Artificial Intelligence training

Artificial Intelligence Training  Learn key AI concepts and intuition training to get you quickly up to speed with all things AI. Covering:
How to merge AI with Open AI Gym to learn as effectively as possible
How to optimize your AI to reach its maximum potential in the real world
Here is what you will get with this course: Artificial Intelligence Training
1. Complete beginner to expert AI skills – Learn to code self-improving AI for a range of purposes. In fact, we code together with you. Every tutorial starts with a blank page and we write up the code from scratch. This way you can follow along and understand exactly how the code comes together and what each line means.
2. Code templates – Plus, you’ll get downloadable Python code templates for every AI you build in the course. This makes a building truly unique AI as simple as changing a few lines of code. If you unleash your imagination, the potential is unlimited.
3. Intuition Tutorials – Where most courses simply bombard you with dense theory and set you on your way, we believe in developing a deep understanding for not only what you’re doing, but why you’re doing it. That’s why we don’t throw complex mathematics at you, but focus on building up your intuition in coding AI making for infinitely better results down the line.
4. Real-world solutions – You’ll achieve your goal is not only 1 game but in 3. Each module is comprised of varying structures and difficulties, meaning you’ll be skilled enough to build AI adaptable to any environment in real life, rather than just passing a glorified memory “test and forget” like most other courses. Practice truly does make perfect.

Artificial Intelligence Course Accreditations

PG Diploma in AI

Diploma in AI

Certified AI Engineer

AI Syllabus

INTRODUCTION TO AI & PYTHON

  • artificial Intelligence training
  • Techniques used for AI
  • Python Basics
  • Getting started with Python
  • Variables, Lists, Vectors, Matrices & Arrays
  • Control Structures – If else, for and while loop
  • Functions & Subroutines
  • Object-oriented Programming
  • Commonly used predefined function in Python
  • Mathematical Computation in Python
  • Miscellaneous Functions & their applications
  • Linear Algebra required for Artificial Intelligence
  • Getting started with PANDAS, QUANDL AND THEANO

FUZZY LOGIC

  • Applications of Fuzzy Logic
  • Problem Formulation, Defuzzification & Rule base
  • Membership Functions
  • Defuzzification Methods
  • MAMDANI & SUGENO Methods
  • Tipping Problem Analysis
  • Fuzzy Clustering
  • Fuzzy C Means Clustering

MACHINE LEARNING

  • Artificial Intelligence & Machine Learning
  • Getting Started with Machine Learning
  • Supervised Learning Introduction & Examples
  • Unsupervised Learning Introduction & Examples
  • Working with Linear Regression Problems in Python
  • Gradient Descent Algorithm for Linear Regression
  • Multivariate Linear Regression Housing Prizes Prediction
  • Logistic Regression Introduction and use cases

GENETIC ALGORITHM

  • Working with Genetic Algorithm
  • Getting started with Genetic Algorithm
  • Reproduction, Crossover & Mutation
  • Roulette Wheel method of selection
  • Fitness Function
  • Working with GA Examples
  • Optimization using GA
  • Clustering
  • Principal Component Analysis
  • PCA Applications and use cases
  • PCA Implementation

BEST MEAN FITTING

  • Working with Best Mean Fitting
  • Single Line as Hypothesis Training
  • Using THEANO for best mean fitting

ARTIFICIAL NEURAL NETWORK

  • Introduction to Network Architecture & Neuron
  • Designing Neural Network Model
  • Model Representation Methods
  • Single Layer Neural Network
  • Weights & Activation Functions
  • Multilayer Neural Network Architecture
  • Gradient Descent Algorithm
  • Working with theano & Python
  • Training Straight line hypothesis
  • Backward Propagation Training
  • Delta method and Gradient Descent

SUPPORT VECTOR MACHINE

  • Introduction to SVM
  • Concept of Support Vector Machine
  • Working with scikit learn library
  • SVM Parameters
  • Using Support Vector for Classification
  • Using Support Vector for Regression
  • Character recognition using SVM
  • Natural Language Processing
  • Natural Language Understanding & Generation
  • Using NLTK for extracting data
  • Naive Baiyes Method
  • Sentiment Analysis Example