Machine Learning Course in Noida

Master Machine Learning in Noida with Python, real-world projects, AI tools, expert training, and career-focused practical learning.

Traning Mode

Online / Offline

Duration

2 to 3 Months

4.6 / 5

Google Reviews

100% Interview

Guarantee

Book Free Counseling

Our Students Placed In These Companies

Machine Learning with AI Course Syllabus

MACHINE LEARNING

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 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 Naive’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
Bootstraр
Introduction to Random Forests
Feature Importance in Random Forests
Boosting
How Boosting Algorithm Works?
Adaboost
Gradient Boost
XGBoost

PYTHON AND DATA SCIENCE LIBRARY

Types of NLP - N-Grams

LLM Transformer

Self-Attention Mechanism

How LLMs Work

Applications of LLM Transformers

CURSOR AI + TRAE AI

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

ADVANCE PYTHON PROGRAMMING

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

ITERATORS- GENERATORS

PYTHON AND DATA SCIENCE LIBRARY

MACHINE LEARNING

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 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 Naive’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
Bootstraр
Introduction to Random Forests
Feature Importance in Random Forests
Boosting
How Boosting Algorithm Works?
Adaboost
Gradient Boost
XGBoost

Types of NLP - N-Grams

LLM Transformer

Self-Attention Mechanism

How LLMs Work

Applications of LLM Transformers

CURSOR AI + TRAE AI

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

Where Our Students Are Placed

See All Placements

20+ Machine Learning Tools Covered

Numpy

Pandas

Matplotlib

Seaborn

Jupyter

Colab

Claude

ChatGPT

Scikit learn

Postgre SQL

My SQL

Pytorch

GitHub

Plotly

Tensor Flow

LangChain

LightGBM

Antigravity

Cursor AI

SciPy

TRAE AI

Kubernetes

30+ Machine Learning Skills You Learn

Python Programming
Data Collection
Data Cleaning
Data Preprocessing
Exploratory Data Analysis (EDA)
Statistical Analysis
Probability
Feature Engineering
Feature Selection
Supervised Learning
Unsupervised Learning
Semi-Supervised Learning
Regression
Classification
Clustering
Dimensionality Reduction
Ensemble Learning
Reinforcement Learning
Model Training
Model Evaluation
Hyperparameter Tuning
Model Optimization
Cross-Validation
Model Selection
Predictive Modeling
Time Series Analysis
Anomaly Detection
Natural Language Processing (NLP)
Computer Vision
Deep Learning
Model Deployment
MLOps
Model Monitoring
Problem Solving
Critical Thinking

Why Choose us?

Hybrid Mode Class

Learn flexibly with combined online and offline classroom training sessions.

Internship Certificate

Receive an internship certificate for hands-on industry project experience.

100% Placement Assistance

Complete placement support provided until you secure a suitable job.

CV Building & ATS Score

Optimize your resume for ATS and improve shortlisting chances significantly.

Mock Interviews

Practice with mock interviews to boost real interview performance confidence.

15 Students Per Batch

Small batches ensure personalized attention and better student-teacher interaction.

Our Process

Online Machine Learning Course Brings in the Better Job Opportunities

Gain a competitive edge in the fast-paced world of technology with Machine Learning Training in Noida. In our comprehensive training programs help you adapt to market changes and modernize technology, enabling you to create a range of applications with modern aspects. APPWARS TECHNOLOGIES online courses are designed to be easily accessible and we pride ourselves as the No. #1 leading machine learning training institute in Noida. Our expert trainers will guide you through the course, boosting your self-confidence and ensuring your success. With real-time assistance available throughout the training, completing our course will open doors to better job opportunities. Join us now and explore a better way of life.

Benefits of Online Machine Learning

We conduct machine learning online training in Noida and the benefits are:

Machine learning online training in Noida saves time as there is no need to travel.
Doubt-clearing sessions are available for participants to clear any confusion.
The syllabus covers all the necessary topics for a comprehensive understanding of machine learning.
The training includes both practical and theoretical sessions to ensure a thorough learning experience.

We are dedicated to helping you overcome any concerns or uncertainties you may have about completing the machine learning course successfully. With our expert guidance and support, you can rest assured that you will be able to acquire the necessary skills and knowledge to excel in this field. Let us help you pave the way to a successful career in machine learning.

Why choose our machine learning course?

Here are the reasons to join our machine learning course:

We teach efficient data-handling techniques to help you become proficient in machine learning.
Our training instills confidence in implementing machine learning language in various web applications with positive results.
We provide job assistance to help you find your dream job and lead a worry-free life.

You can take advantage of our machine learning online course in Noida and experience the benefits firsthand. We provide complete course details before enrollment, ensuring no confusion in the future. Join us with confidence and take the first step towards mastering machine learning.

Time to Meet Our Experts

At our institute, we take pride in having expert trainers who can guide you through our machine learning course online. Our trainers are highly experienced and know how to motivate participants to excel in the field of machine learning. We are committed to providing you with the best possible training to make you an expert in handling the language with ease. Our ultimate goal is to help you achieve success in your career and we are always here to provide support whenever you need it. Join us today to experience the difference and explore better job opportunities.

Top Reasons To Choose APPWARS Technologies

MACHINE LEARNING Training in Noida follows IT management standards, ensuring high-quality education.
APPWARS TECHNOLOGIES is a leading training institute providing well-structured courses and dedicated employment services.
We offer flexible options with regular and weekend classes, as well as give assignments after each session to reinforce learning.
Our state-of-the-art lab is provided with updated technology, available for student use 24*7.
Our trainers are certified experts with years of industry experience, dedicated to helping students with project preparation, interview skills, and job placement.
We also provide free personality development sessions, such as English language fluency, mock interview, group discussion, and presentation skills.
We provide free study materials, PDFs, video training, lab guides, exam preparation, sample papers, and interview preparation.
Students are welcome to retake classes without any additional charges as many times as they need.
We specialize in helping students learn complex technical concepts with ease, providing a supportive learning environment.

APPWARS Technologies Trainer for Machine Learning

Our trainers are professional and highly skilled in their field of expertise.
They constantly update themselves with the latest tools and technologies to provide the best training for the real working environment.
Our trainees are carefully selected by our recognized committee for their fieldwork by various organizations over the years.
Our trainees have many years of experience working in big organizations or institutes.
Our trainers are certified and they have at least 7 years of experience in industries as well as training experience.
Our trainees are connected with many companies and placement cells to provide support and help to the students for their placements.

Placement Assistance After Machine Learning

APPWARS TECHNOLOGIES is committed to providing placement assistance to our students and has an assigned placement cell to place them.
The placement cell provides necessary guidelines to students during the placement process to help them to get their dream jobs.
In addition to placement assistance, APPWARS TECHNOLOGIES also offers resume-building services to help students create impressive resume that aligns with the latest industry trends.
Daily personality development sessions, including group discussions, mock interviews, and presentation skills, are conducted to help students present themselves confidently.
APPWARS TECHNOLOGIES is dedicated to helping students achieve their job aspirations by providing extensive training and career support.

APPWARS Technologies Duration for Machine Learning

Regular classes are available 5 days a week with morning, afternoon, and evening batches.
Weekend classes are available on Saturdays and Sundays.
Fast-track classes are available for those who want to complete the course in a shorter duration.
One-to-one classes are available for personalized attention and learning experience.
Corporate training are available for companies who want to train their employees in machine learning.
Live online classes are available for those who prefer to learn from the comfort of their own homes.

Who can apply for the course?

BE / BTech / MCA passed aspirants to make their careers as Web Developers / Data Scientists

IT-Professionals who want to get a career as a Programming Expert

Professionals from non-IT bkg, and want to establish in IT

Candidates who would like to restart their career after a gap

Get Certified to boost your Professional Growth

Recognition Across the Globe

Globally accepted certification to showcase expertise and unlock career opportunities.

Guaranteed Validity for Life

Lifetime-valid certificate ensures lasting credibility and career-long professional recognition.

Special Opportunities for Networking

Join expert community, collaborate, and grow through valuable networking opportunities.

Got more questions?

Talk to our team directly

Student Testimonials

      EXCELLENT  
   Based on 1354 reviews 
    
 
 
 
   
 
 
       Posted on Google  
   
   Vijay Bhandari 
 
 
 
 Trustindex verifies that the original source of the review is Google. Writing this for " Amit Yadav Sir ":
The webinar held on 25-10-2026 on RAG Technology was an excellent learning experience. Sir explained every concept clearly and in detail, demonstrating strong subject knowledge and excellent teaching skills. What impressed me most was his patience and genuine concern for students, as he actively encouraged questions and took the time to resolve our doubts. His dedication and student-friendly approach truly deserve appreciation and recognition. I hope the company acknowledges his efforts   for his outstanding contribution. Keep up the great work!
  
 
      Posted on Google  
   
   atharva rai 
 
 
 
 Trustindex verifies that the original source of the review is Google. I recently completed a comprehensive training session conducted by Ankit Yadav at AppWars Technologies, focusing on Retrieval-Augmented Generation (RAG) and its integration with Generative AI. The session was exceptionally well-structured, bridging the gap between foundational theory and practical implementation.
Ankit is a brilliant instructor who brings immense industry expertise to the table. He has a unique ability to break down complex architectural concepts into highly digestible modules, making it accessible for both beginners and seasoned developers.
  
 
      Posted on Google  
   
   Aarti 
 
 
 
 Trustindex verifies that the original source of the review is Google. Good experience
  
 
      Posted on Google  
   
   Ankush 
 
 
 
 Trustindex verifies that the original source of the review is Google. Very good presentation by sir, he taught us very important knowledge for future. I am greatfull to attend the workshop.

AT D
  
 
      Posted on Google  
   
   vansh Kr 
 
 
 
 Trustindex verifies that the original source of the review is Google. A great Workshop on RAG by Ankit sir , very beginner friendly language and good interaction
  
 
      Posted on Google  
   
   Muskan Malik 
 
 
 
 Trustindex verifies that the original source of the review is Google. Great Experience, we learn alot about Gen AI in Ambedkar institute of technology, Especially thanks to sir for teaching politely and very efficiently.
  
 
      Posted on Google  
   
   Aditya Sharma 
 
 
 
 Trustindex verifies that the original source of the review is Google. Very Good experience and explainatiom
  
 
  
  
 
    Verified by Trustindex  Trustindex verified that these reviews were published on APPWARS Technologies Pvt. Ltd.'s Google Business Profile. Learn more

Frequently Asked Questions

1. What are the prerequisites for enrolling in the Machine Learning training at Appwars Technologies?

There are no strict prerequisites to join our Machine Learning course. However, basic knowledge of Python programming and fundamental mathematics (linear algebra, statistics, probability) is helpful. Our course starts from the basics and gradually progresses to advanced topics.

2. What topics are covered in the Machine Learning training program?

The Machine Learning training at Appwars Technologies covers:Python for Machine LearningData Preprocessing & Feature EngineeringSupervised & Unsupervised LearningModel Evaluation & OptimizationDeep Learning Basics (with TensorFlow/Keras)Real-world Projects & Case StudiesDeployment of ML Models

3. Will I receive a certificate after completing the course?

Yes, upon successful completion of the training program, you will receive an industry-recognized Machine Learning certification from Appwars Technologies, which can boost your resume and job prospects.

4. Does Appwars Technologies provide placement assistance after the course?

Yes, we offer 100% placement assistance, including resume building, interview preparation, mock interviews, and connections with hiring partners in top tech companies across Noida and India.

5. Is this Machine Learning training suitable for beginners and working professionals?

Absolutely! Our training is designed for students, freshers, and working professionals. We offer flexible batch timings, hands-on projects, and expert mentorship to suit different learning needs and schedules.

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Machine Learning with AI Course Syllabus

MACHINE LEARNING

1. Introduction to Machine Learning

2. Supervised Machine Learning

3. Logistic Regression

4. Decision Trees

5. Classification – k Nearest Neighbor (KNN

6. Naive Bayes Classifier

7. Classification SVM (Support Vector Machine)

8. Unsupervised Learning – Clustering

9. Density based clustering

10. Principal Component Analysis – PCА

11. Ensemble Modelling

PYTHON AND DATA SCIENCE LIBRARY

1. NumPy

2. Pandas

3. Matplotlib

4. Seaborn

5. scikit-learn

Types of NLP - N-Grams

1. Unigram (1-Gram)

2. Bigram (2-Gram)

3. Trigram (3-Gram)

Applications of N-Grams

LLM Transformer

1. Encoder

2. Decoder

Self-Attention Mechanism

How LLMs Work

Applications of LLM Transformers

CURSOR AI + TRAE AI

Module 1: Introduction to Modern Al Tools

Module 2: Getting Started with CURSOR AI

Installation & Setup

Understanding Key CURSOR Features

Hands-on with CURSOR AI

Module 3: Introduction to TRAE AI

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

TRAE AI Capabilities

TRAE Al Tools & Platforms

Module 4: Building Al Agents Using TRAE

Agent Fundamentals

Hands-On Agent Development

Real-World Applications

Module 5: CURSOR + TRAE Integration

Module 6: Advanced Al Development Workflow

Module 7: Capstone Project

Module 8: Career Path & Industry Demand

ADVANCE PYTHON PROGRAMMING

1. Introduction to Python

2. Input and Output Functions

3. Variable and Data Types

4. Operators in Python

5. Conditional Statements in Python

6. Loops in Python

7. User defined functions in python

8. Strings in python

9. List

10. Tuple

11. Sets

12. Dictionaries

13. Python Modules

ITERATORS- GENERATORS

Object Oriented Programming

PYTHON AND DATA SCIENCE LIBRARY

1. NumPy

2. Pandas

3. Matplotlib

4. Seaborn

5. scikit-learn

MACHINE LEARNING

1. Introduction to Machine Learning

2. Supervised Machine Learning

3. Logistic Regression

4. Decision Trees

5. Classification – k Nearest Neighbor (KNN

6. Naive Bayes Classifier

7. Classification SVM (Support Vector Machine)