Deep Learning Course
Deep Learning is the most powerful branch of Artificial Intelligence that makes a machine learn through data and helps it to decide intelligently. At Xplore IT Corp, we have designed the best Deep Learning course for students, professionals and AI enthusiasts. The course comprises deep neural networks, computer vision and natural language processing. If you are willing to learn machine learning, take this perfect step further. We would make the AI concepts that appear complex seem to be simple, easy to remember and this would be supplemented by expert trainers. What this presentation of this course would say is actually making it seem come along with practical exposure through hands-on projects. Other than that, with the increased induction of AI in more and more industries, even deep learning can open up very high-paying jobs too. Join us today in this Deep Learning course and be an AI expert.
Key Features
Highlights of Xplore IT Corp Deep Learning Course
Comprehensive Curriculum
The curriculum of the Deep Learning course has all the core topics such as: Neural networks, Deep neural networks and Natural language processing. It is designed to train the student systematically to learn machine learning.
Experienced Trainers
Our trainers have extensive experience in AI and Deep Learning. They know how to explain complex ideas to the students, making learn machine learning an easy learning area for the students.
Hands-on Training
We believe in learning practically. The Deep Learning course at Xplore IT Corp is trained using real-world projects, coding exercises and case studies to enhance learning.
Support Certification
Students will be supported with certified level certification after completion of the Deep Learning course and absolutely ensure good chances of getting employment in AI fields.
Flexible Learning Options
The Deep Learning course can be made accessible to all at a given time through scheduling of classroom training or online sessions.
100% Placement Support
We offer thorough placement support, guaranteeing that our students land positions in leading IT and AI firms. By combining your abilities with the finest possibilities available.
Deep Learning Course Objectives
Discover various neural network topologies and how they apply in AI for creating intelligent and efficient models. In order to strengthen your skills in AI, deep learning frameworks need to be learned thoroughly.
Understand Neural Networks
Practice applying deep learning principles on practical AI projects. Engage in the construction, iteration and training of AI models towards some applications.
Deep Learning Practice
Identify the latest advanced computer vision and NLP techniques applied in AI. Understand how computers interpret text and images to develop AI-based applications.
Computer Vision and NLP Analysis
Build AI models using TensorFlow and PyTorch, applying Deep Learning techniques. Develop scalable AI applications by applying the most advanced deep learning techniques.
Building AI Models
Our Deep Learning course gives you the practical experience and technical know-how required for jobs using AI .Develop knowledge to work as a data scientist or AI engineer.
AI Industry Readiness
Get the lowdown on what’s right and wrong in AI tech. Dive into how to play fair with AI tools, make stuff clear for everyone and keep AI on the up-and-up.
AI Ethics and Responsible AI
Benefits of Deep Learning Course Certification
- The certification from our Deep Learning course is valid everywhere in the world.
- AI-certified professionals have a competitive edge in the job market.
- By offering certification, he/she has hand-on experience from AI technologies.
- One who has in hand the certification in AI can also be the leader of the industry.
- Employers would look forward to offering AI-based job roles to the certified AI professional.
- Be one of the few non-certified applicants with an AI certification by Xplore IT Corp.
Benefits of Learning Deep Learning Course at Xplore IT Corp
Industry-specific skills
Through our deep learning course, you will be given extensive hands-on experience with AI technologies, which will prepare you and get you ready to work in a job.
Real-world projects
Through the process of learning by real-world projects, it will boost the ability to solve real-world problems concerning AI and the theory implemented.
High Demand for AI Professionals
A huge number of people are looking for professionals in AI and Deep Learning, so to learn machine learning must be learnt with better proficiency in the field.
Enhanced Career Opportunities
Wonderful career opportunities for working as an AI Engineer, a Data Scientist or ML Researcher also provided with esteemed certification in Deep Learning.
Experienced trainers
Experienced professionals in the field will mentor you through Deep Learning, guaranteeing that you understand concepts with ease and assurance through practical projects, real-world applications and knowledgeable mentoring.
Networking and Placement Support
We offer networking and placement support with AI professionals along with supporting placement such that students can get more than enough opportunities for keeping their careers growing in the future.
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 are AI professionals who have experience in Deep Learning and Machine Learning. They are trained on working real-world applications of AI for easier learning, practical exposure with the machine-learning topic. Also, every trainer is qualified to give his experience to help the students step by step practically. This topic Deep Learning involves training on actual live examples that one finds in the industry itself for better exposure. Our AI trainers are so enthusiastic and therefore manage to deliver high quality knowledge in class.
Industry-Leading Training
Best course on deep learning by Xplore IT Corp, this also enables students to learn machine learning approaches.
Hands-on learning
Our training is going to be majorly hands-on with the AI projects letting the student develop their skills in good technical ground.
Experienced AI Trainers
Our trainers are equipped with extensive knowledge and industry insights which leads to effective learning.
Flexible Learning Modes
We provide both in-person and online instruction, so you may select the one that best suits your interests and schedule. Our adaptable alternatives guarantee a smooth learning process from any location.
Certification & Job Assistance
Our Course certification is industry-recognized and that can surely improve your job opportunities.
Affordable Course Fees
We are providing quality AI training at a very affordable fee, wherein money's worth is given.
Placement after Completion of Deep Learning Course
Good Industry Network
Xplore IT Corp partners with leading AI companies for placements.
Dedicated Placement Team
Our team assists the students in their career counseling and resume building besides interview practice.
Internship Opportunities
Students get to experience the working of AI during internship.
Resume Building
We coach the students on how to develop an AI-based resume and that is sure to grab the attention of the recruiter.
Mock Interviews
Our mock interviews prepare the student thoroughly for the AI job interview.
Project Experience
Students work on real-world AI projects, enhancing their portfolios and making them job-ready.
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
This will be for 3-6 months depending on the batch and mode of learning.
Some basic knowledge of the program helps, but probably not necessary.
Of course, our course is for a beginner learning all about machine learning in an extremely easy way.
After the end of this course and placement training, you’re eligible to be an AI Engineer, a Data Scientist or even an ML Expert.
Yes. You get a recognized AI certification on the successful completion of the course.
You will gain hands-on experience with TensorFlow, Keras, Python, PyTorch and more with this Deep Learning course.
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