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Start your Python + AI journey in 12 weeks.

From zero Python to your first machine-learning models. Perfect for beginners and career switchers — mentor-led, project-based, and built to feed straight into our advanced ML and AI tracks.
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0% EMI availablePlacement guaranteeHands-on labs included
Python for AI Track
Only 8 seats left
  • 12 weeks · live mentor-led
  • 40+ hands-on labs & notebooks
  • 3 mini-projects + 1 capstone
  • Weekly 1:1 with your mentor
  • Beginner friendly — no prior code
  • Lifetime placement support
    • Book free counselling call
    30-min call · no pressure · get a personal roadmap
    12 wks
    Cohort length
    40+
    Labs
    9.4/10
    Alumni rating
    88%
    Placement rate

    Alumni working at

    DNMPramataResponsiveSocial Bytes Pvt LtdUPLDMG
    Outcomes

    What you'll be able to do at the end of this course

    Every module ends with a graded lab that mirrors what teams actually ship. No slides-only theory.

    Write clean, idiomatic Python with functions, classes and modules.

    Manipulate data confidently using NumPy and Pandas.

    Visualise data with Matplotlib and Seaborn.

    Build and evaluate your first regression and classification models.

    Use Jupyter notebooks and virtual environments professionally.

    Read CSV, JSON and API data end-to-end into an ML workflow.

    Understand overfitting, train/test splits and cross-validation.

    Ship a portfolio-ready mini ML project to GitHub.

    12-week curriculum

    Five modules. Absolute beginner to first ML model.

    Get the detailed syllabus PDF
    1. Module 01
      Weeks 1–3

      Python Foundations

      The language, done properly.

      • Syntax, variables, control flow
      • Lists, dicts, tuples, sets
      • Functions & scope
      • OOP basics
      • File I/O & exceptions
    2. Module 02
      Weeks 4–5

      Data Handling

      NumPy and Pandas for real datasets.

      • NumPy arrays & broadcasting
      • Pandas DataFrames
      • GroupBy, merges, pivots
      • Cleaning missing data
    3. Module 03
      Weeks 6–7

      Visualisation & EDA

      See your data before you model it.

      • Matplotlib fundamentals
      • Seaborn statistical plots
      • Exploratory Data Analysis workflow
      • Storytelling with charts
    4. Module 04
      Weeks 8–10

      First ML Models

      The scikit-learn essentials.

      • Regression & classification
      • Train/test split & CV
      • Decision Trees & Random Forest
      • Model evaluation metrics
    5. Module 05
      Weeks 11–12

      Capstone & Portfolio

      Ship something you can show.

      • End-to-end mini ML project
      • GitHub + README polish
      • Resume + LinkedIn review
      • Interview prep basics
    Mandatory capstone labs

    Projects you'll actually ship

    Lab 01

    Movie Ratings EDA

    Load a movie dataset, clean it, and produce a set of insight-driven visualisations you can walk through in an interview.

    Lab 02

    House Price Predictor

    Train a regression model on housing data, tune it, and evaluate error with proper cross-validation.

    Lab 03

    Customer Churn Classifier

    End-to-end classification: EDA, feature engineering, model training, evaluation and a clean notebook write-up.

    2,500+
    Beginners taught
    150+
    Hiring partners
    8 yrs
    Training experience
    15
    Max cohort size
    Student stories

    Real placements. Real hikes. Real portfolios.

    "I had never written a line of code. Twelve weeks later I built a working ML model and got my first data role."
    P
    Pooja Verma
    Junior Data Analyst
    First tech job
    "The pace was perfect for a beginner. Mentors never made me feel behind."
    A
    Amit Kumar
    Analyst · retail
    Career switch in 4 mo
    "Rolling straight into the Deep Learning track after this felt effortless."
    N
    Neha Singh
    ML Student
    Track upgrade
    "The capstone project became the first thing I show recruiters. It actually works."
    V
    Vikas Rao
    Data Analyst
    ₹6 LPA offer
    FAQ

    Everything parents & partners ask

    I have zero coding experience — is this for me?

    Yes. This track is designed for absolute beginners. Half our alumni had never written code before joining.

    What can I do after this course?

    You'll be ready for a junior data / analyst role, or to enrol in our AI-ML, Deep Learning or GenAI tracks.

    Is it online or in-person?

    Hybrid. Live weekday classes online, weekend labs and doubt-clearing at our Chandra Layout campus in Bengaluru (or fully online).

    What software will I need?

    Just a laptop. We use Python, Jupyter, VS Code and Google Colab — all free.

    Is there placement support?

    Yes — resume rewrites, mock interviews and referrals to 150+ hiring partners.

    Are EMIs available?

    Yes — 0% interest EMIs across 3, 6 and 9 months. Scholarships for women returnees and tier-3 students.

    Next cohort · Aug intake

    Your Data Engineering career starts with a 30-min call.

    Talk to a mentor. Get an honest read on your background, a personalized roadmap, and full fee + EMI details. No pressure.

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