Artificial Intelligence Course in Coimbatore

The demand for AI professionals is soaring across various sectors, including healthcare, finance, retail, manufacturing, and entertainment. By learning AI, you position yourself for exciting career opportunities and gain a competitive edge in the job market.

AI enables automation of repetitive and time-consuming tasks, boosting productivity and efficiency. By leveraging machine learning algorithms, AI systems can handle complex data analysis, pattern recognition, and decision-making processes with remarkable speed and accuracy.

AI algorithms excel in analyzing vast amounts of data, extracting valuable insights, and making predictions. This capability empowers businesses to make data-driven decisions, optimize processes, improve customer experiences, and identify emerging trends, giving them a competitive advantage.

AI plays a pivotal role in developing personalized experiences and recommendation Examples include personalized shopping recommendations, content recommendations on streaming platforms, and targeted marketing campaigns, enhancing user satisfaction and engagement.

AI has the potential to revolutionize healthcare. It can aid in diagnosing diseases, analysing medical images, predicting patient outcomes, and optimizing treatment plans. AI-powered healthcare solutions have the potential to improve patient care, reduce medical errors, and enhance clinical

AI technologies, such as natural language processing and computer vision, enhance user experiences by enabling interactions through voice commands, chatbots, and virtual These technologies simplify tasks, improve accessibility, and provide personalized interactions, transforming how we interact with computers and devices.

As AI continues to advance, ethical considerations become crucial. Understanding AI concepts, including bias, fairness, privacy, and transparency, equips you to address these concerns By incorporating ethical considerations into Artificial Intelligence development, we can ensure responsible and beneficial use of AI technologies.

AI opens doors for entrepreneurial ventures. The ability to develop AI-driven products and services, such as intelligent applications, predictive analytics tools, and automation solutions, allows you to innovate and create your own startup or contribute to existing ventures.

AI intersects with various disciplines, including computer science, mathematics, psychology, and neuroscience. Learning AI fosters interdisciplinary thinking and collaboration, enabling you to work on diverse projects and explore new frontiers in research and development.

⦁ History
⦁ Features
⦁ Setting up path
⦁ Working with Python scripts
⦁ Basic Syntax
⦁ Variables and Data Types in scripting
⦁ Operators

⦁ If , If- else, Nested if-else statements
⦁ For, While loops
⦁ Nested loops
⦁ Control Statements

⦁ Accessing Strings
⦁ Basic Operations
⦁ String slices
⦁ Function and Methods

⦁ Accessing list
⦁ Operations
⦁ Working with lists
⦁ Function and Methods

⦁ Accessing tuples
⦁ Operations
⦁ Working
⦁ Functions and Methods

⦁ Accessing values in dictionaries
⦁ Working with dictionaries
⦁ Properties
⦁ Functions

⦁ Defining a function
⦁ Calling a function
⦁ Types of functions
⦁ Function Arguments
⦁ Anonymous functions
⦁ Global and local variables

⦁ Importing module
⦁ Math module
⦁ Random module
⦁ Packages
⦁ Composition

⦁ Printing on screen
⦁ Reading data from keyboard
⦁ Opening and closing file
⦁ Reading and writing files
⦁ Functions

⦁ Exception
⦁ Exception Handling
⦁ Except clause
⦁ Try ? finally clause
⦁ User Defined Exceptions

⦁ Class and object
⦁ Attributes
⦁ Inheritance
⦁ Overloading
⦁ Overriding
⦁ Data hiding

⦁ Match function
⦁ Search function
⦁ Matching VS Searching
⦁ Modifiers
⦁ Patterns

⦁ Architecture
⦁ CGI environment variable
⦁ GET and POST methods
⦁ Cookies
⦁ File upload

⦁ Connections
⦁ Executing queries
⦁ Transactions
⦁ Handling error

⦁ Socket
⦁ Socket Module
⦁ Methods
⦁ Client and server
⦁ Internet modules

⦁ Thread
⦁ Starting a thread
⦁ Threading module
⦁ Synchronizing threads
⦁ Multithreaded Priority Queue

⦁ Tkinter Programming
⦁ Tkinter widgets
⦁ Mail Communication in python scripts

⦁ models.py
⦁ urls.py
⦁ views.py
⦁ Setting up database connections
⦁ Managing Users & the Django admin tool
⦁ Django URL Patterns and Views
⦁ Designing a good URL scheme
⦁ Generic Views
⦁ Django Forms
⦁ Form classes
⦁ Validation & Authentication
⦁ Advanced Forms processing techniques
⦁ Unit Testing with Django
⦁ Using Python’s unittest2 library
⦁ Test
⦁ Test Databases

⦁ Scrapy – Overview
⦁ Scrapy – Environment
⦁ Scrapy – Spiders
⦁ Scrapy – Item Pipelines
⦁ Scrapy – Link Extractors
⦁ Scrapy – various output consoles
⦁ Define item
⦁ First spider project
⦁ Crawling Content
⦁ Extracting item
⦁ Scraped data

⦁ What is Flask?
⦁ Flask – Overview
⦁ Flask – Environment
⦁ Flask – Application
⦁ Flask – Routing
⦁ Flask – Variable Rules
⦁ Flask – URL Building
⦁ Flask – SQLite
⦁ Flask – SQLAlchemy

⦁ The core: Image – load, convert, and save
⦁ Smoothing Filters A – Average, Gaussian
⦁ Smoothing Filters B – Median, Bilateral
⦁ OpenCV 3 with Python
⦁ Image – OpenCV BGR: MatplotLIB
⦁ Basic image operations – pixel access
⦁ iPython – Signal Processing with NumPy
⦁ Signal Processing with NumPy I – FFT and DFT for sine, square waves, unitpulse, and random signal
⦁ Signal Processing with NumPy II – Image Fourier Transform: FFT & DFT
⦁ Inverse Fourier Transform of an Image with low pass filter: cv2.idft()
⦁ Image Histogram
⦁ Video Capture and Switching colour spaces – RGB / HSV
⦁ Adaptive Thresholding – Otsu’s clustering-based image thresholding
⦁ Edge Detection – Sobel and Laplacian Kernels
⦁ Canny Edge Detection
⦁ Hough Transform – Circles
⦁ Watershed Algorithm: Marker-based Segmentation I
⦁ Watershed Algorithm: Marker-based Segmentation II
⦁ Image noise reduction: Non-local Means denoising algorithm
⦁ Image object detection: Face detection using Haar Cascade Classifiers
⦁ Image segmentation – Foreground extraction Grabcut algorithm based on graph cuts
⦁ Image Reconstruction – Inpainting (Interpolation) – Fast Marching Methods
⦁ Video: Mean shift object tracking

⦁ Installation
⦁ Features and feature extraction – iris dataset
⦁ Machine Learning Quick Preview
⦁ Data Preprocessing I – Missing / Categorical data
⦁ Data Preprocessing II – Partitioning a dataset / Feature Scaling / Feature Selection / Regularization
⦁ Data Preprocessing III – Dimensionality Reduction vs Sequential Feature Selection / Assessing Feature importance via random forests
⦁ Data Compression via Dimensionality Reduction I – Principal component analysis (PCA)
⦁ Data Compression via Dimensionality Reduction II – Linear Discriminant Analysis (LDA)
⦁ Data Compression via Dimensionality Reduction III – Nonlinear mappings via kernel principal component (KPCA) analysis
⦁ Logistic Regression, Overfitting & regularization
⦁ Supervised Learning & Unsupervised Learning – e.g. Unsupervised PCA dimensionality reduction with iris dataset
⦁ Unsupervised Learning – KMeans clustering with iris dataset
⦁ Linearly Separable Data – Linear Model & (Gaussian) radial basis function kernel (RBF kernel)
⦁ Decision Tree Learning I – Entropy, Gini, and Information Gain
⦁ Decision Tree Learning II – Constructing the Decision Tree
⦁ Random Decision Forests Classification
⦁ Support Vector Machines (SVM)