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Why to Join Full Stack Program at Ybi Foundation?
Our Learner's placed at
Get Ready For
High Paying Job Roles in 2025
According to Microsoft, 82% of leaders globally and 85% of leaders in Asia Pacific said employees will need new skills in an “AI-powered future.”
LinkedIn added that AI has seen rapid growth in the labor market — there is 487% growth in AI talent hiring for India compared to overall hiring.
Your Learning Path for Guaranteed Success!
✨ Beginner Friendly | 👩🎓 Live Online Training | 🎯 Capstone Projects | ⭐ Interview Preparation | Completeion Certificate
Why to Join at Ybi Foundation?
Curriculum
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,
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
SQL and Data Bases
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
Generative AI* (included in Professional Plan)
Introduction to Generative vs. Discriminative Models, Probability Foundations for Generative Modeling, Bayesian Generative Models, Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Flow-Based Generative Models, Autoregressive Models (PixelRNN/CNN, WaveNet), Diffusion Models, Transformer-Based Generation (GPT, BERT-derived), Prompt Engineering & Conditioning, Evaluation Metrics for Generative Models, Fine-Tuning & Transfer Learning, Multimodal Generation (Text-to-Image, Audio), Deployment & Serving Generative APIs, Ethical Considerations & Bias Mitigation
Power BI* and Tableau* (included in Professional Plan)
Data Connectivity & Extraction (Power Query, Tableau Data Source), Data Transformation & Modeling (Power Query M, Tableau Prep), Data Relationships & Schema Design, Calculations & Expressions (DAX Measures, Tableau Calculated Fields), Time Intelligence & Date Functions, Basic Visualizations (Charts, Tables, Maps), Advanced Visualizations (Custom Visuals, Parameter-driven Views, Level-of-Detail Expressions), Interactive Dashboards & Storytelling, Mobility & Responsive Design, Performance Optimization & Query Tuning, Publishing & Sharing (Power BI Service, Tableau Server/Online, Public Gallery), Row-Level Security & Governance, Deployment & Embedding (Power BI Embedded, Tableau Embedded Analytics), Integration with Azure/Salesforce/Databricks, Automated Refresh & Scheduling, Audit & Usage Monitoring, AI-Powered Insights (Power BI AI visuals, Tableau Explain Data)
Math and Statistics* (included in Professional Plan)
Linear Algebra, Differential & Integral Calculus, Probability Theory, Descriptive Statistics, Inferential Statistics & Hypothesis Testing, Regression Analysis, Bayesian Statistics, Multivariate Statistics, Discrete Mathematics, Optimization & Numerical Methods, Time Series Analysis
Big Data* (included in Professional Plan)
SparkSession Initialization, pandas API on Spark Quickstart, DataFrame API, Series API, Index API, CategoricalIndex API, DatetimeIndex API, GroupBy & Aggregations, Window Functions, Vectorized UDFs, DataFrame↔pandas Conversion, SQL Queries & Temporary Views, DataFrame Transformations & Joins, Window Clauses & Set Operations, Performance Optimizations (broadcast joins, predicate pushdown), MLlib Data Types & Basic Statistics, Feature Extraction & Transformation (FeatureHasher, TF–IDF), Classification (Logistic Regression, Decision Trees, Random Forests), Regression (Linear Regression, Ridge Regression), Clustering (KMeans), Collaborative Filtering (ALS), Dimensionality Reduction (PCA), Model Selection & Hyperparameter Tuning (Grid Search, Cross-Validation), Pipelines API
MLOps* (included in Professional Plan)
MLOps Overview & Best Practices, Cloud Provider Account & Workspace Setup (SageMaker, Azure ML, Vertex AI), Version Control & Collaboration, Data & Feature Store Management (SageMaker Feature Store, Azure Feature Store, Vertex AI Feature Store), Experiment & Model Tracking (SageMaker Experiments, Azure ML Experiments, Vertex AI Experiments)
Who Can Join Fullstack?
✔️ College Students
✔️ Freshers / Recent Graduates
✔️ Working Professional
B.Tech / M.Tech / BCA / MCA / BBA / MBA / Diploma and all other domains . . . open for
All Branches - All academic Years
