ABOUT THE COURSE
Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry. It is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.Artificial intelligence must have access to objects, categories, properties and relations between all of them to implement knowledge engineering. Initiating common sense, reasoning and problem-solving power in machines is a difficult and tedious task.
Machine learning is also a core part of AI. Learning without any kind of supervision requires an ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions. Root2learn Solutions provides Artificial Intelligence Course with Machine Learning and Deep Learning curated by real-time experts as per the current market features and requirements. Become master in AI Certification program, Deep divine into Machine Learning and deep learning, Recurrent Neural Networks and LSTMs, convolutional neural networks etc. Our AI Course designed completely with real-time scenarios, live projects & lab practices.
Why learn AI?
By doing artificial intelligence course it opens the world of opportunities. At a basic level, you’ll better understand the systems and devices that you interact with on a day by day basis. In the field of artificial intelligence, the outcomes are genuinely huge. Also, if you stay with the subject and concentrate more, you can help make front line AI applications, similar to the Google Self Driving Car.
Artificial intelligence (AI) is a study field that examines how to achieve intelligent human behaviors on a computer. An ultimate objective of AI is to make a PC that can learn, plan, and take care of issues independently. In spite of the fact that AI has been thought for many years, we can’t make a PC that is as clever as a human in all perspectives. Still, we do have several successful applications. In some cases, the computer implemented with AI technology can be even more clever than us. The Deep Blue system which won against the world chess champion is a great example.
Machine Learning and AI Sensitization
Dealing with Jargons – Data, Statistical Modeling, Data Mining, Machine Learning, NLP, Artificial Intelligence, Analytics (Predictive & Prescriptive), Deep Learning, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Q Learning
- What is ML?
- Why is it required?
- Examples / Videos
- Use Cases
- Tools Used
- R vs Python – Which is better?
- TensorFlow vs Keras vs TFLearn
Deep Diving into Machine Learning
- ML Theory
- Statistical Concepts
- Algorithms (Theory and Practical for each) – Regression (SLR, MLR, Logistic), Naïve Bayes, SVM, Customer Choice Modeling, Association Rule Mining, Clustering (K-means, Hierarchical)
Deep Diving into Artificial Intelligence
- AI Theory – What is AI? How is it different from ML?
“In this module, you’ll get an introduction to Deep Learning and understand how Deep Learning solves problems which Machine Learning cannot. Understand fundamentals of Machine Learning and relevant topics of Linear Algebra and Statistics.
Deep Learning: A revolution in Artificial Intelligence
Limitations of Machine Learning
What is Deep Learning?
Advantage of Deep Learning over Machine learning
Top Reasons to go for Deep Learning
Real-Life use cases of Deep Learning
Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization
- Neural Networks – Working and Calculations
“In this module, you’ll get an introduction to Neural Networks and understand it’s working i.e. how it is trained, what are the various parameters considered for its training and the activation functions that are applied.
How Deep Learning Works?
Training a Perceptron
Important Parameters of Perceptron
What is TensorFlow?
Constants, Placeholders, Variables
Creating a Model Understand limitations of a Single Perceptron
Understand Neural Networks in Detail
Illustrate Multi-Layer Perceptron
Backpropagation – Learning Algorithm
Understand Backpropagation – Using Neural Network Example
MLP Digit-Classifier using TensorFlow Why Deep Networks
Why Deep Networks give better accuracy?
Use-Case Implementation on SONAR dataset
Understand How Deep Network Works?
How Backpropagation Works?
Illustrate Forward pass, Backward pass
Different variants of Gradient Descent
Types of Deep Networks”
- Recurrent Neural Networks and LSTMs
“In this module, you’ll understand Recurrent Neural Networks and its applications. You will understand the working of RNN, how LSTM are used in RNN, what is Recursive Neural Tensor Network Theory, and finally you will learn to create a RNN model.
Introduction to RNN Model
Application use cases of RNN
Training RNNs with Backpropagation
Long Short-Term memory (LSTM)
Recursive Neural Tensor Network Theory
Recurrent Neural Network Model”
- Convolution Neural Networks
“In this module, you’ll understand convolutional neural networks and its applications. You will learn the working of CNN, and create a CNN model to solve a problem.
Introduction to CNNs
Architecture of a CNN
Convolution and Pooling layers in a CNN
Understanding and Visualizing a CNN”
- Auto-encoders and Restricted Boltzmann Machines
“In this module, you’ll understand RBM & Autoencoders along with their applications. You will understand the working of RBM & Autoencoders, illustrate Collaborative Filtering using RBM and understand what are Deep Belief Networks.
Restricted Boltzmann Machine
Applications of RBM
Collaborative Filtering with RBM
Introduction to Autoencoders
- Tensorboard for Visualization
In this module, you will learn how to use Tensorboard for visualization of the computational data graphs that TensorFlow constructs for any deep learning project
Industry Expectations and Tips
- How to approach a ML / AI problem?
- What is Data Science?
- Indicators of an Excellent Data Scientist?
- Interview Tips and Practice – What to say and what NOT to say?
- Managing salary expectations
- Undernourishment and Over-delivery
Self Explanatory. Tips on cracking interviews, career changes, salary expectations, job roles, different designations, various companies hiring etc.
- Sentiment Analysis
- Building Chatbot
- Image Classification
- Handwritten Digits Identification
- Speech to Text Engine
- Training a Gamebot
- Building a Recommendation Engine
- Creating music with Unsupervised Learning
- Generating Text in your own writing style
- Object Detection in live video feed, webcam, video files or Youtube video
These are the hands on projects focusing on the top 10 most sought after skills required by the companies in the market and the top 10 use cases that the industry is working on to resolve. Understanding the codes for these use cases and understanding the implementation of these will help you gain an edge in the industry as well as help you customize any project that you may undertake in the AI domain.
20 days (60 hours) instructor-led Live Training
60 Hrs AI Training Certificate
Training by Highly Experienced and Certified Trainer
The fewer ratio of Trainer and Participants
Practice after each Chapter
Interactive classroom training
Support after Training for Live Projects
Who can attend:
- Software Developers and Architects
- Analytics Professionals
- Data Management Professionals
- Business Intelligence Professionals
- Project Managers
- Aspiring Data Scientists
- Graduates looking to build a career in AI
- Anyone interested in AI
- Associate Project Managers
- Project Managers
- IT Project Managers
- Project Coordinators
- Project Analysts
- Project Leaders
- Senior Project Managers
- Team Leaders
- Product Managers
- Program Managers
- Project Sponsors
- Software Developers
- Project Team Members seeking AI.
How do you provide online training ?
The training would be provided over a web platform. It is the most demanded & modernized way of “Instructor Led Training” without the need for expensive traveling that can be attended from anywhere in the world. You can attend from your home. You will get the same class recorded video, which helps to revise the topics multiple time.
Which option do I choose for training, Virtual or classroom training?
You can decide which one suitable for you:
|Recorded video of the same session to refer in future
|No, recorded video
|Can attain from any place, internet ( 512 KBPS speed) and System required
|Need to go to the training venue
|Can attain from home or office or from another country
|No, have to stay in the same city
|Interaction with global professionals
|Mostly local professionals
|The flexible class pass can attain as many classes want in the same fee
|If miss any class can go through the same training video to connect in the next session, and ask if have any query or can attain in any batch
|If miss the class, will not able to attain the same session
|Gradually learning ( as training will go near about one month, so you can prepare with training) will get enough time to revise covered topics
|Some training will be finished in 4 days, or within one week. So it will be more load and will not have enough time to revise covered topics
|Highly expected trainer
|Maybe have experienced trainer
|Demo session ( past recorded video)
What is Virtual classroom training?
Virtual classroom training for Big data and Hadoop is training conducted via online live streaming of a class. The classes are conducted by a Certified trainer with more than 20 years of work and training experience. It is interactive session, you can asked the question to trainer and will also ask the question. it is one to one interaction. It is video conference type of training.
Is this live training, or will I watch pre-recorded videos?
All the classes are live. They are interactive sessions that enable you to ask questions and participate in discussions during class time. We do, however, provide recordings of each session you attend for your future reference.
What tools do I need to attend the training sessions?
- Windows: any version newer than Windows XP SP3
- Mac: any version newer than OSX 10.6
- Internet speed: Preferably faster than 1 MBPS
- Headset, speakers, microphone: You’ll need headphones or speakers to hear clearly, as well as a microphone to talk to the others. You can use a headset with a built-in microphone, or separate speakers and microphone.
Where is the training held?
There is no training venue for Virtual classroom training. It is online live training you can attend from your home by login at your system, for that we will provide you login id and password.
For classroom training, you will get an email at your registered email id as per your location.
What is 100% training quality guarentee?
If you are not happy with our training quality, inform us within the 1st half of Training on First Day. We will refund your entire training fee with 7 working days.