Natural Language Processing Course is a very fascinating aspect of Artificial Intelligence wherein it enables the computer to understand, interpret, and generate human language. From voice assistants to sentiment analysis, NLP is thereby changing the way the human race communicates with technology. Based at Xplore IT Corp, our course in Natural Language Processing is structured to prepare students in mastering this new-age technology. Whether you are a beginner or someone with prior coding experience, our course will guide you through the core concepts and advanced techniques of NLP.
Sign up for our program, and get to know text processing, language modeling, machine translation, and a whole lot more. With an industry-demanded curriculum, learning through hands-on projects and expert mentorship, you will be ready to face the opportunities in this burgeoning field. Join us, and open doors into exciting career prospects.
Key Features
What you will Learn in this Course
- Natural Language Processing Basics and its Applications
- Text preprocessing techniques such as tokenization and stemming
- Machine learning algorithms used for NLP tasks
- Deep learning models for text classification and language generation
- Tools like NLTK, SpaCy, and TensorFlow for NLP development.
- Development of chatbots and sentiment analysis systems.
Benefits of the Natural Language Processing Course in Coimbatore
Industry-Related Skills
You would acquire expertise on the tools and techniques in huge demand in all industries.
Practical Learning
You would have the practical session that applies your theoretical knowledge to projects.
Guidance from Experts
Learn directly from professionals with a deep understanding of NLP.
Flexibility in Time
Attend classes at your preferred time.
Career Guidance
Get assisted by our placement services to find your dream job.
Certification
Obtain a valuable certificate that can be added to your professional resume.
Syllabus of Natural Language Processing Course
C & C++ Programming
⦁ 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
Python Web App Development With Django
⦁ What is Django?
⦁ DRY programming: Don’t Repeat Yourself
⦁ How to get and install Django
⦁ 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)
Machine Learning Using Flask
⦁ Serializing with pickle & DB setup
⦁ Basic Flask App
⦁ Embedding Classifier
⦁ Deploy
⦁ Updating the Classifier
Our trainers at Xplore IT Corp are professional experts with many years of experience in Natural Language Processing Course and AI. They have worked on diverse NLP projects, including machine translation systems and conversational AI. With their in-depth industry insight and teaching experience, trainers guarantee that the concepts are not only understood well but also confidence is taken to apply them in practical life. The trainers teach through clearly interactive learning and therefore want the students to continue to raise questions, participate in group discussions, and do collaborative projects. Learn from the very best in the industry. Stay tuned.
Learn from Industry Experts
Our courses are taught by professionals with real-world experience.
Comprehensive Learning
Gain in-depth knowledge through our well-structured curriculum.
Hands-on work
Learn skills by doing real-world projects.
Flexible Batches
Choose from weekday or weekend batches for desired Course.
Certification
After finishing the course, you will obtain a recognized certificate.
Job Placement Support
Our students receive job placement opportunities after completing the course.
Continuous Mentorship
Our students of the course will avail ongoing mentor support.
Flexible Payment Plans
Available payment options enable any individual to afford the course. This makes it affordable and accessible to all.
Job prospects after graduating from the course
NLP Engineer: Design and deploy NLP algorithms.
Data Scientist: Applied the knowledge of data science to find insights from the text data.
AI Developer: Developing AI systems especially with a sense of language.
Chatbot Developer: Develop chatbot AI especially for business
Machine Learning Engineer: Work focused on NLP applications in the field of ML.
Research Analyst: Research to improve NLP technology
Our in Demand Courses
Frequently Asked Questions
The Natural Language Processing Course is 3 months long and allows flexible class schedules.
Basic understanding of programming is preferred but we also have foundation classes for the first-timers.
You will work on NLTK, SpaCy, TensorFlow, other popular NLP tools, and many more.
Yes, our NLP certified course certification is an industry-recognized certificate that employers give weight to.
We provide resume building, interview preparation, and placement assistance to all our students.
Absolutely! We have flexible schedules to accommodate working professionals as well.
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