Artificial Intelligence Training Content

 Artificial Intelligence (AI) has a long history but is still properly and actively growing and changing. In this course, youâ??ll learn the basics of modern AI as well as some of the representative applications of AI such as Data Science, Machine Learning, Deep Learning, Statistics, Artificial Neural Networks, Restricted Boltzmann Machine (RBM) and Tensorflow with Python. Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination. This Artificial Intelligence course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is going to apply.
The main goal of this course is to familiarize you with all aspects of AI so that you can start your career as an artificial intelligence engineer. A few of the many topics/modules that you will learn in the program are:
A.	Basics of Deep Learning techniques
B.	Understanding artificial neural networks
C.	Training a neural network using the training data
D.	Convolutional neural networks and its applications
E.	TensorFlow and Tensor processing units
F.	Supervised and unsupervised learning methods
G.	Machine Learning using Python
H.	Applications of Deep Learning in image recognition, NLP, etc.
I.	Real-world projects in recommender systems, etc.
J.	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 Intellipaatâ??s AI Course.
K.	This will help you build a solid AI career and get the best artificial intelligence engineer positions in leading organizations.
Introduction to Deep Learning & AI
Deep Learning: A revolution in Artificial Intelligence
o	Limitations of Machine Learning
What is Deep Learning?
1.	Need for Data Scientists
2.	Foundation of Data Science
3.	What is Business Intelligence
4.	What is Data Analysis
5.	What is Data Mining
What is Machine Learning? Analytics vs Data Science
1.	Value Chain
2.	Types of Analytics
3.	Lifecycle Probability
4.	Analytics Project Lifecycle
5.	Advantage of Deep Learning over Machine learning
6.	Reasons for Deep Learning
7.	Real-Life use cases of Deep Learning
8.	Review of Machine Learning

Data
1.	Basis of Data Categorization
2.	Types of Data
3.	Data Collection Types
4.	Forms of Data & Sources
5.	Data Quality & Changes
6.	Data Quality Issues
7.	Data Quality Story
8.	What is Data Architecture
9.	Components of Data Architecture
10.	OLTP vs OLAP
11.	How is Data Stored?

Key Features

166 Hours of Learning

online | Classroom | Corporate
Learning.

Job Placement Assistance

Based on performance of candidate in training batch, will be placed in our company projects.

2+ Practical Hands-On Projects

After course completion sample projects will be shared by trainers.

Flexible Schedules

Weekdays, Weekend, Classroom batches only in hyderabad location are available.

Support

Job support will be provided until student will work independently. Can be added in technical what sapp groups.

Modes of Training

CLASS ROOM TRAINING
  • Links / Blog link will be shared
  • Real time scenarios/ use cases will be covered in training
ONLINE TRAINING
  • Daily assignment will be given
  • Daily session recording videos access will be given for 90 days
CORPORATE TRAINING
  • Experienced corporate faculty is available
  • Interview questions, material will be provided in training