Artificial Intelligence Online Training

You may learn Artificial Intelligence and Machine Learning skills like Data Science, CNN, perceptron, TensorFlow, Neural Networks, NLP, etc. with our online Artificial Intelligence course in Chennai utilising TensorFlow in conjunction with CCE, IIT Madras. To become a successful artificial intelligence engineer, enrol in the top online artificial intelligence programme taught by Top professors.

Needintech provides a thorough Artificial Intelligence programme that will assist you in working with the most cutting-edge technologies available today (AI). As part of this top AI training, you will learn how to use Python to script Machine Learning programmes, as well as various components of artificial neural networks, supervised and unsupervised learning, logistic regression with a neural network approach, binary classification, and vectorization.

Syllabus of Artificial Intelligence Course in Training

Module 1: Introduction to Data Science

• What is Data Science?
• What is Machine Learning?
• What is Deep Learning?
• What is AI?
• Data Analytics & it’s types

Module 2: Introduction to Python

• What is Python?
• Why Python?
• Installing Python
• Python IDEs
• Jupyter Notebook Overview

Module 3: Python Basics

• Python Basic Data types
• Lists
• Slicing
• IF statements
• Loops
• Dictionaries
• Tuples
• Functions
• Array
• Selection by position & Labels

Module 4: Python Packages

• Pandas
• Numpy
• Sci-kit Learn
• Mat-plot library

Module 5: Importing Data

• Reading CSV files
• Saving in Python data
• Loading Python data objects
• Writing data to csv file

Module 6: Manipulating Data

• Selecting rows/observations
• Rounding Number
• Selecting columns/fields
• Merging data
• Data aggregation
• Data munging techniques

Module 7: Statistics Basics

• Central Tendency
• Probability Basics
• Standard Deviation
• Bias variance Trade off
• Distance metrics
• Outlier analysis
• Missing Value treatment
• Correlation

Module 8: Error Metrics

• Classification
• Regression

Module 9: Machine Learning

• Supervised Learning
• Linear Regression
• Logistic regression

Module 10: Unsupervised Learning

• K-Means
• K-Means ++
• Hierarchical Clustering

Module 11: SVM

• Support Vectors
• Hyperplanes
• 2-D Case
• Linear Hyperplane

Module 12: SVM Kernal

• Linear
• Radial
• polynomial

Module 13: Other Machine Learning algorithms

• K – Nearest Neighbour
• Naïve Bayes Classifier
• Decision Tree – CART
• Decision Tree – C50
• Random Forest

Module 14: ARTIFICIAL INTELLIGENCE

• Perceptron
• Multi-Layer perceptron
• Markov Decision Process
• Logical Agent & First Order Logic
• AL Applications

Module 15: Deep Learning Algorithms

• CNN – Convolutional Neural Network
• RNN – Recurrent Neural Network
• ANN – Artificial Neural Network

Module 16: Introduction to NLP

• Text Pre-processing
• Noise Removal
• Lexicon Normalization
• Lemmatization
• Stemming
• Object Standardization

Module 17: Text to Features

• Syntactical Parsing
• Dependency Grammar
• Part of Speech Tagging
• Entity Parsing
• Named Entity Recognition
• Topic Modelling
• N-Grams
• TF – IDF
• Frequency / Density Features
• Word Embedding’s

Module 18: Tasks of NLP

• Text Classification
• Text Matching
• Levenshtein Distance
• Phonetic Matching
• Flexible String Matching