Courses/Back to Category
    Offline Career Track
    Intermediate
    14 Weeks
    ML foundation builders

    AI / ML

    Learn machine learning the practical way with Python, data preprocessing, model building, evaluation, and a modern introduction to AI tooling and LLM awareness.

    Mode

    Offline Coding Classroom + Dedicated Coding Portal

    Batch Flow

    Weekday batches available • Call to confirm the latest slot

    Know More

    Call +91-9989309198 for the latest timings and admission support.

    Who This Is For

    Python learnersData analytics graduatesAspiring ML engineers
    Python for ML
    Data Preparation
    Machine Learning
    Model Evaluation
    Visualization
    Mini Projects
    AI / ML

    Offline Career Track

    Explore the full curriculum through module-wise content, practical work, and offline classroom guidance.

    COURSE OUTLINE

    Curriculum Timeline

    A full offline learning roadmap with visible content, guided practice, and room to add or remove curriculum items easily.

    Module 1Weeks 1-24 items

    Module 1

    Python & Math Foundations

    Contents

    01

    Python refresher for data workflows

    02

    NumPy arrays, statistics mindset, and linear algebra basics

    03

    Data exploration habits and notebook-based thinking

    04

    Set the groundwork for machine learning practice

    Module 2Weeks 3-44 items

    Module 2

    Data Preparation & Feature Engineering

    Contents

    01

    Pandas cleaning, missing values, encoding, and scaling

    02

    Feature engineering basics and train/test mindset

    03

    Use preprocessing workflows and pipelines

    04

    Prepare usable datasets for ML experiments

    Module 3Weeks 5-74 items

    Module 3

    Supervised Learning

    Contents

    01

    Regression and classification workflows

    02

    Linear models, decision trees, random forests, and ensemble basics

    03

    Model training, tuning, and evaluation metrics

    04

    Build interpretable ML experiments with scikit-learn

    Module 4Weeks 8-94 items

    Module 4

    Unsupervised Learning & Model Evaluation

    Contents

    01

    Clustering, dimensionality reduction, and unsupervised basics

    02

    Cross-validation, overfitting, and model comparison

    03

    Common ML pitfalls and best practices

    04

    Present results clearly using visuals and metrics

    Module 5Weeks 10-124 items

    Module 5

    Applied AI Projects

    Contents

    01

    End-to-end project workflow from data to model to report

    02

    Deployment awareness and model persistence basics

    03

    Domain case studies like churn, forecasting, or recommendation

    04

    Team review and iteration process

    Module 6Weeks 13-144 items

    Module 6

    Modern AI Awareness

    Contents

    01

    Intro to LLMs, prompt engineering, and AI-assisted workflows

    02

    Model ethics, bias, validation, and responsible usage

    03

    Where ML ends and GenAI tools begin

    04

    Plan your next step toward advanced ML or AI engineering

    Projects You Will Build

    Prediction model case studyClustering analysis projectML evaluation reportAI workflow mini capstone

    Tools & Tech Stack

    PythonNumPyPandasMatplotlibscikit-learnNotebook workflow

    Key Takeaways

    • Build a practical understanding of machine learning workflows
    • Train, evaluate, and communicate ML models effectively
    • Understand modern AI trends without skipping core fundamentals
    What You'll Master

    Skills That Help You Build Real Confidence

    Every concept listed here comes directly from the structured curriculum and is taught through classroom explanation plus practical implementation.

    Python refresher for data workflows
    NumPy arrays, statistics mindset, and linear algebra basics
    Data exploration habits and notebook-based thinking
    Set the groundwork for machine learning practice
    Pandas cleaning, missing values, encoding, and scaling
    Feature engineering basics and train/test mindset
    Use preprocessing workflows and pipelines
    Prepare usable datasets for ML experiments
    Regression and classification workflows
    Linear models, decision trees, random forests, and ensemble basics
    Model training, tuning, and evaluation metrics
    Build interpretable ML experiments with scikit-learn
    Clustering, dimensionality reduction, and unsupervised basics
    Cross-validation, overfitting, and model comparison
    Common ML pitfalls and best practices
    Present results clearly using visuals and metrics
    End-to-end project workflow from data to model to report
    Deployment awareness and model persistence basics
    Domain case studies like churn, forecasting, or recommendation
    Team review and iteration process
    Intro to LLMs, prompt engineering, and AI-assisted workflows
    Model ethics, bias, validation, and responsible usage
    Where ML ends and GenAI tools begin
    Plan your next step toward advanced ML or AI engineering

    Our Mission

    What this course is built to do

    The Foundation

    Start strong and stay consistent

    • Start with python & math foundations and build momentum module by module in an offline classroom.
    • Every batch combines concept explanation with guided practice so learners do not get stuck learning alone.
    • Hands-on work starts early with practical builds like Prediction model case study.

    The Journey

    Move toward practical results

    • Move through 6 structured modules with clear milestones and regular lab guidance.
    • Work toward outcomes like build a practical understanding of machine learning workflows and train, evaluate, and communicate ml models effectively.
    • Call +91-9989309198 to get the latest batch timings, fee details, and admission guidance.

    Classroom Screens

    A more premium offline learning experience

    Inspired by premium edtech layouts, these panels highlight how the course feels inside the classroom, lab, and support flow at CAT Computer Point.

    Classroom View
    Snapshot 1

    Mentor-led concept breakdown

    We teach offline with step-by-step explanation first, so every learner understands the idea before jumping into code or tasks.

    Python & Math Foundations
    Data Preparation & Feature Engineering
    Supervised Learning
    Lab Hour
    Snapshot 2

    Practice right after class

    Every course at CAT Computer Point includes guided practice so students can implement, debug, and revise with support in the room.

    Prediction model case study
    Clustering analysis project
    NumPy
    Offline Support
    Snapshot 3

    Batch guidance and real follow-up

    This is designed for an offline institute experience, with direct mentoring, revision help, and a simple call option for course guidance.

    Who it's for: ML foundation builders
    Call: +91-9989309198
    Build a practical understanding of machine learning workflows
    Everything You Get

    Packed With Value, Built For Results

    This is not just a list of topics. It is an offline learning setup designed to help students understand, practice, and actually finish with useful outcomes.

    Offline classroom learning with guided practice
    Structured notes, worksheets, and revision support
    Daily doubt clarification with faculty
    Hands-on project: Prediction model case study
    Portfolio task: Clustering analysis project
    Tool practice with Python
    Batch guidance and follow-up support from CAT Computer Point
    Completion guidance and confidence-building review sessions

    Notes & Practice Support

    Included with your course

    Structured notes, guided lab sheets, and real offline support

    Since CAT Computer Point is offline-based, learners get direct explanation, in-class practice, and instructor-reviewed revision support instead of being left alone with only video content.

    Use your notes, revision points, assignments, and mini projects together so concepts stay fresh between classes and practical confidence keeps improving batch after batch.

    Need details on timings, fees, or the right track? Call +91-9989309198 and we'll help you choose the best batch.