable to you.
Yes, our DataMites Artificial Intelligence course offers live online learning options for learners in Toronto, subject to batch availability. This format allows you to learn remotely, interact with instructors, and develop your AI skills without needing to attend sessions in person.
Our AI training in Toronto is designed for students, graduates, professionals, and career changers who want to develop knowledge of artificial intelligence and related technologies. If you are interested in AI applications and have the time to commit to structured learning, contact us to discuss the course that best fits your background and goals.
Yes, beginners can explore our DataMites Artificial Intelligence course, with the appropriate learning pathway depending on their existing technical knowledge. We recommend discussing your experience with our team so we can help you identify a suitable starting point and understand any recommended prerequisites.
We accept online debit card and credit card payments through Razorpay, and credit card EMI is available where applicable. A token advance is collected during registration, and the remaining course fee must be paid in full before course completion; our team can confirm the applicable payment schedule for your selected program.
Our course fee ranges from CAD 2,990 to CAD 4,270, depending on the selected program and its inclusions. The course may cover structured learning, instructor-led sessions, practical exercises, and project work, so we recommend confirming the exact curriculum, learning resources, and other inclusions with our DataMites team before enrolling.
You can contact our DataMites team to ask about demo or trial class availability for the Artificial Intelligence course in Toronto. We will help you confirm whether a session is available and explain how to access it before you make your enrolment decision.
If you miss a live online session during our DataMites AI training in Toronto, contact our team to check the available options for catching up on the material. Access to recordings, alternate batches, or make-up sessions depends on the applicable course arrangements, so please confirm these details before enrolling.
Refund eligibility depends on the applicable cancellation and refund policy for your DataMites Artificial Intelligence course in Toronto. Please contact our team to confirm the policy, any applicable conditions, and the refund process before making a cancellation request.
Our DataMites AI training is designed to help learners connect theoretical concepts with practical applications through relevant exercises and project-based learning, depending on the selected program. Contact us to confirm the specific projects, tools, and practical components included in your course.
Our DataMites Artificial Intelligence course may cover core AI concepts, machine learning, data analysis, Python programming, and related tools and technologies, depending on the selected curriculum. For an accurate list of modules, software, and project technologies, contact our team for the current syllabus for AI training in Toronto.
We offer online learning options for our DataMites AI training in Toronto, subject to the available course formats and batches. If you are looking for an online AI course in Toronto, contact our team to compare the available schedules, learning arrangements, and any applicable Flexi Pass options.
A Flexi Pass allows learners to attend multiple batches of the same course during its validity period and revisit topics whenever they need additional practice or revision. It remains valid for 3 months from activation, subject to the applicable course terms; contact DataMites to confirm availability and the conditions for your selected program.
The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -
The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.
No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.
Refer & Earn
+1-628-228-6062 
