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    ⏳ Duration : 3 Months

    βœ… Artificial Intelligence βœ… Machine Learning βœ… Data Science
    βœ… Computer Vision βœ… NLP and Text Analytics and more . . .

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​Best Live Training and Top MNC's Projects in FREE Bootcamps!

πŸ”₯Program Highlights:

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Live Online Classes

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Hands-On Projects

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Live Doubt Classes

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Skill Certifications

🎯 Tools You will Cover

and more . . . 

πŸ“š What You Will Learn?

 50+ Hours    10+ Projects    Multiple Certificates 

Python Programming

Environment Setup: Install Python, configure Anaconda & Jupyter Notebooks, and familiarize with the interpreter workflow.
Core Data Types: Work with integers, floats, strings, and booleans.
Operators: Arithmetic Operators, Comparison Operators, Logical Operators, Assignment Operators, Bitwise Operators, Membership Operators, Identity Operators.
Container Types: Create and manipulate lists, tuples, dictionaries, and sets.
Control Flow: Implement logic using if / elif / else statements and repetition with for and while loops.
Functions: Define reusable code blocks, handle positional and keyword arguments, return values, and apply default parameters.
Machine Learning

Data Preprocessing and Feature Engineering: Handling missing data: Imputation techniques, Data normalization and standardization, Encoding categorical variables: One-hot encoding, label encoding, Feature selection and extraction: PCA, correlation analysis.
Supervised Learning: Regression and Classification problems, simple linear regression, multiple linear regression, ridge regression, logistic regression, k-nearest neighbour, naive Bayes classifier, linear discriminant analysis, support vector machine, decision trees, bias variance trade-off, cross-validation methods such as leave-one-out (LOO) cross-validation, k-folds cross-validation, multi-layer perceptron, feed-forward neural network. Ensemble models.
Unsupervised Learning: clustering algorithms, k-means/k-medoid, hierarchical clustering, top-down, bottom-up: single linkage, multiple-linkage, dimensionality reduction, principal component analysis.
Model Evaluation and Optimization: Train-test split, cross-validation, and k-fold validation, Hyperparameter tuning: Grid search, random search, Bias-variance tradeoff and overfitting, 
Machine Learning

Data Preprocessing and Feature Engineering: Handling missing data: Imputation techniques, Data normalization and standardization, Encoding categorical variables: One-hot encoding, label encoding, Feature selection and extraction: PCA, correlation analysis.
Supervised Learning: Regression and Classification problems, simple linear regression, multiple linear regression, ridge regression, logistic regression, k-nearest neighbour, naive Bayes classifier, linear discriminant analysis, support vector machine, decision trees, bias variance trade-off, cross-validation methods such as leave-one-out (LOO) cross-validation, k-folds cross-validation, multi-layer perceptron, feed-forward neural network. Ensemble models.
Unsupervised Learning: clustering algorithms, k-means/k-medoid, hierarchical clustering, top-down, bottom-up: single linkage, multiple-linkage, dimensionality reduction, principal component analysis.
Model Evaluation and Optimization: Train-test split, cross-validation, and k-fold validation, Hyperparameter tuning: Grid search, random search, Bias-variance tradeoff and overfitting, 
Deep Learning (ANN)

Neural Networks: Basics of neural networks: Neurons, layers, activation functions, Feedforward and backpropagation, Introduction to deep learning frameworks: TensorFlow, PyTorch, Loss functions and optimizers (SGD, Adam). Build a simple neural network using TensorFlow/PyTorch. Train the network on a small dataset (e.g., MNIST).
Computer Vision

Image I/O and Video Handling, Color Space Conversion, Geometric Transformations, Smoothing & Blurring, Thresholding, Morphological Operations, Edge Detection (Canny), Corner Detection (Harris, Shi–Tomasi), Keypoint Description & Matching (SIFT, SURF, ORB), Object Detection (Haar Cascades, HOG+SVM), Contour-Based Segmentation, Deep Learning Detection (YOLO), Instance Segmentation (Mask R-CNN), Convolutional Neural Networks, Transfer Learning (VGG, ResNet), Model Fine-Tuning, Pipeline Deployment & Applications
NLP and Text Analytics

Text Preprocessing (Tokenization, Stopword Removal, Stemming, Lemmatization), Language Modeling (n-grams, Neural Language Models), Word Embeddings (Word2Vec, GloVe, FastText), Sequence Modeling (RNNs, LSTMs, GRUs), Attention Mechanisms & Transformers, Part-of-Speech Tagging, Named Entity Recognition, Dependency & Constituency Parsing, Semantic Role Labeling, Text Classification, Sentiment Analysis, Topic Modeling (LDA, NMF), Machine Translation (Seq2Seq, Attention), Text Summarization, Question Answering, Dialogue Systems, Information Extraction & Relation Extraction, Text Analytics & Visualization 
Generative AI [⭐ Professional]

Introduction to Databases and SQL, Relational Data Modeling & ER Diagrams, Database Schema Design & Normalization (1NF–3NF), SQL Data Definition Language (CREATE/ALTER/DROP), SQL Data Manipulation Language (INSERT/UPDATE/DELETE), Basic SELECT Queries (SELECT/FROM/WHERE/ORDER BY), Advanced SELECT (GROUP BY/HAVING/DISTINCT), Joins (INNER/LEFT/RIGHT/FULL), Subqueries & Nested Queries, Set Operations (UNION/INTERSECT/EXCEPT), Aggregate & Scalar Functions, Views, Stored Procedures & Triggers, Indexes & Query Optimization, Transactions & Concurrency Control, Security & Permissions, Introduction to NoSQL & NewSQL Databases 

β›³ Download Your Certificate after Completion

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πŸ”₯ Get Multiple Industry Recognizable Certifications

All Our Learner'sβ™₯️Ybi Foundation

❓ Frequently Asked Questions

What are the Benefits of joining FREE Bootcamp at Ybi Foundation?

🎁 Career Perks For Sure!

FREE Bootcamps at Ybi Foundation provides an unique opportunity to Gain real-world industry experience, with top industry projects with 1 : 1 Mentor Guidance from Industry Experts. After the Completion of course - you will get a Digitally Verified Completion Certificate to boost your resume and accelerate your career success.

What are the pre-requisites to apply for FREE Bootcamp at Ybi Foundation?

πŸŽ“ Under-Grads | Graduates | Freshers | Post Grads - All are eligible!

There are no specific prerequisites to apply for FREE Bootcamp Program at Ybi Foundation. Whether you come from a technical or non-technical background -  you are welcome to join. All our programs are designed from scartch to support learners from all areas of study, regardless of their prior experience.

Can I join FREE Bootcamp with my regular college?

πŸŽ“ Yes! you can Join FREE Bootcamp Program after your college hours

FREE Bootcamp Program at Ybi Foundation offers complete flexibility, allowing students to work on projects after college hours at their convenience. With full support from our team, you'll gain hands-on experience and build an impressive portfolio without disrupting your studies.

Will I get Completion Certificate at Ybi Foundation?

βœ… Yes! Get Your Completion Certificate For Sure!

All learners get their Digitally Verified Completion Certificate after the 100% completion of their enrolled program.

How to get support from team?

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Our team is always with you to support in each and every phase. Feel free to connect us on Call or WhatsApp at (+91) 966 798 7711 or write us at support@ybifoundation.org

πŸš€Explore FREE Bootcamps in 2025

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Machine Learning

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Python Programming

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Computer Vision

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Machine Learning

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NLP & Text Analytics

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Python Programming

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SQL

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