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ARTIFICIAL INTELLIGENCE

Accelerate your career prospects with iteanz Artificial Intelligence Training

About Program

Highest Quality and the Most Productive, High Energy Learning Experience.

Curriculum

Comprehensive Course Curriculum. Customizable to full-fill your trainer needs.

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About Program

Learn AI from iteanz the market leader !

Combine the power of Python, Machine Learning, Deep Learning, and Artificial Intelligence for Real-World AI applications!

Achieving an Artificial Intelligence certification exemplifies your expertise, and commitment and can advance your career. AI is the most trending IT Technology today and choosing an AI career path is the right choice one can make.

Iteanz Artificial Intelligence training adds value and Accelerates your career. Enroll Today!

This course will start with a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems and will take you to the expert level.

You will learn about Python Programming, Machine Learning, and Artificial Intelligence as part of this course.

Hands-on experience will be gained and the Artificial Intelligence training is delivered by experienced AI Developers.

Success Factors:

  • High-Quality Training
  • Top Technical Trainers
  • Comprehensive Course Curriculum
  • 100% Placement Assistance
  • Superb Satisfaction Score
  • Internship on Real-Time Project 
  • 3I- Individual Focus, Innovative & Interactive

Curriculum

CHAPTER 1:INTRODUCTION

  • In this course, you will learn to implement mathematical ideas in machine learning. You will investigate the process of learning and understand the application of various learning algorithms.
  • Prerequisites:
    The course assignments and notes will use python programming language and expects a basic knowledge of python. We assume the student has completed the Machine Learning Foundations or has an equivalent fluency in mathematics and fundamentals.

CHAPTER 2: LINEAR MODELS

  • Understand linear approximation and modeling of problems and develop linear models

CHAPTER 3: DIMENSIONALITY REDUCTION

  • Use ideas from linear algebra to transform dimensions and warp space providing additional flexibility and functionality to linear models.

CHAPTER 4:SVM

  • Develop and implement kernel-based methods to develop nonlinear models to solve a few complex tasks.

CHAPTER 5:NEAREST NEIGHBOURS, K-MEANS, AND GAUSSIAN MIXTURE MODELS

  • Review pattern recognition ideas with distance and cluster-based models to understand similarity measures and grouping criteria.

CHAPTER 6:NAIVE BAYES AND DECISION TREES

  • Dive into applications of the Bayes theorem and the use of decision criteria when learning from data.

CHAPTER 7:SEARCH

  • Look at the search from the perspective of graphs, trees, and heuristic-based optimizations.

CHAPTER 8:LOGIC AND PLANNING

  • Discover ways to encode logic and develop agents that plan actions in an environment.

CHAPTER 9:REINFORCEMENT LEARNING AND HIDDEN MARKOV MODELS

  • Engineering agents learn from a sequence of actions using rewards and penalties.

CHAPTER 10:Q-LEARNING AND POLICY GRADIENT

  • Operate in a stateful world over value and policy approximations tasks

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It’s not just about training. It’s about career progress. Iteanz allows you to take control of your career by learning the right skills at the right time.

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