Advanced Machine Learning Training in Noida with Real-Life Projects

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 Compound

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
• SortList 
• Copy List 
• Join List 
• List built-in Method
• n append()
• n count()
• n extend()
• n reverse()
• n sort() 

10. Tuple

• Access Tuples
• Update Tuples
• UnpackTuples
• LoopTuple
• JoinTuples
• Tuplebuilt-in Methods
• n index)
• n count0

11. Sets

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

12. Dictionaries

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

13. Python Modules

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

Object Oriented Programming

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

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 
• ReadCSV
• 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

5. scikit-learn

• Overview of Scikit-learn: Architecture and API design
• Installing and importing Scikit-learn
• Dataset loading utilities (load_iris, load_digits, etc.)
• Working with real-world datasets (using pandas + sklearn)
• Data preprocessing:

Handling missing values (Simplelmputer)

Feature scaling (StandardScaler, MinMaxScaler) 

Encoding categorical features (OneHotEncoder, LabelEncoder) 

• Train-test split and cross-validation (train_test_split, cross_val_score) 

1. Introduction to Machine Learning

• 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 anp 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

• N-Grams are a fundamental concept in Natural Language Processing (NLP) used to analyze and predict sequences of words or characters in text data. An N-Gram is a contiguous sequence of N items (words or characters) from a given text. 

1. Unigram (1-Gram)

• A sequence containing one word.
• Example:

Sentence: “I love Al”

Unigrams:

• I
• love 
• AI

2. Bigram (2-Gram)

• A sequence containing two consecutive words.
• Example:

Sentence: “I love Al”

Bigrams:

• I love
• love AlI

3. Trigram (3-Gram)

• A sequence containing three consecutive words.
• Example:

Sentence: “I love learning Al”

Trigrams:

• love learning
• love learning Al

Applications of N-Grams

• Text prediction
• Auto-complete systems
• Spellchecking
• Machine Translation
• Sentiment Analysis
• Chatbots & Virtual Assistants

• Large Language Models (LLMs) are advanced Artificial Intelligence models designed to understand, generate, and process human language. Modern LLMs such as OpenAI GPT models are built using the Transformer architecture, which revolutionized Natural Language Processing (NLP). 

1. Encoder

• Reads and understands the input text
• Extracts important features and context

2. Decoder

• Generates the output text 
• Predicts the next word based on context 

• The most important concept in Transformers is Self-Attention.
• It helps the model understand:

Which words are important

Relationship between words

Context of the sentence

• Input text is converted into tokens 
• Tokens are converted into embeddings
• Transformer layers process the data
• Attention mechanism identifies context
• Model predicts the next word/token

• Chatbots & Virtual Assistants 
• Machine Translation 
• Text Summarization
• Content Generation 
• Code Generation 
• Sentiment Analysis 
• Question Answering Systems 

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

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

Module 7: Capstone Project

• Al-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