Python DA DS AI ML GenAI
A live, instructor-led program taking you from Python basics to job-ready skills in Data Science, Machine Learning, and Generative AI — with real projects and deployment experience.
check_circle Recordings included check_circle 1:1 doubt solving check_circle Mock interviews
Next cohort enrolling
Taught live
by working pros
1:1 mentorship
never stuck alone
Mock interviews
get job-ready
Placement help
until you land
Overview
About this program.
Students will master Python, SQL, statistics, data analysis and visualization, core and advanced ML algorithms, time series forecasting, deep learning, GenAI with LLMs, prompt engineering, embeddings, vector databases, and RAG. The course culminates in hands-on projects including an ML prediction system deployed on AWS, a Solar Panel Defect Classifier using deep learning, and multiple GenAI applications like a document chatbot, resume analyzer, and Text-to-SQL assistant.
Suitable for freshers, working professionals, software developers, data analyst aspirants, and AI/ML career switchers. By the end, students will be prepared for roles such as Data Analyst, Data Scientist, ML Engineer, AI Engineer, and GenAI Developer.
Duration: 3 Months | Mode: Live Online | Level: Beginner to Job-Ready
Is this you?
Built for people who are ready to start.
Complete beginners
No coding background needed — we start from the fundamentals and build up.
Career switchers
Moving into tech from another field and want a structured, mentored path.
Students & fresh grads
Turn your degree into job-ready, portfolio-backed practical skills.
Working professionals
Level up your stack, fill gaps, and prepare for a better role.
Outcomes
What you'll walk away with.
- check_circle Write Python code confidently for data analysis and machine learning
- check_circle Query and analyze data using SQL for real-world business problems
- check_circle Clean, transform, and prepare datasets using NumPy and pandas
- check_circle Perform Exploratory Data Analysis and create compelling visualizations
- check_circle Apply business statistics, probability, and ML mathematics
- check_circle Build and evaluate supervised and unsupervised machine learning models
- check_circle Use advanced ML algorithms like XGBoost, LightGBM, and ensemble methods
- check_circle Forecast time-series data for sales, finance, and demand planning
- check_circle Understand neural networks and train deep learning models using TensorFlow/Keras
- check_circle Build Generative AI applications using LLMs, prompt engineering, and RAG
- check_circle Work with vector databases like Chroma, FAISS, and Pinecone
- check_circle Use LangChain and LlamaIndex to build LLM-powered tools
- check_circle Deploy ML models as web applications using Flask and AWS
- check_circle Build and deploy a deep learning project using transfer learning on AWS EC2
- check_circle Develop GenAI projects including a document chatbot, resume analyzer, and Text-to-SQL assistant
- check_circle Prepare for Data Science, ML, and GenAI interviews
Curriculum
The syllabus.
01 Module 1: Python Programming for Data Science
- arrow_right_alt Variables, data types, operators
- arrow_right_alt Conditional statements, loops, functions
- arrow_right_alt Data structures, string and file handling
- arrow_right_alt Exception handling, OOP, modules
- arrow_right_alt Mini Project: Student Performance Tracker
02 Module 2: SQL for Data Analytics and Data Science
- arrow_right_alt SQL fundamentals, tables, keys, constraints
- arrow_right_alt Data filtering, sorting, aggregation
- arrow_right_alt Joins, subqueries, CTEs, views
- arrow_right_alt Window functions, SQL with Python
- arrow_right_alt Business analysis and interview practice
03 Module 3: Data Analytics with Python
- arrow_right_alt NumPy and pandas fundamentals
- arrow_right_alt Reading CSV, JSON, SQL data
- arrow_right_alt Data cleaning, missing values, duplicates
- arrow_right_alt Outlier detection, encoding, scaling
- arrow_right_alt Mini Project: Developer Trends and AI Adoption Analysis
04 Module 4: Exploratory Data Analysis and Visualization
- arrow_right_alt EDA techniques and pattern recognition
- arrow_right_alt Correlation and group-based analysis
- arrow_right_alt Matplotlib, Seaborn, and Plotly visualizations
- arrow_right_alt Dashboard-style reporting and storytelling
05 Module 5: Business Statistics, Probability and ML Mathematics
- arrow_right_alt Descriptive and inferential statistics
- arrow_right_alt Probability, Bayes theorem, distributions
- arrow_right_alt Hypothesis testing, p-value, t-test, ANOVA
- arrow_right_alt Linear algebra and gradient descent intuition
06 Module 6: Machine Learning Fundamentals
- arrow_right_alt Supervised and unsupervised learning
- arrow_right_alt Linear Regression, Logistic Regression, KNN, Decision Tree
- arrow_right_alt Random Forest, Naive Bayes, SVM, K-Means, PCA
- arrow_right_alt Model evaluation: accuracy, F1, ROC-AUC, cross-validation
- arrow_right_alt Bias, variance, underfitting, overfitting
07 Module 7: Advanced Machine Learning and Model Optimization
- arrow_right_alt Ensemble methods: bagging, boosting, XGBoost, LightGBM, CatBoost
- arrow_right_alt Hyperparameter tuning with GridSearchCV and RandomizedSearchCV
- arrow_right_alt ML pipelines, imbalanced data handling, feature selection
- arrow_right_alt Model explainability with SHAP and LIME
08 Module 8: Time Series Forecasting
- arrow_right_alt Time series fundamentals: trend, seasonality, noise
- arrow_right_alt ARIMA, Prophet, and ML-based forecasting
- arrow_right_alt Forecast evaluation using MAPE and RMSE
- arrow_right_alt Business interpretation of forecast results
09 Module 9: Deep Learning Fundamentals
- arrow_right_alt Artificial neurons, activation functions, forward propagation
- arrow_right_alt Backpropagation, optimizers (SGD, Adam), epochs, batches
- arrow_right_alt Dropout, early stopping, overfitting in neural networks
- arrow_right_alt TensorFlow/Keras and intro to PyTorch
- arrow_right_alt Neural network project on tabular data
10 Module 10: Generative AI, LLMs, Prompt Engineering, and RAG
- arrow_right_alt LLM basics: tokens, context window, temperature
- arrow_right_alt Zero-shot, few-shot, role-based, and structured prompting
- arrow_right_alt Embeddings, semantic search, vector databases (Chroma, FAISS, Pinecone)
- arrow_right_alt RAG architecture, chunking, retrieval
- arrow_right_alt LangChain, LlamaIndex, tool calling
- arrow_right_alt AI agents, hallucination, guardrails, responsible AI
11 Module 11: ML Project Deployment with Flask, AWS & MLOps Basics
- arrow_right_alt Flask app development and routing
- arrow_right_alt Breast Cancer Prediction model integration
- arrow_right_alt AWS deployment of ML web application
- arrow_right_alt MLOps basics and deployment workflow
12 Module 12: Deep Learning Project - Solar Panel Defect Classification
- arrow_right_alt CNN model training (10 and 20 epochs)
- arrow_right_alt Transfer learning with MobileNetV2 and EfficientNetB0
- arrow_right_alt Hyperparameter optimization and model comparison
- arrow_right_alt Streamlit app conversion and deployment on AWS EC2
13 Module 13: Generative AI Projects
- arrow_right_alt Chat Scholar: EdTech AI Assistant
- arrow_right_alt Resume AI Nova: AI Resume Analyzer and Career Assistant
- arrow_right_alt PDF RAG Chatbot using Web Scraped Data
- arrow_right_alt Text-to-SQL Chatbot
14 Module 14: Interview Prep
- arrow_right_alt ML interview preparation
- arrow_right_alt Deep Learning interview preparation
- arrow_right_alt Generative AI interview preparation
Why live
You've tried learning alone. How'd that go?
Free tutorials
Endless videos, nobody to answer your "why", and no one checking your code. Most people quit.
Recorded courses
You watch alone, on your own willpower, with zero accountability. Completion rates are famously low.
This live cohort
A real mentor answers you in real time, reviews your work, and a cohort keeps you accountable to the finish.
Included with enrollment
Everything you get. One price.
All of the above is included — no add-ons, no upsells, no surprise fees later.
Complete program
$200
Zero-risk enrollment
Enroll with confidence.
Talk before you pay
Message an advisor and get honest answers about the batch and syllabus — no pressure, no hard sell.
Never fall behind
Every class is recorded and yours for life. Miss a session or need a rewatch — it's always there.
Try us free first
Not sure yet? Start with our free interactive courses and see exactly how we teach before you invest.
Your mentor
Learn from someone who does the job.
Naushad Sheikh
Founder, PluralLabs
Before you ask
Questions, answered.
Do I need prior experience?
No. This program is built to take you from the fundamentals to job-ready. If you can use a computer, you can start — and your mentor and 1:1 sessions make sure you never get stuck alone.
What if I miss a live class?
Every session is recorded and uploaded, and you keep lifetime access. You can also bring anything you missed to your 1:1 doubt-solving sessions with the instructor.
What exactly is included in the fee?
Everything listed above — live classes, lifetime recordings, 1:1 doubt solving, study material, reviewed assignments and projects, interview prep, mock interviews and placement support. One price, no hidden upsells.
Will this actually help me get a job?
That's the goal. Alongside the skills, you get interview preparation, mock interviews with real feedback, resume and LinkedIn review, and placement assistance with referrals shared in our community.
Can I talk to someone before I pay?
Absolutely. Message us on WhatsApp and an advisor will walk you through the batch, the syllabus and whether it fits your goals — no pressure.
Not sure yet — is there a free way to try?
Yes. Our interactive courses and coding challenges are free forever. Start there, see how we teach, and join a live cohort whenever you're ready.
Your next batch starts Jul 21.
Seats in a live cohort are limited by design — small groups mean real attention. Reserve yours before this batch fills.
Not ready to enroll yet?
Start with our free interactive courses and coding challenges — no card, no catch.