data science course in Noida with AI

1. Introduction to Python

• Simple 
• Open Source
• High Level Programming
• Portable
• Object and Procedure Oriented
• Easy to Maintain

2. Input and Output Functions

• The Input() function
• The print() function

n use of % percent operator

n use of format()

3. Variable and Data Types 

• What is a Variable
• Assign Values to variable
• Typecasting
• Data Types in Python 

n Numeric

n String

n Boolean

n Componup

n List

n Tuple

n Set

n FrozenSet

n Dictionary

4. Operators in Python

• Types of Operators 

n Arithmetic Operators

n Comparison Operators

n Assignment Operators

n Logical Operators

n Bitwise Operators

n Identity Operators

n Membership Operators

n Operators Associativity

n Operator Precedence

n BEDMAS

5. Conditional Statements in Python

• The if Statement
• if- else statement
• The elif Statement
• Nested if-else ladder

6. Loops in Python

• Use of while loop
• use of for loop
• range) function
• arange) function
• The break Statement
• The continue Statement
• The pass statement

7. User defined functions in python

• Define a function
• calling a function
• Types of function
• UDF
• Function Arguments
• Functions Parameters
• Anonymous Function
• Global and Local Variable
• lambda
• map
• reduce
• Filter
• Mathematical Function 
• Trigonometric Function
• Random Function

8. Strings in python

• creating strings 
• difference between “” &”
• creating multiline comment 
• basic string operations
• creating slices in strings
• String Built-in Functions
• n capitalize()
• n upper()
• n lower()
• n isalnum()
• n isalpha()
• n isnumeric()
• n isdecimal()
• n islower()
• n isupper()

9. List

• Access List items 
• Change List items 
• Add List Items 
• Remove List Items 
• Loop List 
• List Comprehension 
• Copy List
• Join List
• List built-in Method
• n append()
• n count()
• n extend()
• nreverse()
• n sort()

10. Tuple

• Access Tuples 
• Update Tuples
• UnpackTuples
• LoopTuple
• JoinTuples
• Tuplebuilt-in Methods
• n index()
• n count()

11. Sets

• Create Sets
• Access Set items
• Add Set items
• Remove Set items
• Loop Sets
• Join Sets

12. Dictionaries

• Create Dictionaries
• Access Dictionary items
• Change Dictionary items 
• Add Dictionary items 
• Remove Dictionary items 
• Loop Dictionaries
• Copy Dictionaries
• Nested Dictionaries
• Dictionary built-in Methods 
• n keys()
• n values()
• n items()
• n get()

13. Python Modules

• Date Module 
• Time Module
• os Module
• The import statement
• The from…import Statement

14. File Handling in Python

• Readfiles 
• Write/ Create files
• Delete files
• Rename files

15. Exception Handling

• Error in Python Program 
• Syntax error 
• Exception
• Types of Exceptionvelopment
• Handling Exception in Python
• Raising Exception
• User Defined Exception

16. Regular Expression

• Match function
• Search function
• Matching VS Searching
• Modifiers
• Patterns

Object Oriented Programming

• Class 
• Object
• Inheritance
• Polymorphism
• Overloading
• Overriding
• Abstraction
• Encapsulation 
• Web scraping 

1. MySQL

• Introduction 
• MySQL Data Types
• Creating Databases in MySQL
• Some Useful Operations on MySQL Databases
• CreatingTables in MYSQL
• MYSQL Table Commands
• Using ALTER command in MySQL
• Using DESCRIBE in MYSQL
• Using TRUNCATE in MySQL
• Using DROP in MySQL
• ALTER Command in MySQL
• Sample Queries in MySQL
• Constraints in MySQL
• Using INSERT command in MySQL
• Using UPDATE command in MySQL
• Using DELETE command in MySQL
• SELECT Queries in MySQL
• Using REPLACE command in MySQL
• JOINS in MYSQL
• RIGHT JOINS in MySQL
• LEFT JOINS in MySQL
• INNER JOINS in MySQL
• LEFTJOINS vs RIGHTJOIN in MySQL 
• Primary Keys in MySQL 
• FOREIGN KEYS in MySQL 

2. Jupyter Overview

• Updates to Notebook Zip 
• Jupyter Notebooks 
• Use of Google Colab 

3. Git & GitHub

• GitHub Introduction 
• GitHub Edit Core
• Pull from GitHub
• Push to GitHub
• GitHub Branch

1. NumPy

• Creating arrays 
• Array indexing
• Array slicing
• Numpy Data Types
• Copy vs View
• Array Shape
• Array reshape
• Iterating
• Join
• Search
• Filter
• Split
• Sort

2. Pandas

• Pandas Series 
• Pandas Data Frame 
• Read CVS
• ReadJSON
• Cleaning Data
• Missing Value Handling 
• Optimizing Data Format
• Redundancy Minimization 
• The corr() function 
• Plotting Graphs in pandas 

3. Matplotlib

• Welcome to the Data Visualization Section 
• Introduction to Matplotlib
• Matplotlib Part 1
• Matplotlib Part 2
• Matplotlib Part 3

4. Seaborn

• Introduction to Seaborn
• Distribution Plots
• Categorical Plots
• Matrix Plots
• Grids
• Regression Plots
• Style and Color

• Probability distribution 
• Normal distribution
• Poisson’s distribution
• Descriptive Statistics 
• Inferential Statistics 
• Bayes’ theorem
• Central limit theorem 
• Hypothesis testing 
• One Sample T-Test 
• Anova and Chi-Square 

1. Introduction To Machine Learning 

• Importance Of Machine Learning 
• How did Machine Learning work? 
• Relationship Between Artificial Intelligence, Machine Learning, and Data Science 
• Different Categories of Machine Learning: Supervised, Unsupervised and Reinforcement 
• Applications of Machine Learning 

2. Supervised Machine Learning

• Linear Regression 
• Simple Linear Regression 
• Multiple Linear Regression
• Performance Metrics: Accuracy Scores, R^2 And Adjusted RA2
• To Compare the Model with Different Numbers of Independent Variables.
• Approaches to Feature Selection: Univariate Selection, Feature Importance, RFE
• Parameter Tuning and Model Evaluation
• Data Transformation and Normalization
• Ridge & Lasso Regression (L1 & L2)

3. Logistic Regression

• Concept of Logistic Regression 
• Univariate Logistic Regression 
• Multivariate Logistic Regression: Model Building and Evaluation 
• Dealing with Categorical Independent Variable – Hot Encoding Vs Dummy Variable 

4. Decision Trees

• Concept of Decision Trees 
• Importance and Usage of Ginny And Entropy 
• Information Gain 
• Visualizing Decision Trees Nodes and Splits
• Working of The Decision Tree Algorithm
• Extending Decision Trees to Regressing Problem
• Evaluating Decision Trees Models: Accuracy, Precision, Recall, Confusion Matrix

5. Classification – k Nearest Neighbor (KNN)

• Classification and Regression 
• Application, Advantages and Disadvantages 
• Distance Metric – Euclidean, Manhattan, Chebyshev, Minkow 
• Measuring accuracy using Cross-Validation, Stratified k-fold, Confusion Matrix,Precision, Recall, Fl-score. 

6. Naive Bayes Classifier

• The Bayes Theorem 
• The Gaussian Naïve’s Bayes Classifier – Assumptions of The Naïve Bayes Classifier, Functioning of The Naïve’s Bayes Algorithm

7. Classification SVM(Support Vector Machine)

• Classification and Regression
• Separating line, Margin and Support Vectors
• Linear SVC Classification
• Polynomial Kernel – Kernel Trick
• Gaussian Radial Basis Function (RBF)
• Grid Search to tune hyper-parameters
• Support Vector Regression

8. Unsupervised Learning – Clustering

• Introduction to Clustering 
• K-Means Clustering: Linkage 
• Use of Elbow Curve & Silhouette Score
• Hierarchical Clustering – Agglomerative & Divisive
• Distance Matrix, Dendrogram

9. Density based clustering

• DBSCAN (Density based clustering) Technique
• Density based clustering
• Eps: Density
• Core, Border and Noise points
• Density edge and Density connected points
• DBSCAN Algorithm
• Hyper Parameters: MinPts and EpsA
• Advantages and Limitations of DBSCAN

10. Principal Component Analysis – PCА

• Noise in The Data and Dimensionality Reduction
• Capturing Variance – The Concept of Principal
• Components
• Assumptions in Using PCA
• Eigenvectors and Orthogonality of Principal Components
• What Is the Complexity Curve?
• Advantages of Using PCA
• The Working of The PCA Algorithm
• Singular Value Decomposition

11. Ensemble Modelling

• Popular Ensembles
• Bagging
• Bootstrap
• Introduction to Random Forests
• Feature Importance in Random Forests
• Boosting
• How Boosting Algorithm Works?
• Adaboost
• Gradient Boost 
• XGBoost 

1. Introduction to Deep Learning

• What is Deep Learning? 
• Why Deep Learning? 
• Relationship Between Al, Machine Learning, and Deep Learning 
• History and Evolution of Deep Learning 
• Challenges Motivating Deep Learning
• Key Applications of Deep Learning

2. Artificial Neural Networks (ANN

• Importance of ANN in Al & Machine Learning 
• Biological Inspiration: How ANN Mimics the Human Br 
• Input Layer: Receiving raw data
• Hidden Layers: Processing and feature extraction
• Output Layer: Generating predictions
• Activation Functions: ReLU, Sigmoid, Tanh, Softmax
• Training and Optimization in ANN
• Forward and Backpropagation
• Loss Functions in ANN (MSE, Cross-Entropy)
• Gradient Descent & Optimization Algorithms (SGD, Adam,RMSprop 
• Overfitting & Regularization (Dropout, Batch Normalization)
• Applications of ANN
• Image & Speech Recognition
• Natural Language Processing (NLP)
• Financial Market Forecasting
• Healthcare & Disease Prediction
• Implementing ANN in Python
• Using TensorFlow & Keras
• Building & Training an ANN Model
• Evaluating Model Performance

3. Convolutional Neural Networks (CNN)

• What is a CNN?
• Why CNNs for Image Processing?
• How CNNs Differ from Traditional Neural Networks
• Architecture of CNN
• Convolutional Layer: Feature extraction
• Pooling Layer: Dimensionality reduction (Max Pooling, Average Pooling)
• Fully Connected Layer: Classification and output
• Activation Functions: ReLU, Softmax
• Types of CNN Architectures
• LeNet-5 – Early CNN model
• AlexNet – Breakthrough in deep learning
• VGGNet – Deeper architectures
• ResNet – Solving vanishing gradient problem
• InceptionNet – Multi-scale feature learning
• Training a CNN
• Data Augmentation for Better Performance
• Loss Functions for CNN (Categorical Cross-Entropy, MSE)
• Optimization Techniques (Adam, SGD)
• Applications of CNN 
• Image Classification: Face Recognition, Object Detection 
• Medical Image Analysis: Disease Detection (X-rays, MRIs)
• Autonomous Vehicles: Lane detection, Pedestrian recognition
• Augmented Reality & Deepfake Technology
• Implementing CNN in Python
• Using TensorFlow & Keras
• Building a CNN Model for Image Classification

4. Recurrent Neural Networks (RNN)

• What is an RNN? 
• Why RNNs for Sequential Data?
• Difference Between RNN and Traditional Neural Networks
• Architecture of RNN
• Input, Hidden, and Output Layers
• Recurrent Connections: Handling time-series & sequential data
• Backpropagation Through Time (BPTT)
• Types of RNN Architectures 
• Basic RNN – Simple recurrent structure
• Long Short-Term Memory (LSTM) – Solving the vanishing gradient problem
• Gated Recurrent Unit (GRU) – Efficient alternative to LSTM
• Bidirectional RNN (Bi-RNN) – Learning from past and future context
• Applications of RNN

5. Natural Language Processing (NLP) with Deep Learning

• Introduction to NLP 
• What is Natural Language Processing? 
• Importance of NLP in Al 
• Key NLP Concepts 
• Tokenization, Stemming, and Lemmatization
• Stopwords & Named Entity Recognition (NER)
• POS Tagging & Chunking
• Bag of Words (BoW) & TF-IDF
• Deep Learning for NLP
• Word Embeddings (Word2Vec, GloVe, FastText)
• Recurrent Neural Networks (RNNs) for NLP
• Transformers & BERT for NLP
• Attention Mechanisms in NLP
• Applications of NLP
• Chatbots & Virtual Assistants
• Text Classification & Sentiment Analysis
• Speech-to-Text & Text-to-Speech Conversion
• Implementing NLP Models
• Using NLTK, Spacy, TensorFlow, and Transformers
• Sentiment Analysis & Text Generation

6. Deep Learning with Autoencoders

• What are Autoencoders? 
• Importance in Deep Learning
• Applications of Autoencoders
• Architecture of Autoencoders
• Encoder: Extracting meaningful features
• Latent Space: Compact representation
• Decoder: Reconstructing the original data
• Types of Autoencoders
• Vanilla Autoencoder: Basic architecture
• Denoising Autoencoder: Removing noise from data
• Sparse Autoencoder: Learning useful representations
• Variational Autoencoder (VAE): Generative modeling
• Convolutional Autoencoder (CAE): Image processing applications
• Applications of Autoencoders 
• Data Compression & Feature Extraction
• Anomaly Detection
• Image Denoising
• Generative Modeling
• Implementing Autoencoders in Python
• Using TensorFlow & Keras

7. Computer Vision with Deep Learning

• What is Computer Vision?
• The Evolution of Computer Vision
• Importance and Real-World Applications
• Key Concepts in Computer Vision
• Image Processing vs. Computer Vision
• Understanding Pixels and Image Representation
• Feature Extraction and Object Recognition
• Techniques in Computer Vision
• Image Processing: Filters, Edge Detection, Histogram Equalization
• Feature Detection & Matching: SIFT, SURF, ORB
• Object Detection: YOLO, SSD, Faster R-CNN
• Image Segmentation: Semantic & Instance Segmentation
• Role of Convolutional Neural Networks (CNNs)
• Transfer Learning for Computer Vision
• Generative Adversarial Networks (GANs) for Image Generation
• Autoencoders for Image Compression
• Applications of Computer Vision
• Healthcare: Medical Image Analysis (X-rays, MRIs)
• Autonomous Vehicles: Object Detection & Lane Detection
• Security: Facial Recognition & Surveillance
• Implementing Computer Vision in Python ES
• OpenCV for Image Processing Con
• Real-time Object Detection with YOLO 

• Introduction to Generative Al 
• What is Generative Al & How it Works?
• Key Features of GenAl
• Introduction to Large Language Models (LLMs)
• What are LLMs?
• How LLMs Work? (Transformers, Self-Attention Mechanism)
• Popular LLMs & Their Applications
• GPT (ChatGPT), BERT, LLaMA, Claude
• Applications in Chatbots, Content Generation, Code Assistance, Al Art
• Future of Generative AI & LLMs
• Al Ethics & Responsible Al
• Challenges & Advancements in LLMs

Module 1: Introduction to Modern Al Tools

• What is Al-first Development? 
• Evolution of Al Coding Assistants 
• Overview of CURSOR AI & TRAE AI
• How developers use these tools in real-world projects 

Module 2: Getting Started with CURSOR AI

Installation & Setup

• Installing Cursor Editor
• Integrating GitHub/GitLab
• Setting environment, extensions & APIs

Understanding Key CURSOR Features

• Smart Al Pair Programming
• Code Autocomplete & Refactoring
• Al Chat for Code Explanations
• Multi-file Project Understanding
• Cursor Composer Mode
• Auto debugging assistance
• Using Cursor for test-case generation

Hands-on with CURSOR AI

• Building your first Al-assisted program 
• Real-time code optimization 
• Converting requirements into working code
• Fixing bugs with Al suggestions
• Al-generated documentation

Module 3: Introduction to TRAE AI

What is TRAE (Track-Reason-Execute) AI?

• TRAE architecture overview 
• How TRAE agents understand and break tasks 
• Difference between standard LLM vs TRAE-based systems 

TRAE AI Capabilities

• Task tracking & workflow automation
• Multi-step reasoning
• Agent collaboration
• Automated execution with human oversight

TRAE Al Tools & Platforms

• TRAE SDK basics 
• Agent Studio Platforms 
• Integration with APIs & external tools 

Module 4: Building Al Agents Using TRAE

Agent Fundamentals

• Agent roles & responsibilities 
• Reasoning graph 
• Memory handling 
• Decision-making flow 

Hands-On Agent Development

• Creating your first TRAE-based agent
• Connecting agent with external APIs
• Automating data extraction & reporting
• Building a task automation pipeline

Real-World Applications

• Chatbots & virtual assistants
• Automated customer service flows
• Sales & lead management automation 
• Content creation & research automation 

Module 5: CURSOR + TRAE Integration

• How CURSOR helps build TRAE agents 
• Auto-code workflows for Al automation
• Full-stack agent development
• Automating testing & deployment using Al

Module 6: Advanced Al Development Workflow

• Multi-agent systems 
• Prompt engineering for agents 
• Workflow design patterns
• Long-term memory integration
• Secure Al development principles

Module 7: Capstone Project

• AI-powered chatbot
• Automated data analytics agent
• Code generation assistant
• Workflow automation system

Module 8: Career Path & Industry Demand

• Jobs in Al Agent Development 
• Using CURSOR & TRAE skills in MNC interviews 
• Portfolio building 
• Resume + GitHub project guidance