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Intellipaat Artificial Intelligence course in Canada is an industry-designed course for learning TensorFlow, artificial neural network, perceptron in neural network, transfer learning in machine learning, backpropagation for training networks through hands-on projects and case studies. Get the best online artificial Intelligence Training in Canada from Artificial Intelligence certified experts. This is created in collaboration with IBM.
Intellipaat is providing comprehensive training on Artificial Intelligence in Canada. You will clearly learn supervised and unsupervised learning, TensorFlow for training Deep Learning model, neural networks and more. You will have a thorough hands-on experience after completing the course.
You don’t need to have any basic skillset to go forward in this course.
Uber, Microsoft among many others are keen on hiring talented professionals to their companies in Artificial Intelligence domain. As it is a growing field there has been an increase in the number of vacancies in the recent years to the tune of 500% in Canada. This trend is going to continue and will provide many talented professionals a chance to excel in their career.
As per Indeed.com Machine Learning engineers made up 61% of all the Artificial Intelligence jobs in 2016. Since June 2015, AI and Machine Learning jobs have grown by nearly 500%. As enormous amount of money invested in research this will create equally enormous amount of jobs in future.
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, which is the most important aspect of Artificial intelligence, is used from AI-powered recommender systems (Chatbots) and Search engines for online movie recommendations. Therefore, to remain relevant and gain expertise in this emerging technology, enroll in Intellipaat’s AI Course.
Here are a few reasons why Artificial Intelligence is a great career option:
This will help you build a solid AI career and get the best artificial intelligence engineer positions in leading organizations.
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1.1 Field of machine learning, its impact on the field of artificial intelligence
1.2 The benefits of machine learning w.r.t. Traditional methodologies
1.3 Deep learning introduction and how it is different from all other machine learning methods
1.4 Classification and regression in supervised learning
1.5 Clustering and association in unsupervised learning, algorithms that are used in these categories
1.6 Introduction to ai and neural networks
1.7 Machine learning concepts
1.8 Supervised learning with neural networks
1.9 Fundamentals of statistics, hypothesis testing, probability distributions, and hidden markov models.
2.1 Multi-layer network introduction, regularization, deep neural networks
2.2 Multi-layer perceptron
2.3 Overfitting and capacity
2.4 Neural network hyperparameters, logic gates
2.5 Different activation functions used in neural networks, including relu, softmax, sigmoid and hyperbolic functions
2.6 Back propagation, forward propagation, convergence, hyperparameters, and overfitting.
3.1 Various methods that are used to train artificial neural networks
3.2 Perceptron learning rule, gradient descent rule, tuning the learning rate, regularization techniques, optimization techniques
3.3 Stochastic process, vanishing gradients, transfer learning, regression techniques,
3.4 Lasso l1 and ridge l2, unsupervised pre-training, xavier initialization.
4.1 Understanding how deep learning works
4.2 Activation functions, illustrating perceptron, perceptron training
4.3 multi-layer perceptron, key parameters of perceptron;
4.4 Tensorflow introduction and its open-source software library that is used to design, create and train
4.5 Deep learning models followed by google’s tensor processing unit (tpu) programmable ai
4.6 Python libraries in tensorflow, code basics, variables, constants, placeholders
4.7 Graph visualization, use-case implementation, keras, and more.
5.1 Keras high-level neural network for working on top of tensorflow
5.2 Defining complex multi-output models
5.3 Composing models using keras
5.3 Sequential and functional composition, batch normalization
5.4 Deploying keras with tensorboard, and neural network training process customization.
6.1 Using tflearn api to implement neural networks
6.2 Defining and composing models, and deploying tensorboard
7.1 Mapping the human mind with deep neural networks (dnns)
7.2 Several building blocks of artificial neural networks (anns)
7.3 The architecture of dnn and its building blocks
7.4 Reinforcement learning in dnn concepts, various parameters, layers, and optimization algorithms in dnn, and activation functions.
8.1 What is a convolutional neural network?
8.2 Understanding the architecture and use-cases of cnn
8.3‘What is a pooling layer?’ how to visualize using cnn
8.4 How to fine-tune a convolutional neural network
8.5 What is transfer learning?
8.6 Understanding recurrent neural networks, kernel filter, feature maps, and pooling, and deploying convolutional neural networks in tensorflow.
9.1 Introduction to the rnn model
9.2 Use cases of rnn, modeling sequences
9.3 Rnns with back propagation
9.4 Long short-term memory (lstm)
9.5 Recursive neural tensor network theory, the basic rnn cell, unfolded rnn, dynamic rnn
9.6 Time-series predictions.
10.1 Gpu’s introduction, ‘how are they different from cpus?,’ the significance of gpus
10.2 Deep learning networks, forward pass and backward pass training techniques
10.3 Gpu constituent with simpler core and concurrent hardware.
11.1 Introduction rbm and autoencoders
11.2 Deploying rbm for deep neural networks, using rbm for collaborative filtering
11.3 Autoencoders features and applications of autoencoders.
12.1 Image processing
12.2 Natural language processing (nlp) – Speech recognition, and video analytics.
13.1 Automated conversation bots leveraging any of the following descriptive techniques:  Ibm watson, Microsoft’s luis, Open–closed domain bots,
13.2 Generative model, and the sequence to sequence model (lstm).
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Auto-Encoder Assignment
As part of this assignment, you have to implement an LSTM encoder. Create an input sequence of numbers. Build an LSTM RNN model on top of this data. Compile the model with ‘adam’ to be the optimizer and loss to be ‘mse’. Fit the model on data and set the number of epochs to be 300. Predict the values and verify it with the input data.
CNN Assignment
In this assignment, you have to build your convolutional Neural Network using MNIST dataset. For this, you will have to download the MNIST dataset through Keras. You will be asked to fit the dataset to a model and evaluate the loss and accuracy of the model. You will be working with pooling layers, dense layers, dropout layers, flatten layers, and NumPy.
Binary Classification on ‘Customer_Churn’ Using Keras
In this project, you will have to analyze the data of a telecom company to find insights and stop customers from churning out to other telecom companies. You will be working on data manipulation and visualization, and create 3 different models with the help of Keras.
Face Detection Project
For the project, you will be using Python 3.5(64-bit) with OpenCV for face detection. The system will have to be able to detect multiple faces in a single image. You will be working with essential libraries like cv2 and glob (glob helps in finding all the pathnames matching a specified pattern).
Keras Assignment
Build a sequential model using Keras on top of this Diabetes dataset to find out if a patient has diabetes or not. You will use Stochastic Gradient as the optimization algorithm. You will be required to build another sequential model where ‘Outcome’ is the dependent variable and all other columns are predictors.
MLP Assignment
You will be detecting wine fraud using Neural Networks as a part of this assignment. You will use the latest version of SciKit Learn (>0.18). Use the wine data set from the UCI Machine Learning Repository. Import the dataset, split the data, and use the predict () method to get predictions. You will have to train your model using Scikit Learn’s estimator objects.
AI and Deep Learning Intro Assignment
For this assignment, you will need to install Anaconda on your system with Python version 3.6 or above. Create a TensorFlow environment, download TensorFlow, and download Pandas, Numpy, SciKit-learn, SciPy, Matplotlib in both Anaconda and TensorFlow environment. You will also need to install Keras and TFLearn in the TensorFlow environment.
RNN Assignment
As part of the assignment, you will be using an airline-passenger dataset to predict the number of passengers for a particular month. Write a simple function to convert a single column of data into a two-column dataset. You will divide the data into train and test set.
TensorFlow Assignment
Through this assignment, you will learn to create a session in TensorFlow. You will define constants and perform computations using the session, print ‘Hello World’ using the same, and create a simple Linear Equation, y=mx+c in Tensorflow, where m and c are variables and x is a placeholder.
TFLearn Assignment
In this assignment, you will be required to find out the factors that lead up to a patient having cancer. You will need to load the dataset and print the number of samples and features in the data. Then, you will divide the data into train & and create a network.
The entire content of this AI course is developed by leading AI professionals to help you find the best artificial intelligence engineering job at the top MNCs. During the certification training, you will work on real-world projects that will help evaluate your skills and learning in real-time business scenarios, thus helping you accelerate your career effortlessly.
Upon the completion of this artificial intelligence online course, there will be quizzes that reflect the type of questions asked in the certification examination and will help you score better.
Intellipaat Course Completion Certification will be awarded on the completion of the project work (after the expert review) and upon scoring at least 60 percent marks in the quiz. Intellipaat certification is well recognized in top 80+ MNCs like Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, etc.
Gajraj Singh
Intellipaat offered fully updated learning material for the best Artificial Intelligence training. The video quality and other course materials were appreciable. I would definitely recommend Intellipaat's AI training to my friends.
Mary Jain
Artificial Intelligence trainers of Intellipaat simplified the topics of Artificial Intelligence, Machine Learning, and TensorFlow to help even an average learner grasp these toughest concepts. Thank you, Intellipaat!
Samar Jain
Intellipaat Artificial Intelligence training had great projects in building chatbots and other cool real-world applications. The entire certification training course was up to the mark. I found it one of the best AI courses online.
Our Artificial Intelligence online training involves the simultaneous participation of both learners and instructors in an online environment. Being 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 AI classes and equip you with them to further enhance your learning process. On the completion of this AI training online, your experience will be equivalent to that of a professional who has worked for 6 months in the industry.
At Intellipaat, you can enroll in either the instructor-led online training or self-paced training. Apart from this, Intellipaat also offers corporate training for organizations to upskill their workforce. All trainers at Intellipaat have 12+ years of relevant industry experience, and they have been actively working as consultants in the same domain, which has made them subject matter experts. Go through the sample videos to check the quality of our trainers.
Intellipaat is offering the 24/7 query resolution, and you can raise a ticket with the dedicated support team at anytime. You can avail of the 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 trainers.
You would be glad to know that you can contact Intellipaat support even after the completion of the training. We also do not put a limit on the number of tickets you can raise for query resolution and doubt clearance.
Intellipaat is offering you the most updated, relevant, and high-value real-world projects as part of the training program. This way, you can implement the learning that you have acquired in real-world industry setup. All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry-ready.
You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc. After completing the projects successfully, your skills will be equal to 6 months of rigorous industry experience.
Intellipaat actively provides placement assistance to all learners who have successfully completed the training. For this, we are exclusively tied-up with over 80 top MNCs from around the world. This way, you can be placed in outstanding organizations such as Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, and Cisco, among other equally great enterprises. We also help you with the job interview and résumé preparation as well.
You can definitely make the switch from self-paced training to online instructor-led training by simply paying the extra amount. You can join the very next batch, which will be duly notified to you.
Once you complete Intellipaat’s training program, working on real-world projects, quizzes, and assignments and scoring at least 60 percent marks in the qualifying exam, you will be awarded Intellipaat’s course completion certificate. This certificate is very well recognized in Intellipaat-affiliated organizations, including over 80 top MNCs from around the world and some of the Fortune 500companies.
Apparently, no. Our job assistance program 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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