- Supervised learning with neural networks
- Machine learning concepts
- Clustering and association in unsupervised learning, algorithms that are used in these categories
- Introduction to ai and neural networks
- Classification and regression in supervised learning
- The benefits of machine learning with respect to Traditional methodologies
- Deep learning introduction and how it is different from all other machine learning methods
- Fundamentals of statistics, hypothesis testing, probability distributions, and hidden Markov models.
Medium of instructions English
Mode of learning Self study, Classroom
Mode of Delivery Video and Text Based
The purpose of this artificial learning course and training program is to enable the candidate to be able to design and curate modern applications and tools using the idea of artificial intelligence. As of now, the nations are advancing into being digitized, thus making artificial intelligence to be even more important.
The syllabus to Artificial Learning Course and Training has been structured in a very precise manner. All the important points are considered in the syllabus. Topics such as Neural Networks, Keras API, Deep learning, Dnns, Rnns, Cnns and similar many such topics will be covered enabling the candidate to expertise in this field. This is a self-paced program. The candidates will take self-initiative and detailed knowledge of the different topics covered in this course. Candidates need to meet all the evaluation criteria.
- Online program
- Self-Paced 24 hours videos
- Instructor-led Training for 44 hours
- 24x7 Lifetime Access and Support
- 48 hours of project works
- Lifetime Free Upgrade
- Certification by Intellipaat
- Faculty assistance
- Self paced learning
- Project work
- 1:1 doubt resolution sessions
Course and certificate fees
Fee details of the Artificial Intelligence Course and training
Amount (without tax)
Online training (Exclusive of taxes)
certificate providing authority
Who it is for
The programme is ideal for professionals who are:
- Software professionals
- Professionals in Data Science, Analytics, Search engine, e-commerce, etc
- Fresh Graduates
Certification Qualifying Details:
This course consists of 13 main topics which will be completed by the candidate at his own ease. The candidate has to score 60% in the quizzes that will be held after the completion of this course along with the projects. After passing all the criteria, the candidate will be given a certificate of completion.
What you will learn
After the completion of the Artificial Learning course and training program, the candidate will be able to-
- Understand the core of Artificial Learning
- Explain the difference between deep learning and other machine learning methods
- Decipher the machine learning concepts
- Have a detailed understanding of multi-layered neural networks
- Appreciate the various activation functions in neural networks
- Realize how deep learning works
- Know about TensorFlow
- Deploy Keras with Tensorboard
- Define complex multi-output models
- Acknowledge GPU in Deep Learning
- Learn more about Chatbots
- Commiserate with Deep learning applications
- Deep understanding of CNN and RNN
- Understand rbm and autoencoders
Filling the form
To apply for an Artificial intelligence Course and training, simply follow the steps below
Step 1: Kindly click on the official https://intellipaat.com/artificial-intelligence-deep-learning-course-with-tensorflow/ website of the portal.
Step 2: You will see that there is Enrol Now Tab. Just click it and the fees section will appear.
Step 3: The mode that best suits you can be selected namely self-paced training and online classroom. Then proceed to checkout.
Step 4: A page of ‘Order Summary’ will appear. Log int from your Google ID or Facebook ID. Make the payment
Step 5: You will then be able to browse the programme.
Introduction to Deep Learning and Neural networks
- Defining complex multi-output models
- Keras high-level neural network for working on top of TensorFlow
- Composing models using Keras
- Sequential and functional composition, batch normalization
- Deploying Keras with tensorboard, and neural network training process customization.
Autoencoders and restricted Boltzmann machine
- Introduction rbm and autoencoders
- Deploying rbm for deep neural networks, using rbm for collaborative filtering
- Autoencoder's features and applications of autoencoders.
Multi-layered Neural Networks
- Multi-layer perceptron
- Overfitting and capacity
- Neural network hyperparameters, logic gates
- Back propagation, forward propagation, convergence, hyperparameters, and overfitting.
- Multi-layer network introduction, regularization, deep neural networks
- Different activation functions used in neural networks, including relu, softmax, sigmoid and hyperbolic functions
Deep Learning Libraries
- Tensorflow introduction and it's open-source software library that is used to design, create and train
- Graph visualization, use-case implementation, Keras, and more.
- Understanding how deep learning works
- Python libraries in TensorFlow, code basics, variables, constants, placeholders
- Activation functions, illustrating perceptron, perceptron training
- multi-layer perceptron, key parameters of perceptron;
- Deep learning models followed by google’s tensor processing unit (tpu) programmable AI.
TFL earn API for TensorFlow
- Defining and composing models, and deploying tensorboard
- Using TfL earn API to implement neural networks
- Recursive neural tensor network theory, the basic rnn cell, unfolded rnn, dynamic rnn
- Introduction to the rnn model
- Long short-term memory (lstm)
- Time-series predictions.
- Use cases of rnn, modeling sequences
- Rnns with backpropagation
- Mapping the human mind with deep neural networks (dnns)
- Several building blocks of artificial neural networks (anns)
- Reinforcement learning in dnn concepts, various parameters, layers, and optimization algorithms in dnn, and activation functions.
- The architecture of dnn and its building blocks
- Understanding recurrent neural networks, kernel filter, feature maps, and pooling, and deploying convolutional neural networks in tensorflow.
- ‘What is a pooling layer?’ how to visualize using cnn
- How to fine-tune a convolutional neural network
- What is a convolutional neural network?
- Understanding the architecture and use-cases of cnn
- What is transfer learning?
Gpu in deep learning
- Deep learning networks, forward pass and backward pass training techniques
- Gpu constituent with simpler core and concurrent hardware.
- GPU's introduction, ‘how are they different from CPUs?’ the significance of GPUs
- Automated conversation bots leveraging any of the following descriptive techniques: Ibm Watson, Microsoft’s Luis, Open–closed domain bots
- Generative model, and the sequence to sequence model (lstm)
Deep learning applications
- Natural language processing (NLP) – Speech recognition, and video analytics.
- Image processing
Artificial Neural networks and Various methods
- Lasso l1 and ridge l2, unsupervised pre-training, Xavier initialization
- Various methods that are used to train artificial neural networks
- Stochastic process, vanishing gradients, transfer learning, regression techniques,
- Perceptron learning rule, gradient descent rule, tuning the learning rate, regularization techniques, optimization techniques
How it helps
This course’s main agenda is to help learners with ease in the field of artificial intelligence. One who enrolls in this program will have great results on the professional front.
After the completion of this program, certificates will be awarded which will be recognized by top companies like Cisco, Sony, Cognizant, TCS, Mu Sigma, Hexaware and many more.
There are many industries and companies that consider this certificate offered by Intellipaat. They include- Cognizant, TCS, Standard Chartered, Genpact, Cisco, Ericsson, Sony, Saint-Gobain, Hexaware and many others.
Basically, there will be three projects that will be dealt with in this program. First is Image recognition with TensorFlow, Second will be Building an Ai-based chatbot and lastly e-commerce product recommendation.
Skills such as Keras, TensorFlow, Neural Networks, Deep Learning, Kernel, GPU, Deep Neural Networks, Chatbots, Autoencoders, Time-series predictions, RBM, LSTM and such others will be covered in this program.
This is a self-paced program. The candidate will not be bound to any rigid timings. They will learn at their own ease taking a suitable time for themselves.
There is no such previous knowledge required to enroll in this program. Anyone who is interested in becoming a machine learning engineer or artificial engineer may take up this course online.
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