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Course Designed Exclusively

  • Become an AI & ML Expert in Just 6 Month

  • -Perfect for Working Professionals

Course Duration

6–7 Months

Course Syllabus

18+ Modules

0% Interest Installments

3 to 6 Months

Language

English & தமிழ்

Career Growth Opportunities in AI & Machine Learning

Job Role

Data Scientist

Job Role

Machine Learning Engineer

Job Role

Data Analyst /
Business Intelligence Analyst

Job Role

Data Engineer / MLOps Engineer

Job Role

ML Engineer / AI Analyst

Job Role

Data Science Research Associate

Average Salary

₹8.5 LPA

Based on AmbitionBox, Naukri & Glassdoor data

Master 30+ Essential Industry Tools

Python

Pandas

Scikit-learn

Seaborn

NumPy

PyTorch

VectorDB

OpenAI

Matplotib

Kaggle

Power BI

Tableau

MySQL

MongoDB

Docker

Module 01

Introduction to the Programme

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    DS vs ML vs DL vs AI

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    What is AI?

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    The power of AI/ML

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    The limitations of AI/ML

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    Brief about Definitions, Use Cases, Lifecycle

Module 02

Python Fundamentals

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    • Variables, Numbers

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    • Strings Lists, Dictionaries, Sets

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    • Tuples If condition

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    • For loop Functions

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    • Lambda Functions

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    • Modules (pip install)

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    • File Handling (Read/Write)

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    • Exception Handling

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    • Classes & Objects

Module 03

Data Libraries

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    NumPy: Arrays, Functions, Save/Load

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    Pandas: Series, DataFrames, Combining DataFrames

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    Data Manipulation Functions

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    • Saving & Loading Datasets

Module 04

Data Structures & Algorithms (DSA)

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    • Big O Notation

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    • Arrays, Linked List, Hash Table, Stack, Queue

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    • Trees & Graphs

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    • Algorithms: Binary Search, Sorting (Bubble, Quick, Merge)

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    Recursion

Module 05

Mathematics for ML

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    Linear Algebra: Vectors, Matrices, Eigenvalues, SVD

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    Probability: Random Variables, Distributions, Bayes’ Rule, Gaussian

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    Statistics: Descriptive & Inferential, Hypothesis Testing

Module 06

Exploratory Data Analysis (EDA)

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    Data Overview & General Statistics

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    • Univariate, Bivariate, Multivariate Analysis (Histograms, Scatter, Heatmaps, Pairplots, etc.)

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    • Missing Value Treatment

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    • Outlier Detection & Treatment

Module 07

Feature Engineering

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    Handling Missing Values & Outliers

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    • Data Normalisation

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    • Encoding (One-Hot, Label)

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    Feature Selection

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    Train-Test Split, Cross Validation

Module 08

Regression & Classification Models

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    • Scikit-learn ML Workflow

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    • Regression: Linear, Logistic

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    • Evaluation Metrics: MSE, MAE, MAPE

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    • Classification: Accuracy, Precision, Recall, F1 Score, ROC Curve

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    • Confusion Matrix

Module 09

Non-Linear Models (Tree-Based)

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    • Decision Trees

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    • Random Forest

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    • XGBoost

Module 10

Advanced Classifiers

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    • Support Vector Machines (SVM)

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    • K-Nearest Neighbors (KNN)

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    • Naïve Bayes

Module 11

Clustering, PCA & Model Tuning

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    • K-Means Clustering

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    • Principal Component Analysis (PCA)

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    • Hyperparameter Tuning: GridSearchCV, RandomSearchCV

Module 12

Neural Networks & Transfer Learning

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    • Neural Network Basics: Forward/Backpropagation

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    • Building MLP (Sigmoid, ReLU, Softmax, Weights & Biases)

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    • ANN & CNN Basics

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    • Tensors & Operations

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    • Training Neural Networks

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    • Pre-trained Models & Use Case

Module 13

Transformers & Attention

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    • Introduction to Transformers

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    • Components & Architectures

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    • Applications of Transformers

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    • Hugging Face Library Basics

Module 14

LLM Fine-Tuning & RAG

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    • Fine-Tuning & PEFT (Prefix Tuning, Prompt Tuning, QLoRA)

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    • QLoRA Application & Implementation

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    • Introduction to LLMs & Prompt Engineering

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    • Embeddings & Tokenization (BPE, Sentence Embeddings)

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    • Retrieval-Augmented Generation (RAG)

Module 15

AI Agents & APIs

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    • OpenAI APIs

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    • AWS Bedrock

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    • Agent Frameworks

Module 16

Diffusion, DSP & Generative AI

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    • Diffusion Models & Audio Noise Distillation

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    • DSP Pipelines

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    • Image & Audio Synthesis

Module 17

MLOps Fundamentals

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    • Introduction to MLOps

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    • Experiment Tracking with MLFlow

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    • Cloud Familiarity (AWS, Azure, etc.)

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    • Packaging & Running Projects

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    • Model Lifecycle Management

Module 18

Full Stack AI Deployment

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    • FastAPI Deployment

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    • Model Monitoring

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    • Gradio & Streamlit Basics

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    • Backend + Frontend Integration

This Course is Designed For

Working IT Professionals

Upgrade your skills to AI & ML, stay relevant, and move toward high-growth tech roles.

Non-IT Professional

Shift into the AI & ML space with a guided learning path and practical hands-on training.

Freelancers & Tech Pros

Add AI solutions to your service offerings, automate workflows, and increase your earning power.

Job Seekers

Build strong fundamentals, projects, and portfolio to confidently start your career journey.


Get your industry-verified NASSCOM certificate on completion

See How Uptor Transforms Careers – Before vs. After

Before Joining

I don’t know where to start in data science and ML

Data tools (Python, SQL, notebooks)

  • Too many scattered tutorials, no clear roadmap

  • My career growth is stuck with no portfolio

  • I’m unsure which DS/ML roles fit me

  • Job switch feels risky without proof of skills

  • Lots of data, no plan to turn it into insights

After Completing

Step-by-step roadmap for Data Science with ML

  • Confident with Python, SQL, Pandas, visualization

  • Build production-style ML projects end-to-end

  • Solid portfolio: EDA → features → models → metrics

  • Ready to apply for DS/ML roles with clarity

Future-proof skills: ML, GenAI basics, and MLOps i

  • More interviews and better opportunities

Frequently Asked Questions

Answers to common questions about our AI & ML Course

Do I need prior coding?

Basic programming knowledge helps, but it’s not mandatory. We start with a Python & fundamentals fast-track module to support beginners and returning professionals.

Is this course suitable for working professionals?

Yes — this program is designed for working IT professionals and career switchers. With 2 live doubt-clearing sessions per week + recordings.

Is it live or recorded?

Recorded lessons to learn at your pace, 2 weekly live doubt-clearing sessions, Mentor support + community access.

Will I get a certificate?

Yes. You will receive an industry-recognized AI & ML certificate jointly from Uptor & LMES.

Do you provide placement Guarantee?

We do not promise guaranteed jobs — we believe in skill-based success.

However, we support you with:

✅ Resume & LinkedIn optimization
✅ Interview preparation

How is this different from IIT/Big brands?

Smaller cohort, bilingual clarity, hands-on RAG/Agents/MLOps, heavy code feedback.

What if I am from a Non-IT background?

You are welcome. We have a structured learning path, beginner modules, and mentor support to help non-tech learners transition into AI & ML.

How much time should I spend weekly?

On average: 6–10 hours per week
Perfect for working professionals & weekend learners.

What are the tools & tech I will learn?

You will learn Python, NumPy, Pandas, scikit-learn, PyTorch, TensorFlow, Hugging Face, MLflow, Evidently, LangChain, FastAPI, Vector DBs, RAG pipelines, and more.

What language is the course taught in?

The course is taught in Tamil + English for clarity and faster learning.

What is the course duration?

The program runs for 6–8 months with continuous support.

How many seats are available?

We accept only 30–40 students per cohort to provide personal mentorship and support.

Do you help with real-world projects?

Yes. You will build multiple real AI projects including:

  • RAG applications

  • AI agents

  • ML models

Still confused? You're not alone — Let's fix it together