What is AIML The Topics You Have To KNOW About (AIML) Before You Start.

 AIML (ARTIFICIAL INTELLIGENCE / MACHINE LEARNING)

About the Course:

In this course, the participants will learn deep artificial neural networks (ANN) basics to

its different branches convolutional neural network (CNN) for computer vision, LSTM

(Long short-term-memory) for NLP (natural language processing) to mathematics (linear

algebra & calculus) and Python (basic to advanced) to implement deep neural network

libraries like TensorFlow, PyTorch and API (Application programming interface) like keras.

About the Trainers:

A Team of Trainers with 30+ years of overall combined industry experience And 8

years on AIML. Currently working on AI & data science related projects.

What is the prerequisite?

Basic computer knowledge, good in math (12th class), passion to build intelligent

systems to solve real-world problems.

Education Qualification?

Any Graduate/Engineer with a math background

Duration

120 Hours (normal track)

AIML (ARTIFICIAL INTELLIGENCE / MACHINE LEARNING)

Content@glance

Topics                                                                                                  Duaration

Introduction to AI (Artificial intelligence)

Programming (Python) for AI                                                              6 Weeks

Mathematics for AI 1 Week

ML (Machine Learning) - a branch of AI                                             2 Week

Deep Learning - a subfield of ML                                                        2 Weeks

AI on Cloud

Getting started With Cloud                                                                   1 Week

Natural Language Processing with a mini Project                                2 Weeks

Computer Vision with a mini Project                                                    2 Weeks

AIML (ARTIFICIAL INTELLIGENCE / MACHINE LEARNING)

Topics                                                                                                    Details

Introduction to AI                                                                                        Introduction

                                                                                        History, Why? How? Real-time Examples of AI

Programming for AI

• Getting Started with Python
• Python Intermediate
• Numpy
• Python Advanced
• RegEx
• OOPs
• Lambda
• Database

Mathematics for AI

• Linear Algebra
• Calculus
• Fundamental Statistics
• Advanced Calculus
• Numerical Optimisation

 

Machine Learning

• Machine Learning
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Linear Regression
• Logistic Regression
• Polynomial Regression
• Multiple Regression
• Classification
• Prediction
• Algorithms
• Support Vector Machines (SVMs)
• Tree Models
• Naive Bayes Model
• Principal Component Analysis
• Clustering
• Boosting
• Time Series

AIML (ARTIFICIAL INTELLIGENCE / MACHINE LEARNING)

Topics                                                                                                    Details

Deep Learning

• Deep Learning
• Architecture
• Neural Networks
• Multi Level Perceptron
• Convolutional Neural Networks
• Recurrent Neural Networks

Professional AI

Getting started With Cloud

• AWS Fundamentals and Services
• Azure Fundamentals and Services

Natural Language Processing

• Natural Language Processing
• Introduction
• Exploring NLP Libraries
• NLTK
• SPACY
• GENSIM
• KERAS
• RASA
• REGEX
• SCIKIT LEARN
• Python text files
• PDF and regular expressions
• Tokenization
• Stemming
• Lemmatization
• stop words Phrase Matching and Vocabulary
• Topic Modeling
• Latent Dirichlet Allocation Overview
• Non-negative Matrix Factorization
• Text Blob
• TextBlob Introduction

AIML (ARTIFICIAL INTELLIGENCE / MACHINE LEARNING)

Topics                                                                                                    Details 

Natural Language Processing

• Finding a polarity of a string with TextBlob
• Sentiment analysis with TextBlob
• Measuring language subjectivity with TextBlob and
• Python
• Language Translation with Python Module TextBlob
• ,
• extBlob nGrams Spacy
• Concepts and Parameters and Interacting with
• Chatbot
• Bonus: Discovering NLP on Cloud ( AWS, Azure and
• Google Cloud Platform

Computer Vision

• Computer Vision
• Introduction
• OpenCV
• Introduction to the Library
• Image Processing for Computer Vision
• Linear Image Processing
• Model Fitting
• Frequency Domain Analysis
• Camera Models and Calibration
• Camera Views
• Camera Models
• Camera Calibration
• Stereo Geometry
• Image Motion
• Image Classification
• Photometry
• Optical Flow
• Tracking
• Parametric model
• Useful Libraries
• Recognition
• Generative Models
• Discriminative models

AIML (ARTIFICIAL INTELLIGENCE / MACHINE LEARNING)

Topics                                                                                                    Details 

Computer Vision

• Finding a polarity of a string with TextBlob
• Sentiment analysis with TextBlob
• Measuring language subjectivity with TextBlob and Python
• Language Translation with Python Module TextBlob
• extBlob nGrams Spacy
• Color spaces and Segmentation
• 3D perception
• Binary Morphology
• Bonus: Computer Vision On Cloud ( AWS, Azure andGoogle Cloud Platform)
• Bonus: Discovering NLP on Cloud ( AWS, Azure andGoogle Cloud Platform

Mini projects

• Auto Attendance through Facial recognition
• Chatbots
• Voice to text processing
• OCR on Cloud. 

Duration

120 Hours 

• Stay Tuned For More Blogs And Updates.....