Key Points

Supervised & Unsupervised Learning
Model Evaluation & Tuning
Python for ML
Data Preprocessing & EDA
Real-World Projects
Scikit-learn, NumPy & Pandas
Neural Networks Basics
Hands-on Model Building
Deployment with Flask/Streamlit

Training Key Features

Project-Based Hands-on Training

Work on real-world machine learning projects to apply algorithms, preprocessing, and deployment techniques effectively.

5+ Real-World Case Studies

Analyze and solve industry-relevant problems using machine learning and data science methodologies.

Deployment & Model Integration

Learn how to deploy ML models using Flask, Streamlit, or cloud services to bring models into real applications.

Job-Focused Resume & Portfolio Building

Craft professional resumes and ML portfolios that showcase your practical knowledge and project experience.

Interview Preparation & Mock Sessions

Get prepared with mock interviews, technical questions, and tips for ML-specific interview rounds.

Guidance from ML Industry Experts

Learn from professionals working in AI & ML domains with years of hands-on and teaching experience.

Machine Learning Training Key Features

Prerequisite

No prior machine learning experience is required.

Basic understanding of Python programming and mathematics (linear algebra, statistics) is beneficial but not mandatory.

This course is ideal for students (B.E/B.Tech/B.Sc/BCA/MCA), graduates, postgraduates, or working professionals looking to build a career in Machine Learning, Data Science, or AI domains.

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+91 7887885052

  • Python Programming
  • Jupyter Notebook
  • NumPy
  • Pandas
  • Matplotlib / Seaborn
  • Scikit-learn
  • Google Colab
  • Git & GitHub Basics

  • Supervised Learning
  • Unsupervised Learning
  • Model Evaluation & Metrics
  • Feature Engineering
  • Regression & Classification
  • Clustering Techniques
  • Train/Test Split, Cross Validation
  • Hyperparameter Tuning

  • Introduction to Deep Learning
  • Neural Networks Basics
  • TensorFlow / Keras Basics
  • Model Deployment using Flask/Streamlit
  • End-to-End Project Building
  • Working with Real Datasets
  • Capstone Project
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