IBM is currently identified as a leader in the 2021 Gartner Magic Quadrant for Cloud AI Developer Services Reports. Students of this Artificial Intelligence course in Toronto learn through an integrated blended learning journey developed as an IBM partnership, packed with the all right skills needed to become Artificial Intelligence and Data Science experts today. Students are expected to be very much work-ready, and geared up for today's Artificial Intelligence and Data Science job responsibilities on completing this cutting-edge Artificial Intelligence course in Toronto, offered as an IBM-partnered program. IBM, located in Armonk, New York, is a major cognitive solutions and cloud platform firm that provides a wide range of technology and consulting services. IBM has been involved in research since its inception. Each year, IBM invests several billion dollars in research and development.
What Can I Expect From This Simplilearn Program Developed In Collaboration With IBM?
You will obtain credentials from IBM and Simplilearn in the Artificial Intelligence course in Toronto on completing the Master's program. These certificates will attest to your abilities as an Artificial Intelligence expert. In addition, you will receive the following upon completion of this artificial intelligence course in Toronto.
Many other benefits you can get from artificial Intelligence Course In Toronto
The Simplilearn and IBM partnered Artificial Intelligence Engineer Master's Program included key topics comprising Artificial Intelligence, Data Science, Machine Learning, and Deep Learning to get learners to grasp the application of advanced models or tools in the real-world. The curriculum taught in Simplilearn's Artificial Intelligence course in Toronto is set up to provide a 360 degree mastery of AI topics, which includes key statistical fundamentals used in Data Science, ML, and Python programming.
In this AI course in Toronto, you will learn:
You will be able to do the following by the end of the Artificial Intelligence Course In Toronto :
Over 15 real-life branded projects in various domains are included in this Artificial Intelligence course in Toronto. These projects offer you the opportunity to apply what you have learned in supervised/unsupervised learning, SVMs, convolutional neural networks, deep learning, TensorFlow, reinforcement learning, TensorFlow, neural, and recurrent neural networks, and many others.
This Artificial Intelligence Course In Toronto includes a capstone project that allows you to review the topics you learned throughout the program. You'll take dedicated, supervised classes to build a high-quality industry project that addresses a real-world problem. From exploratory data analysis to model design and fitting, the capstone project will cover all the bases.
To complete the capstone project, you will use cutting-edge Artificial Intelligence-based supervised and unsupervised algorithms in the domain of your choice. You will have to apply several models, including Regression, Multinomial Naive Bayes, SVM, Tree-based algorithms, and NLP. You will have a certificate attesting to your capstone credential on finishing project successfully, and also have the opportunity to showcase cutting-edge project work to employers to validate your expertise.
Project 1: Uber Fare Forecasting | Domain: Delivery (Commerce)
Uber, one of the major taxi companies in the United States, seeks to increase the accuracy of fare predictions for any trip. Assist Uber by creating and selecting the best model.
Project 2: Mercedes-Benz test bench time reduction | Domain: Automobile
Mercedes-Benz, a worldwide German automaker, aims to cut down on the amount of time it spends on the test bench for any given vehicle. Faster testing will shorten the time it takes to get a product to market. To achieve the stated goal, build and optimize the algorithm using dimensionality reduction and various techniques such as XGBoost.
Project 3: Predicting Amazon product ratings | Domain: E-commerce
One of the top US-based e-commerce companies, Amazon, recommends products within the same category to customers based on their activity and reviews on similar products. Amazon hopes to improve its recommendation engine by projecting non-rated product ratings and including them in suggestions based on those projections.
Project 4: Walmart Demand Forecasting | Domain: Sales
Predict correct sales for 45 Walmart shops, one of the largest US-based retailers, taking into account the influence of promotional markdown events. Examine whether macroeconomic factors such as the CPI, unemployment rate, and so on have an impact on sales.
Project 5: Improving Comcast's customer experience | Domain: Telecom
Comcast, a mega US corp in the global telecommunications industry, intends to identify and resolve issue areas and significantly improve its customer happiness numbers. The organization is also searching for critical tips that may be put into action to provide the best possible customer service.
Project 6: IBM Attrition Analysis | Domain: Workforce Analytics
IBM, one of the largest IT businesses in the United States, wants to know what variables impact employee churn. The organization, in addition wants to develop a statistical model (logistical regression based) on the factors mentioned inorder to analytically determine if and when employee churn can occur.
Project 7: Analysis of Toronto 311 Service Requests |Domain: Telecommunications
Analyze the data from New York City 311 calls for service requests. You'll concentrate on data wrangling techniques to decipher data trends and show the most common complaint kinds.
Project 8: Analysis of MovieLens Datasets | Domain: Engineering
The GroupLens Research Project is a research group at the University of Minnesota's Computer Science and Engineering Department. This group's researchers are working on many initiatives in the areas of collaborative filtering, information filtering, and recommender systems. For user datasets, we ask you to do an analysis utilizing the Exploratory Data Analysis technique.
Project 9: Stock Market Data Analysis | Domain: Stock Market Data Analysis
You will import data from the companies Yahoo, Apple, Amazon, Microsoft, and Google as part of this project using the Yahoo data reader. You'll need to leverage analytics (fundamental) and do closing price, and stock trades volume plotting, do return analysis (daily), and find out correlation among the stocks by leveraging pair plots.
This Artificial Intelligence Course In Toronto is appropriate for a variety of positions and specialties, including:
The following are the requirements for enrolling in an Artificial Intelligence Course In Toronto:
Delve into AI basics and generative AI principles. Grasp the importance of explainable AI. Employ prompt engineering to enhance generative AI performance. Understand ChatGPT's mechanisms, features, and constraints. Explore varied ChatGPT applications. Gain foresight into generative AI's future and challenges.
Discover procedural and object-oriented programming. Uncover Python's benefits. Set up Python and its IDE. Master Jupyter Notebook. Apply Python basics like identifiers, indentation, and comments. Understand data types, operators, and string functions. Explore Python loops and variable scopes. Learn about OOP, its features, and elements like methods, attributes, and access modifiers.
Kickstart your learning of Python for Data Science with this Data Scientist course and familiarize yourself with programming, tastefully crafted by IBM.
* Gain a comprehensive overview of Python's tools and techniques for data analytics
* Experience vital skill development in Python for various data science roles
* Engage in a blended learning approach covering data wrangling, mathematical computing, and more
* Explore practical applications for a hands-on understanding
* Propel your data science career with Simplilearn's specialized training
* Gain career success with our comprehensive Machine Learning course
* Learn from 40+ hrs of Applied Learning and interactive labs
* Complete 4 hands-on projects to solidify your understanding
* Receive mentoring support throughout your learning journey
* Master essential machine learning concepts for certification
* Gain the skills needed to become a successful machine learning engineer
Differentiate deep learning from machine learning. Explore various neural network types. Excel at forward and backward propagation in deep neural networks. Introduce modeling and performance enhancement in deep learning. Understand hyperparameter tuning and model interpretability. Learn dropout and early stopping implementation. Master CNNs, object detection, and RNN fundamentals. Grasp PyTorch basics and neural network creation.
The capstone project allows you to implement the skills you learned throughout this bootcamp. You will solve industry-specific challenges by leveraging various AI and ML techniques. The capstone project will help you showcase your expertise to employers.
Our Masters in Artificial Intelligence is exhaustive and this certificate is proof that you have taken a big leap in mastering the domain.
The knowledge and AI skills you've gained working on projects, simulations, and case studies will set you ahead of the competition.
Talk about your AI masters certificate on LinkedIn, Twitter, and Facebook, boost your resume, or frame it - tell your friends and colleagues about it.
The Artificial Intelligence course in Toronto will give you an insight into Artificial Intelligence tools and methodologies to prepare you for success in your role as an Artificial Intelligence Engineer. The industry-recognized certification from IBM and Simplilearn will attest to your new skills and on-the-job expertise. The program will train you on Python, Machine Learning techniques, including data reprocessing, regression, clustering, and Deep Learning methodologies and its applications using TensorFlow.
As a part of this AI online course in Toronto co-developed with IBM, you will receive the following:
You will be entitled to acquire the Masters in AI certificate, which will attest to your AI engineer abilities, provided you meet the following minimum requirements.
Course | Course completion certificate | Criteria |
Introduction to Artificial Intelligence Course |
Required | 85% of Online Self-paced completion and Pass Assessment test at 80% |
Data Science with Python | Required | 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, a score above 75% in course-end assessment, and successful evaluation in at least 1 project |
Machine Learning | Required | 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom and successful evaluation in at least 1 project |
Deep Learning with Keras and TensorFlow |
Required | Attend 1 Live Virtual Classroom and successful evaluation in at least 1 project and score 70% for course-end assessment. |
Advanced Deep Learning and Computer Vision | Required | Attend 1 LVC batch, Pass a Project, Pass an Assessment test 70% |
AI Capstone Project | Required | Attendance of 1 Live Virtual Classroom and successful completion of the capstone project |
The courses for which you will receive IBM credentials are as follows:
You can enroll in this AI training in Toronto on our website and make an online payment using any of the following options:
Once payment is received you will automatically receive a payment receipt and access information via email.
Math principles such as statistics, probability, linear algebra, calculus, and Bayesian algorithms should be understood by professionals who want to start AI careers. Statistics, learning theory, problem-solving, classical mechanics, and language processing are all skills they'll need. It is also suggested that you know at least one programming language, data structure, and logic.