Build a strong foundation in Data Science & AI with hands–on training‚ expert mentorship– and real–world projects. Gain in–demand skills and accelerate your career in the world of intelligent technology!
Gain real–world experience with live projects
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Revisit course materials anytime‚ anywhere
Daily assignments and quizzes to track progress
ATS friendly Resume Template
3-Month Internship with Live projects
Live Session for LinkedIn optimization
Scenario-based Interview Practice with 300+ Questions
Access to Internal Referral Vacancies
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A project to extract data from websites using libraries like Beautiful Soup and Scrapy, useful for gathering information such as product prices or news articles.
An application that fetches real-time weather data using APIs like OpenWeatherMap and displays current conditions, forecasts, and alerts for specified locations.
A simple AI-driven chatbot using NLP libraries like NLTK or spaCy, capable of holding basic conversations, answering FAQs, or providing customer support.
A personal finance tool that allows users to input, categorize, and track expenses, generating reports and visualizations using libraries like Pandas and Matplotlib.
A task management app with CRUD operations, built using Flask or Django for the backend, allowing users to add, update, delete, and mark tasks as complete.
A system for managing book inventories, user checkouts, and returns in a library, featuring a database backend like SQLite and a GUI using Tkinter.
Develop a model using convolutional neural networks (CNNs) to classify images into categories (e.g., cats vs. dogs) using datasets like CIFAR-10 or ImageNet.
Create a natural language processing (NLP) model to analyze text data from social media, reviews, or customer feedback to determine the sentiment (positive, negative, or neutral).
Build a regression model using algorithms like linear regression or random forests to predict house prices based on features such as location, size, and amenities, using datasets like the Boston Housing dataset.
Implement a classification model to detect fraudulent transactions in financial data using techniques like logistic regression, decision trees, or gradient boosting.
Develop a collaborative filtering or content-based recommendation system to suggest products, movies, or articles to users based on their past interactions and preferences.
Apply clustering algorithms like K-means to segment customers into distinct groups based on their purchasing behavior and demographics, aiding targeted marketing strategies.
| Benefits | iLearnings Training | Other platforms | Youtube |
|---|---|---|---|
| Free placement assistance | |||
| Direct interview invites | |||
| Guaranteed 1-month internship | |||
| Live classes+ recorded | |||
| Non-IT to IT Transitions | |||
| Google Rating 4.8 | |||
| Hands-on Experience | |||
| Post-Course Support | |||
| Additional Certifications | |||
| Scholarships and Financial Aid |
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