Value Addition Courses (VAC), Free Resources

September 6, 2025March 9, 2025 inkr.hyd@gmail.com

CSE-VAC-Artificial Intelligence – Full Youtube Playlist

Overview of Artificial Intelligence, AI, VAC, B.Sc., Free resources, Unit 1

Artificial Intelligence is transforming industries, businesses, and daily life. In this video, we cover *Unit 1: Introduction to AI*, exploring *what AI is, its evolution, applications, and future trends*. This ...session provides a *comprehensive guide* for beginners and enthusiasts looking to understand the foundations of AI.

📌 *Topics Covered in This Video:*
✔️ What is Artificial Intelligence?
✔️ Importance and Applications of AI
✔️ AI's Evolution and History
✔️ Types of AI: Weak AI, Strong AI, Super AI
✔️ AI's Impact on Healthcare, Finance, and Smart Cities
✔️ Future Trends in AI and Ethical Considerations

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## *⏳ Timestamps:*

⏱ *00:00* Introduction
⏱ *00:35* Overview of Artificial Intelligence (AI)
⏱ *05:52* Foundations of AI
⏱ *21:40* AI in India
⏱ *28:26* Impact and Examples of AI
⏱ *34:44* Future of AI
⏱ *41:28* Thank you…

---

## *📌 Key Takeaways from This Video:*
✅ Learn the basics of Artificial Intelligence and how it works.
✅ Understand AI's role in automation, machine learning, and problem-solving.
✅ Discover how AI is shaping industries like *healthcare, finance, education, and robotics*.
✅ Explore AI's future trends, including *Explainable AI, AI in governance, and smart cities*.

---

## *🔗 Useful Resources:*
📖 *Read More About AI:* https://csvtu.org.in/overview-of-artificial-intelligence-ai-bsc-cg-free-u1/
https://csvtu.org.in/

---

## *🔍 Tags for SEO Optimization:*
Artificial Intelligence, AI Overview, Future of AI, Machine Learning, AI Basics, AI Applications, AI in Healthcare, AI in India, Smart Cities, AI Trends, AI Course, AI Tutorial, What is AI, Deep Learning, AI in Finance, AI Ethics, AI Development, Value Addition Course, Chhattisgarh, Bsc, VACShow More

Overview of Artificial Intelligence, AI, VAC, B.Sc., Free resources, Unit 1

Overview of Artificial Intelligence, AI, VAC, B.Sc., Free resources, Unit 1

Artificial Intelligence is transforming industries, businesses, and ...

Value Added Course, CG, AI What is Machine Learning-Supervised, Unsupervised Reinforcement U2, Lec-1

Value Added Course, CG, AI What is Machine Learning-Supervised, Unsupervised Reinforcement U2, Lec-1

🎓 Lecture 0201: Introduction to Machine Learning (ML) 📚 Course: ...

🎓 Lecture 0201: Introduction to Machine Learning (ML)
📚 Course: Artificial Intelligence (Value Addition Course – CSVAC-01)
🏫 Offered by: All undergraduate Colleges in Chhattisgarh
🎯 Credits: 2 | 💡 Level: Beginner | ...⏱️ Duration: 30 Minutes

🔍 What You’ll Learn in This Lecture
In this engaging session, we dive into the world of Machine Learning, one of the most important branches of Artificial Intelligence. Students will explore:

✅ What is Machine Learning and how does it work?
✅ Key differences between ML and traditional programming
✅ Core idea of learning from data
✅ Three main categories: Supervised, Unsupervised, and Reinforcement Learning
✅ Real-world applications in healthcare, finance, education, games, and more
✅ Famous ML algorithms and tools
✅ How ML is applied in intelligent systems and AI pipelines
✅ Career paths and skills required for ML professionals

This lecture provides a beginner-friendly explanation of ML’s types, uses, and future scope. Perfect for students from all domains who are just starting with AI.

📌 Lecture Highlights
📈 Definition and Concept of ML
⚙️ Supervised Learning: Concepts and Use Cases
📊 Algorithms: Decision Trees, SVM, k-NN, Naive Bayes
🧠 Unsupervised Learning: Clustering, Dimensionality Reduction
🤖 Reinforcement Learning: Agents, Rewards, Policy, Games
🏥 Applications in Healthcare, Retail, Finance, E-commerce
🧰 Tools and Libraries: Scikit-learn, TensorFlow, PyTorch, Keras
📦 Role of Data, Feature Engineering, and Model Tuning
📉 Issues of Bias and Overfitting
💼 Career Opportunities in ML and AI

🎓 Why Watch This Lecture?
If you’ve ever wondered how Netflix recommends shows, how Google detects spam, or how robots learn to walk — Machine Learning is behind it all! This lecture gives you real-life examples and easy explanations to help you understand the core ideas of ML and its categories. Whether you're from arts, commerce, science, or tech — this lecture will make ML accessible and exciting.

📌 This content is structured from CSVTU’s official academic curriculum for the AI VAC course and aligned with the latest industry practices.

🔗 Explore More Courses: https://csvtu.org.in/cg-vac-courses/

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Value Added Course, CG, AI Advanced AI, ML Model Lifecycle:Training, Validation Evaluation U-2 Lec-2

Value Added Course, CG, AI Advanced AI, ML Model Lifecycle:Training, Validation Evaluation U-2 Lec-2

🎓 Lecture 0202: ML Model Lifecycle Explained Step-by-Step 📚 Course: ...

🎓 Lecture 0202: ML Model Lifecycle Explained Step-by-Step
📚 Course: Artificial Intelligence (Value Addition Course – CSVAC-01)
🏫 Offered by: All undergraduate Colleges in Chhattisgarh
🎯 Credits: 2 | 💡 Level: Beginner | ...⏱️ Duration: 30 Minutes

🔍 What You’ll Learn in This Lecture
In this lecture, we take a complete tour of the Machine Learning (ML) model lifecycle—from start to finish. This session simplifies each stage so students from all streams can understand how real-world AI models are built, trained, and deployed.

✅ What is the ML Model Lifecycle?
✅ How do we collect and prepare data for ML?
✅ What’s the purpose of splitting data into train/test/validation sets?
✅ How do we choose features and models?
✅ How does training and tuning work?
✅ What is overfitting and underfitting?
✅ What are the evaluation metrics like accuracy, precision, recall, F1-score, MAE, RMSE?
✅ How are ML models deployed and monitored in real-world systems?
✅ What is model drift and how do we retrain models?

This lecture prepares students for hands-on projects, internships, and future AI development work by grounding them in essential lifecycle steps.

📌 Lecture Highlights
📊 Data Collection, Preprocessing, and Feature Engineering
🔍 Dataset Splitting: Train, Validation, Test
🤖 Model Selection and Training
⚙️ Hyperparameters and Cross-Validation
📉 Overfitting, Underfitting, Regularization Techniques (L1, L2)
📈 Evaluation Metrics for Classification and Regression
🧮 Confusion Matrix, ROC-AUC, MAE, MSE, RMSE
🛠️ Tools for Deployment: Flask, Docker, FastAPI, TensorBoard, MLflow
🔄 Model Drift, Monitoring, and Retraining
📂 MLOps and Version Control
🔍 Importance of Explainability in ML Decisions

🎓 Why Watch This Lecture?
This lecture is perfect for students who want to build career-ready skills in Artificial Intelligence and Machine Learning. Whether you dream of being a data scientist, ML engineer, or AI researcher, understanding the model lifecycle is your first step.

You’ll gain clarity on how models are created, tested, and improved using real-world standards and tools. This lecture is designed using CSVTU’s official curriculum and explained in simple language for all undergraduate learners.

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#CSVTU, #VAC, #ArtificialIntelligence, #MachineLearning, #MLModelLifecycle, #ModelTraining, #CrossValidation, #DataPreprocessing, #MLMetrics, #ConfusionMatrix, #ROC, #MAE, #MSE, #Overfitting, #Underfitting, #MLOps, #MLTools, #ExplainableAI, #AIforStudents, #AIinEducation, #DrHoneyTiwari, #PythonML, #CSVTUAI, #AIlectureSeries, #CSVTUCourses, #AIProjectsShow More

Value Added Course, CG, AI, Advanced AI, Introduction Decision Trees, KNN & Naive Bayes, U-2, Lec-3

Value Added Course, CG, AI, Advanced AI, Introduction Decision Trees, KNN & Naive Bayes, U-2, Lec-3

🎓 Lecture 0203: ML Classification Algorithms Simplified 📚 Course: ...

🎓 Lecture 0203: ML Classification Algorithms Simplified
📚 Course: Artificial Intelligence (Value Addition Course – CSVAC-01)
🏫 Offered by: All Undergraduate Colleges in Chhattisgarh
🎯 Credits: 2 | 💡 Level: Beginner | ⏱️ ...Duration: 30 Minutes

🔍 What You’ll Learn in This Lecture
In this lecture, we explore three popular Machine Learning algorithms used in classification problems:
✅ Decision Tree – A rule-based model that mimics human decision-making
✅ K-Nearest Neighbors (KNN) – A distance-based algorithm for pattern recognition
✅ Naive Bayes – A probability-based model best for text classification

You’ll learn their concepts, working principles, advantages, limitations, and real-life use cases. Whether you are a beginner in AI or exploring models for projects, this session gives you the foundation to choose the right algorithm.

📌 Lecture Highlights
🌳 Decision Trees

Flowchart-like structure

Splitting criteria: Gini, Entropy

Overfitting and pruning

Applications: banking, healthcare, business

📍 K-Nearest Neighbors (KNN)

Instance-based lazy learning

Distance metrics: Euclidean, Manhattan, Cosine

Choosing optimal K value

Use cases: handwriting, recommender systems

📊 Naive Bayes

Based on Bayes' Theorem

Assumes feature independence

Variants: Gaussian, Multinomial, Bernoulli

Perfect for text-based classification

⚖️ Comparison of All Three

Training vs Prediction time

Interpretability and noise sensitivity

When to use which algorithm

🛠️ Tools and Implementations

Scikit-learn, Weka, R

Used in apps and real-world AI systems

Common in healthcare, retail, finance, and NLP

🎓 Why Watch This Lecture?
This lecture is your gateway to understanding real-world classification algorithms. You will not only gain technical insights but also understand how to select an algorithm based on data characteristics, interpretability, speed, and domain.

Ideal for engineering students, non-CS learners, and beginners in AI, this lecture is aligned with CSVTU's official VAC syllabus and uses simple explanations with examples you can relate to.

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#CSVTU, #VAC, #ArtificialIntelligence, #MachineLearning, #DecisionTree, #KNN, #NaiveBayes, #ClassificationAlgorithms, #MLAlgorithms, #GiniIndex, #Entropy, #BayesTheorem, #ScikitLearn, #MLForBeginners, #AIInEducation, #MLUseCases, #DistanceMetrics, #MLTools, #MLProjects, #DrHoneyTiwari, #CSVTUAI, #AIVideoLectures, #TextClassification, #ExplainableAI, #MLLectureSeriesShow More

Value Added Course, CG, AI Artificial Neuron, Activation Functions, Feedforward NN U2, Lec-4

Value Added Course, CG, AI Artificial Neuron, Activation Functions, Feedforward NN U2, Lec-4

🎓 Lecture 0204: Neural Network Fundamentals – From Neurons to ...

🎓 Lecture 0204: Neural Network Fundamentals – From Neurons to Feedforward Learning
📚 Course: Artificial Intelligence (Value Addition Course – CSVAC-01)
🏫 Offered by: All undergraduate Colleges in Chattisgarh
🎯 Credits: 2 | ...💡 Level: Beginner | ⏱️ Duration: 30 Minutes

🔍 What You’ll Learn in This Lecture
This lecture introduces the core building blocks of Deep Learning — from how artificial neurons work to how Feedforward Neural Networks (FNN) learn from data using backpropagation. You'll explore:

✅ What is a neural network and how it mimics the brain
✅ Components of an artificial neuron: input, weight, bias
✅ Linear vs Non-linear functions and the need for non-linearity
✅ Activation Functions: Sigmoid, ReLU, Tanh
✅ Feedforward Neural Networks: layers, flow, and logic
✅ Forward propagation and backpropagation
✅ Loss functions and optimization with gradient descent
✅ Overfitting, dropout, epochs, and batch size
✅ Real-world applications and tools used for neural networks

📌 Lecture Highlights
🧠 Understanding Artificial Neurons and Biological Comparison
⚙️ Activation Functions: How They Work and When to Use
📊 Structure and Function of Feedforward Neural Networks
🔁 Forward Pass and Backpropagation
📉 Loss Functions (MSE, Cross-Entropy) and Optimization with Gradient Descent
📆 Training Concepts: Epochs, Batch Size, and Dropout
🔬 Real-World Applications in Healthcare, Education, Finance, Cybersecurity
🛠️ Tools: TensorFlow, PyTorch, Keras, Colab, Scikit-learn
⚠️ Training Challenges: Overfitting, Large Data Needs, Architecture Design

🎓 Why Watch This Lecture?
Whether you want to understand how AI learns or you’re preparing for ML projects and careers, this lecture explains the foundation of modern AI systems in the most beginner-friendly way.

Aligned with CSVTU’s official curriculum, this lecture demystifies terms like "ReLU", "backpropagation", and "gradient descent" with clarity. Learn how networks make predictions, adapt through training, and get applied in your day-to-day tech.

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#CSVTU, #VAC, #ArtificialIntelligence, #NeuralNetworks, #ArtificialNeuron, #ActivationFunctions, #FeedforwardNN, #ReLU, #Sigmoid, #Backpropagation, #GradientDescent, #Epochs, #DeepLearning, #MLLecture, #MLForBeginners, #AIInEducation, #TensorFlow, #PyTorch, #Keras, #Dropout, #Classification, #Regression, #CSVTUAI, #DrHoneyTiwari, #AIForStudents, #ExplainableAI, #AIApplicationsShow More

Value Added Course, CG, VAC, AI Advanced AI, CNNs, RNNs, Layers, Pooling, Backpropagation U2, Lec-5

Value Added Course, CG, VAC, AI Advanced AI, CNNs, RNNs, Layers, Pooling, Backpropagation U2, Lec-5

🎓 Lecture 0205: Deep Learning with CNNs and RNNs Explained 📚 Course: ...

🎓 Lecture 0205: Deep Learning with CNNs and RNNs Explained
📚 Course: Artificial Intelligence (Value Addition Course – CSVAC-01)
🏫 Offered by: CSVTU, Department of Computer Science
🎯 Credits: 2 | 💡 Level: ...Beginner to Intermediate | ⏱️ Duration: 30 Minutes

🔍 What You’ll Learn in This Lecture
This lecture dives into the core of deep learning using Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). These are two of the most powerful models used in AI for solving vision and sequence problems.

✅ How CNNs process images through layers, filters, and pooling
✅ Structure and functioning of RNNs for sequence-based tasks
✅ Working principles of LSTM and GRU units
✅ Common architectures, vanishing gradient problem, and solutions
✅ Importance of dropout, batch normalization, and optimizers
✅ Real-world applications in NLP, medical imaging, robotics, and more
✅ Backpropagation, learning rate tuning, and model regularization techniques
✅ Comparing CNN vs RNN – when to use which model

📌 Lecture Highlights
🧠 CNN Architecture: Convolution, ReLU, Pooling, Flatten, Dense
📊 RNN Mechanics: Sequences, Feedback Loops, Time-step Memory
📉 Vanishing Gradient and LSTM Solutions
⚙️ Training Techniques: Backpropagation, Gradient Descent, Epochs, Batch Size
🛡️ Regularization with Dropout and Batch Normalization
🔁 Optimizers: SGD, Adam, RMSprop
📈 Use Cases in NLP, Computer Vision, and Healthcare
🛠️ Model Tuning: Hyperparameters, Layer Stacking, Validation Techniques

🎓 Why Watch This Lecture?
Whether you're a student exploring AI, preparing for ML projects, or curious about how deep learning powers today's smart systems, this lecture gives you the most important foundations of CNNs and RNNs in a beginner-friendly manner.

You’ll learn how modern applications like chatbots, face recognition, sentiment analysis, and self-driving cars work under the hood, aligned with CSVTU’s official AI curriculum.

🔗 Explore More Courses: https://csvtu.org.in/cg-vac-courses/

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#CSVTU, #VAC, #ArtificialIntelligence, #DeepLearning, #CNN, #RNN, #NeuralNetworks, #LSTM, #GRU, #Backpropagation, #Pooling, #ImageRecognition, #SequenceLearning, #AIForStudents, #MedicalImagingAI, #Chatbots, #SentimentAnalysis, #Dropout, #BatchNormalization, #NLP, #ComputerVision, #CSVTUAI, #DrHoneyTiwari, #AIeducation, #AICourseIndia, #TensorFlow, #PyTorch, #MachineLearningShow More

CSVTU, VAC, CG, AI Advanced AI, Unit - 2, Lecture -6: Real-World Applications of Deep Learning

CSVTU, VAC, CG, AI Advanced AI, Unit - 2, Lecture -6: Real-World Applications of Deep Learning

🎓 Lecture 0206: Real-World Applications of Deep Learning 📚 Course: ...

🎓 Lecture 0206: Real-World Applications of Deep Learning
📚 Course: Artificial Intelligence (Value Addition Course – CSVAC-01)
🏫 Offered by: CSVTU, Department of Computer Science
🎯 Credits: 2 | 💡 Level: Beginner to ...Intermediate | ⏱️ Duration: 30 Minutes

🔍 What You’ll Learn in This Lecture
In this insightful lecture, we explore how deep learning is applied in the real world—specifically in three powerful domains:

✅ Image Recognition using CNNs for face detection, medical imaging, object classification
✅ Game AI with rule-based and reinforcement learning agents powering smart NPCs and dynamic difficulty
✅ Recommendation Systems that personalize your experience on platforms like Netflix, Amazon, and Spotify

You will learn the underlying technologies like OpenCV, TensorFlow, YOLO, and Deep Reinforcement Learning, plus the logic behind collaborative and content-based filtering used in recommender systems.

📌 Lecture Highlights
📷 Image Recognition

CNN architecture and feature extraction

Object detection and segmentation (YOLO, SSD)

OpenCV + TensorFlow workflows

Use in healthcare, surveillance, and AR

🎮 Game AI

Rule-based vs learning agents

Reinforcement learning basics (Q-learning, DQN)

Game simulations with Unity ML-Agents, OpenAI Gym

AlphaGo and adaptive gaming behavior

🛒 Recommendation Systems

Collaborative filtering ("users like you also liked…")

Content-based methods using metadata

Hybrid systems used by Netflix & Amazon

Personalization and its business impact

🛠️ Challenges and Ethics

Bias, data privacy, compute cost

Deepfake misuse and model accountability

Importance of responsible AI development

🎓 Why Watch This Lecture?
This lecture connects deep learning theory to practical, high-demand skills used across industries. Whether you're working on a project or preparing for AI careers, this lecture provides clarity on how CNNs, RL, and recommender models work in the real world.

This session follows the official CSVTU AI VAC syllabus and is suitable for engineering, science, and interdisciplinary students.
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#CSVTU, #VAC, #ArtificialIntelligence, #DeepLearning, #ImageRecognition, #GameAI, #RecommendationSystems, #CNN, #OpenCV, #TensorFlow, #YOLO, #AlphaGo, #NetflixAI, #AmazonAI, #ReinforcementLearning, #CollaborativeFiltering, #ContentBasedFiltering, #AIApplications, #AIEducation, #DLinGames, #FacialRecognition, #SmartRecommendations, #EthicalAI, #DrHoneyTiwari, #MeetuTiwari, #CSVTUAI, #AIForStudents, #aiinreallifeShow More

CSVTU, VAC, CG | Advanced AI, U- 2, Lecture 7: Foundations of Computer Vision with Images and Pixels

CSVTU, VAC, CG | Advanced AI, U- 2, Lecture 7: Foundations of Computer Vision with Images and Pixels

CSVTU, VAC, CG, AI Advanced AI, Unit - 2, Lecture 0207: Foundations of ...

CSVTU, VAC, CG, AI Advanced AI, Unit - 2, Lecture 0207: Foundations of Computer Vision with Images and Pixels
👨‍🏫 Instructor: Meetu Tiwari
📚 Course: Artificial Intelligence (Value Addition Course – CSVAC-01)
🏫 ...Offered by: CSVTU, Department of Computer Science
🎯 Credits: 2 | 💡 Level: Beginner | ⏱️ Duration: 30 Minutes

🔍 What You’ll Learn in This Lecture
This lecture is the starting point for understanding how machines “see” using AI. You’ll learn about:

✅ How images are stored, read, and processed
✅ The role of pixels, resolution, and image formats
✅ Differences between grayscale and color images
✅ RGB, HSV, and other color models used in AI
✅ How OpenCV handles image processing
✅ Basic concepts like image histograms, thresholding, and edge detection
✅ Real-time image and video processing
✅ Applications of computer vision in devices, apps, healthcare, and more
✅ The ethics of computer vision and responsible AI use

📌 Lecture Highlights
🧠 Pixels & Digital Images

Pixel grid, intensity values, 8-bit/24-bit images

Image matrices and file formats

🌈 Color Spaces

RGB, HSV, LAB, YCrCb

Conversion between formats using OpenCV

📊 Image Preprocessing

Normalization, resizing, noise removal

Thresholding, histogram equalization, augmentation

🔍 Feature Detection

Edges, textures, and their importance in classification

Algorithms: Canny, Sobel, Laplacian

🎥 Video & Real-time CV

Frame-by-frame image processing

Applications in surveillance, AR, and robotics

🧰 Tools Used

OpenCV, NumPy, Python libraries

Easy integration into ML/DL workflows

🔐 Ethics & Real-World Use Cases

Face unlock, AR filters, X-ray analysis

Privacy concerns and dataset bias

Industrial and healthcare deployments

🎓 Why Watch This Lecture?
If you’re just starting with AI or computer vision, this lecture helps you understand how machines analyze images and videos. From camera input to AI models, every step relies on these fundamental concepts.

Perfect for engineering, science, and interdisciplinary students, this session follows the official CSVTU AI VAC syllabus and prepares you for practical AI projects.

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CSVTU, VAC, AI Advanced AI, Unit-2, Lecture 8: Computer Vision Applications - From Objects to Faces

CSVTU, VAC, AI Advanced AI, Unit-2, Lecture 8: Computer Vision Applications - From Objects to Faces

🎓 **Lecture 0208: Computer Vision Applications – From Objects to ...

🎓 **Lecture 0208: Computer Vision Applications – From Objects to Faces**
👨‍🏫 **Instructor**: Meetu Tiwari
📚 **Course**: Artificial Intelligence (Value Addition Course – CSVAC-01)
🏫 **Offered by**: CSVTU, Department of Computer Science
🎯 **Credits**: ...2 | 💡 **Level**: Beginner to Intermediate | ⏱️ **Duration**: 30 Minutes

---

### 🔍 **What You’ll Learn in This Lecture**

This lecture introduces you to the **most practical and widely-used techniques in computer vision**:
✅ **Object Detection** with YOLO, R-CNN, and bounding boxes
✅ **Edge Detection** with Sobel, Canny, and real-time applications
✅ **Face Recognition** using Haar, LBPH, FaceNet, DeepFace
✅ Emotion detection, facial landmarks, gait recognition
✅ Tools like OpenCV, TensorFlow, Dlib in live use
✅ Ethical concerns and deployment challenges in real-world systems

You’ll understand **how AI sees the world**—detecting people, objects, faces, and emotions using modern deep learning methods.

---

### 📌 **Lecture Highlights**

🔍 **Feature Detection & Edge Detection**

* Sobel, Canny, Harris, FAST, SIFT, SURF, ORB
* Used in shape detection, tracking, stitching

📦 **Object Detection & Tracking**

* YOLO, R-CNN, bounding boxes
* DeepSort, KLT for real-time object tracking

😊 **Face Recognition & Emotion Detection**

* Haar Cascade, LBPH, FaceNet, DeepFace
* Facial landmark detection and clustering
* Emotion classification using CNN

📱 **AI in Real Devices & Apps**

* Face unlock, AR filters, fitness trackers
* Retail heatmaps, security surveillance
* Gait recognition in forensics

⚠️ **Deployment & Ethics**

* Accuracy vs performance challenges
* Dataset bias, privacy laws, deepfake risks
* Regulations, opt-in systems, and fairness

🧰 **Tools & Libraries**

* OpenCV, TensorFlow, Dlib, MediaPipe
* Implementations for Python, mobile, embedded devices

---

### 🎓 **Why Watch This Lecture?**

If you're interested in **how AI powers surveillance, smartphones, retail analytics, AR filters, and more**, this lecture connects concepts to hands-on tools and real-world impact.

This lecture follows the official **CSVTU AI VAC curriculum** and is ideal for students in **engineering, IT, data science, or any AI-related field**.

---

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CSVTU, VAC, AI Tools and NLP, U -2, Advanced AI, Lecture 9: Tokenization, Lemmatization, POS Tagging

CSVTU, VAC, AI Tools and NLP, U -2, Advanced AI, Lecture 9: Tokenization, Lemmatization, POS Tagging

🎓 **Lecture 25: NLP Basics for AI – Tokenization, Lemmatization, POS ...

🎓 **Lecture 25: NLP Basics for AI – Tokenization, Lemmatization, POS Tagging**
👩‍🏫 **Instructor**: Meetu Tiwari
📚 **Course**: Artificial Intelligence (Value Addition Course – CSVAC-01)
🏫 **Offered by**: CSVTU, Department of Computer Science
🎯 ...**Credits**: 2 | 💡 **Level**: Beginner-Friendly | ⏱️ **Duration**: 30 Minutes

---

### 🔍 **What You’ll Learn in This Lecture**

In this lecture, we introduce the **essential building blocks of Natural Language Processing (NLP)**, the AI field that powers Siri, ChatGPT, Google Translate, and more.

✅ **What is NLP and why is it important in AI**
✅ Concepts of **Tokenization** – breaking sentences into words
✅ **Lemmatization vs Stemming** – how AI understands base word forms
✅ **POS (Part-of-Speech) Tagging** – identifying verbs, nouns, adjectives in a sentence
✅ Hands-on tools: **spaCy**, **NLTK**, **TextBlob**, and more
✅ Applications: chatbots, voice assistants, document summarizers, translation
✅ Bonus: Named Entity Recognition (NER), Dependency Parsing, and grammar trees
✅ Real-world examples with Python code using spaCy and NLTK

---

### 📌 **Lecture Highlights**

🔤 **Tokenization**

* Word, sentence, and subword tokenization
* Essential for AI models like BERT and GPT

🔄 **Lemmatization**

* Converts "running", "eating", "cats" → "run", "eat", "cat"
* Compared with stemming for accuracy

📌 **POS Tagging**

* Assigns labels like NN (Noun), VBZ (Verb), JJ (Adjective)
* Used in grammar checks, lemmatization, and sentence understanding

🛠️ **Tools Used**

* NLTK for education and rules
* spaCy for fast, industrial-strength NLP
* Real code examples included

📚 **Applications Covered**

* Chatbots, translators, summarization, resume parsers, customer support bots
* NLP in document processing, education, tourism, law, healthcare

---

### 🎓 **Why Watch This Lecture?**

If you’ve ever wondered how AI understands human language, this lecture shows you the **exact steps NLP models follow**. You’ll get a **solid foundation** for building your own chatbots, document analyzers, or even contributing to tools like ChatGPT.

Perfect for **CSVTU students, beginners in AI/ML, Python learners, and interdisciplinary students**, this lecture aligns with your official VAC syllabus.

---

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**#CSVTU, #VAC, #ArtificialIntelligence, #NLP, #Tokenization, #Lemmatization, #POSTagging, #spaCy, #NLTK, #Chatbots, #Translation, #NER, #DependencyParsing, #AIForBeginners, #AIInEducation, #TextProcessing, #NaturalLanguageProcessing, #AIApplications, #PythonNLP, #DocumentAI, #DrHoneyTiwari, #CSVTUAI, #LanguageModels, #BERT, #GPT, #TextAnalytics, #AIinIndia**Show More

CSVTU, VAC, Unit-2: NLP Apps – Chatbots, QA Systems & NER | Lecture 10 | AI Tools & NLP Explained

CSVTU, VAC, Unit-2: NLP Apps – Chatbots, QA Systems & NER | Lecture 10 | AI Tools & NLP Explained

🎓 Lecture 0210: NLP Applications – Chatbots, QA Systems, and Named ...

🎓 Lecture 0210: NLP Applications – Chatbots, QA Systems, and Named Entity Recognition (NER)
👩‍🏫 Instructor: Meetu Tiwari
📚 Course: Artificial Intelligence (Value Addition Course – CSVAC-01)
🏫 Offered by: CSVTU, Department of ...Computer Science
🎯 Credits: 2 | 💡 Level: Beginner-Friendly | ⏱️ Duration: 30 Minutes

🔍 What You’ll Learn in This Lecture
This lecture covers the core NLP applications that make digital assistants, customer bots, and smart search engines work. We’ll dive deep into:

✅ What are Chatbots and how they work
✅ Difference between Rule-Based and AI-Based Chatbots
✅ NLP pipeline for chatbot response generation
✅ Intent Detection and Entity Extraction with examples
✅ How Question Answering (QA) systems retrieve and respond
✅ QA vs Chatbots: Functional Differences
✅ Use of Transformers like BERT and T5 in QA
✅ Introduction to Named Entity Recognition (NER)
✅ Types of entities, NER labels, and algorithms
✅ Use of spaCy, HuggingFace, Rasa, Dialogflow in NLP

📌 Lecture Highlights
💬 Chatbots

Simulate human conversations in real time

Used in education, banking, e-commerce, and support

Built using tools like Rasa, Dialogflow, IBM Watson

❓ Question Answering Systems

Answer user queries from documents and knowledge bases

Use search, context retrieval, and deep learning (BERT, T5)

Power tools like Google Search, customer support bots

🧠 Named Entity Recognition (NER)

Extracts names, places, dates, and more

Algorithms: Rule-based, CRF, BiLSTM, BERT

Used in resumes, contracts, social media, healthcare data

⚖️ Privacy and Ethics in NLP

Data protection laws (like GDPR)

Risks in chatbots and NER exposing sensitive data

Consent, anonymization, and secure deployment practices

🔗 Integration

NLP tools embedded in apps, websites, voice assistants

Supports multilingual queries and domain-specific NLP

🎓 Why Watch This Lecture?
This lecture is essential for anyone building AI-powered user interfaces—from chatbots and helpdesks to intelligent search systems and HR automation.

Whether you're a student, developer, or beginner in AI, you’ll walk away with a strong understanding of how NLP makes machines understand names, questions, and conversations.

🔗 Explore More Courses: https://csvtu.org.in/cg-vac-courses/

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#CSVTU, #VAC, #ArtificialIntelligence, #NLP, #Chatbots, #QuestionAnswering, #NamedEntityRecognition, #IntentDetection, #EntityExtraction, #Dialogflow, #spaCy, #Rasa, #GPT, #BERT, #DeepLearningNLP, #AIAssistants, #CustomerSupportAI, #HuggingFace, #PrivacyInAI, #AIApplications, #Transformers, #QAvsChatbots, #NERinBusiness, #MeetuTiwari, #CSVTUAIShow More

CSVTU VAC Unit-2 Adv. AI, Lecture -11:Speech Recognition, Signal Processing, MFCC & Acoustic Models.

CSVTU VAC Unit-2 Adv. AI, Lecture -11:Speech Recognition, Signal Processing, MFCC & Acoustic Models.

🎓 **Lecture 11: Speech Recognition – Signal Processing, MFCC, Acoustic ...

🎓 **Lecture 11: Speech Recognition – Signal Processing, MFCC, Acoustic Models**
👩‍🏫 **Instructor**: Meetu Tiwari
📚 **Course**: Artificial Intelligence (Value Addition Course – CSVAC-01)
🏫 **Offered by**: CSVTU, Department of Computer Science
🎯 **Credits**: ...2 | 💡 **Level**: Beginner-Friendly | ⏱️ **Duration**: 30 Minutes

---

### 🔍 **What You’ll Learn in This Lecture**

This lecture dives into the **science behind how machines convert spoken words into written text**. You’ll explore the core of speech recognition systems and their real-world impact.

✅ What is **Speech Recognition** and how it works
✅ Concepts of **Signal Processing** and **Fourier Transform**
✅ **MFCC (Mel Frequency Cepstral Coefficients)** – the heart of voice feature extraction
✅ Acoustic Models – GMM-HMM, CNN, RNN, Transformer-based
✅ Language Models – n-grams, LSTM, Transformers
✅ Tools: **Google Speech API, CMU Sphinx, Kaldi, Mozilla DeepSpeech**
✅ Offline vs Online Speech Recognition
✅ Key challenges: Noise, Accents, Real-Time Processing
✅ Use cases in **healthcare**, **mobile**, **assistive tech**, **smart assistants**

---

### 📌 **Lecture Highlights**

🎤 **Speech-to-Text Pipeline**

* From audio input to final text using signal processing and AI
* Covers sampling, MFCC, spectrograms, acoustic & language models

🔊 **Acoustic Models**

* Traditional (GMM-HMM) and modern (CNN, RNN, Transformer) models
* DeepSpeech and Wav2Vec for real-time, end-to-end ASR

💡 **Language Models**

* Predict word sequences, improve context accuracy
* Solve confusion like “ice cream” vs “I scream”

🛠️ **Popular Tools and Libraries**

* CMU Sphinx: Open-source, offline
* Google Speech API: Cloud-based, multilingual
* Kaldi, DeepSpeech, Wav2Vec, PyDub, Librosa

📱 **Applications**

* Smart assistants (Alexa, Siri, Google)
* Voice typing, transcription tools
* Speech interfaces in mobile, healthcare, education

⚖️ **Ethics & Privacy**

* Need for consent, GDPR compliance
* Challenges in sensitive domains like healthcare and law

---

### 🎓 **Why Watch This Lecture?**

If you’ve ever spoken to your phone or used voice commands, you’ve already interacted with **speech recognition AI**. This lecture explains how it all works—technically and practically.

Perfect for **students, developers, educators, and AI beginners**, this session shows how **signal processing + deep learning = real-time voice intelligence**.

---

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### 📢 Don’t Forget to:

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🔔 Subscribe to catch the next lecture on **Text-to-Speech (TTS) and Voice Generation**
Follow us on:
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🐦 X / Twitter: @Meetu_Tiwari_ji

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**#CSVTU, #VAC, #ArtificialIntelligence, #SpeechRecognition, #SignalProcessing, #MFCC, #AcousticModels, #DeepSpeech, #GoogleSpeechAPI, #Kaldi, #CMUSphinx, #Wav2Vec, #FourierTransform, #VoiceAI, #SmartAssistants, #SpeechToText, #VoiceTechnology, #PythonAI, #SpeechPipeline, #SpeechApplications, #MeetuTiwari, #CSVTUAI, #HealthcareAI, #AccessibilityTech, #SpeechSynthesis, #SpeechTools, #AIinIndia**Show More

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Unit 1 Introduction

Title
Overview of Artificial Intelligence, AI, VAC, B.Sc., Free resources, Unit 1
VAC CG, VAC Courses for Chhattisgarh, Artificial Intelligence

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