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Artificial Intelligence Training in Ahmedabad

Artificial Intelligence is the most popular and highly in-demand and Job-oriented skill in the IT Industry. In today’s era, most of the advanced tech products are driven by Artificial Intelligence. People generally use big tech phrases to explain the concept of machine learning like Self Driving cars, Face recognition, Fingerprint identification, etc. We at Logicrays, help you to understand the core concept of ML/AI with hands-on experience.

This course is beginner friendly and will give you a clear picture of the Artificial Intelligence world. During the course students will design and develop interesting live-projects like Predicting the House Price, Temperature Prediction of a Day, Facial Recognition, Animal recognition, etc. After completing this course you will be ready to learn Advance Machine Learning .

WHAT IS Artificial Intelligence AND ML

Right now in industry people uses many terms like Machine Learning, Artificial Intelligence, deep Learning , Data Discovery , Data Science, Data Analytics very interchangibly but they all are very similiar to each other. In other way we can say that all the fields are stiched together and uses similiar techniques. In one sentence, Artificial Intelligence is way of creating a computer program that improves with experiments. Generally AI involves creating models (models means program logic that can not be written by humans itself). For example think about writing program logic for Face Recognition, how would you write with your programming knowledge it is quite difficult , isn’t it? So, to handle that kind of task you will use Artificial Intelligence technique.
Artificial Intelligence is active field of research and have vast promissing in the present time and near future. All the BIG TECH Giants are using Artificial Intelligence in their hard core products. Think about Amazon , Google, Flipkart, Dropbox, Zomato, Facebook, Spotify, etc. all these company are using AI in their product development.

WHY YOU NEED TO LEARN Artificial Intelligence?

Today, Artificial Intelligence have touched almost all the sectors or industry like Finance, Biotech, Engineering, Aerospace, Database, Signal Processing, Information Technology. Because of that openings for ML/AI engineers is more in almost every sectors.Other advantages of taking Machine Learning course:

Saves Time
and Money
Automation Tools
based Testing
Best for
Regression Testing
Reuse of
test cases
Artificial Intelligence
  • Introduction of Python
  • Overview of python
  • History about python
  • How python is differ from other languages
  • Installation of python
  • Variables, Keywords, Data types and Operators
  • What is Variables
  • Keywords in python
  • How to comment in python
  • Operators in python
  • Basic I/O and Type casting
  • Getting data from user using input function
  • Different datatypes in python
  • Numbers and strings
  • what is string in python
  • working with string datatypes
  • Builtin methods of string
  • string formatting
  • Lists , Tuples, Dictionary and sets
  • working with list datatypes
  • Properties of list
  • Builtin methods of list
  • List comprehension
  • working with Tuple
  • Properties of Tuple
  • methods of Tuple
  • Working with Dictionary
  • Properties of Dictionary
  • Different methods of Dictionary
  • Dictionary comprehension
  • Working with sets
  • properties of sets
  • different methods of sets
  • Decision making and loops
  • Introduction to Decision making
  • Introduction control flow statements
  • working with IF statement, ELSE statement, ELIF statemeent
  • Usecase of nested loop in programming
  • While loop in python
  • Usecase of Break and Continue statement
  • FOR loop in python
  • Pass statement
  • User Defined Function and Modules
  • Introduction to function
  • function define and call
  • Built-in function
  • Modules
  • Importing Module and Packages
  • Exception Handling in python
  • Understanding Exception handling
  • Run Time Errors
  • Try, Except, Finally
  • Raise Exception
  • File Handling in Python
  • Working with files
  • Reading, Writing, making use of File
  • read(), readlines(), write(), writelines()
  • Object Oriented programming with Python
  • OOPs Concepts: Class and Objects
  • making of class and objects
  • Inheritance and Overriding
  • Overloading functions
  • Operator overloading
  • Encapsulation: Hiding attributes
  • Search Algorithm
  • Components of search Problem
  • How to Solve search algorithm
  • Depth-First Search
  • Breadth-First Search
  • Greedy Best-First Search
  • A* Search
  • Adversarial Search
  • Alpha-Beta Pruning
  • Depth-Limited Minimax
  • Inferencing conclusion from knowledge
  • Knowledge-Based Agents
  • Propositional Logic
  • Inference
  • Knowledge Engineering
  • Inference Rules and Resolution
  • First Order Logic
  • what is Probability
  • what is Conditional Probability
  • Random Variables
  • Power of Bayes’ Rule
  • Joint Probability
  • Probability Rules
  • what is Bayesian Networks
  • Sampling
  • Markov Models
  • Hidden Markov Models
  • Optimization algorithm
  • Local Search
  • Hill Climbing
  • Simulated Annealing
  • Linear Programming
  • Constraint Satisfaction
  • Node Consistency and Arc Consistency
  • Backtracking Search
  • Machine Learning
  • Supervised Learning
  • Nearest-Neighbor Classification
  • Perceptron Learning
  • Support Vector Machines
  • Regression
  • Loss Functions
  • Overfitting
  • Regularization
  • Introduction to scikit-learn
  • Reinforcement Learning
  • Markov Decision Processes
  • Q-Learning
  • Unsupervised Learning
  • k-means Clustering
  • World of Neural Network
  • What is Neural Networks
  • Activation Functions
  • Neural Network Structure
  • Gradient Descent
  • Multilayer Neural Networks
  • Backpropagation
  • Overfitting
  • TensorFlow for Neural Network
  • Computer Vision using Neural Network
  • Image Convolution in computer vision
  • Convolutional Neural Networks
  • introduction Recurrent Neural Networks
  • Inferencing knowledge for Text Data / Language
  • what is Natural Language Processing
  • Syntax and Semantics
  • Context-Free Grammar
  • Introduction to nltk tool
  • n-grams and Tokenization
  • Markov Models
  • Bag-of-Words Model
  • Naive Bayes
  • Information Retrieval
  • tf-idf for Information Retrieval
  • Information Extraction
  • Word Net
  • Word Representation using One-Hot encoding
  • word2vec model for word representation
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