Artificial Intelligence Certification Course

Artificial intelligence Course is the best choice for career, It is winding up progressively in the modern world where everything is driven by information and automation. Artificial intelligence will impact all portions of day by day life in future, Becoming an Artificial Intelligence Engineer puts you on the way to an exciting, developing profession that is anticipated to develop forcefully into 2025 and past. In our Artificial Intelligence Training, you’ll learn the basics of modern AI technologies as well as some of the present-day applications of AI.

Key Features

  Instructor-led Sessions
  Lifetime Access
  Assignments
  Real-life Case Studies
  24 x 7 Expert Support
  Hands on Projects
  Certification

Instructor Led Online Training

 1312


CST Timing

Start Date: 28 Sep, 2019

Course Duration: 60 Hours

Time: 08:30 AM 11:30 AM

Review:

Instructor Led Classroom Training

 1312


CST Timing

Start Date: 28 Sep, 2019

Course Duration: 60 Hours

Time: 08:30 AM 11:30 AM

Review:

Course Curriculum

  • 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

Description:

This section of the course provides a sensitization as well as an in-depth explanation on what is Machine Learning and Artificial Intelligence. Given Harvard’s latest publication the hottest job of the 21st century is “Data Scientist”. But what exactly is a data scientist? What is the job role? What concepts do you need to know to become a successful data scientist? What projects do you undertake as a data scientist and what is the methodology? What business use cases do you solve or create using data science? This section helps you understand answers to questions like these and more specifically helps you understand what do all the jargons listed actually mean – because once you start to search information about data science you come across all such jargons. There is so much information on the web that it is very easy to get confused. So in a nutshell, this section helps you understand and take a grasp of the 3 fundamental questions of Data Science – What is it? Why is it required? and How is it done?

  • 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)

Description:

Machine Learning Certification Training using R helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, SVMs & Naïve Bayes. This Machine Learning Training exposes you to concepts of Statistics, Time Series and different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. Throughout the Data Science Certification Course, you’ll be given hands on practices on various projects and datasets. Moreover, you are given the freedom to bring in your projects/datasets and the trainer will assist you in that too! You will also get to know what situation/business problem requires which algorithms to be used. All the details of an algorithm such as the hyperparameters, smoothing, regularization, cross validation, confusion matrix, optimization of your algorithms to achieve better outputs or increased accuracy are all a part of this section of the course.

  • AI Theory – What is AI? How is it different from ML?

Description:

“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.

Topics:

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

  • History
  • Fundamentals
  • Neural Networks – Working and Calculations

Description:

“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.

Topics:

How Deep Learning Works?
Activation Functions
Illustrate Perceptron
Training a Perceptron
Important Parameters of Perceptron
What is TensorFlow?
TensorFlow code-basics
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

Description:

“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.

Topics:

Introduction to RNN Model
Application use cases of RNN
Modelling sequences
Training RNNs with Backpropagation
Long Short-Term memory (LSTM)
Recursive Neural Tensor Network Theory
Recurrent Neural Network Model”

  • Convolution Neural Networks

Description:

“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.

Topics:

Introduction to CNNs
CNNs Application
Architecture of a CNN
Convolution and Pooling layers in a CNN
Understanding and Visualizing a CNN”

  • Auto-encoders and Restricted Boltzmann Machines

Description:

“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.

Topics:

Restricted Boltzmann Machine
Applications of RBM
Collaborative Filtering with RBM
Introduction to Autoencoders
Autoencoders applications
Understanding Autoencoders”

  • Tensorboard for Visualization

Description:

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

  • 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
  • Description:

    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
  • Description:

    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.

Course Description

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.

This program is designed for all those who are interested in learning Artificial Intelligence and machine learning techniques in big data domain and write intelligent applications.

No Prerequisites to take Artificial Intelligence Training with SFJ Business Solutions.

  • 10 Projects on Artificial Intelligence (one of them is Self Driving Car !!)
  • 10 Projects on Machine Learning.

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