Machine Learning Certification: From Data to Deployment

Machine Learning Mastery: From Data to Deployment is a career-aligned, project-driven certification program that takes learners from the fundamentals of machine learning to real-world model deployment. Whether you're a beginner or someone looking to formalize your skills, this 16-day course delivers a practical and industry-relevant path into the world of ML.
 
With hands-on projects, mentor-led live sessions, and tools like Python, Scikit-learn, Pandas, and cloud deployment, you'll build both the confidence and the portfolio to step into a machine learning role.
 
Who Is It For?
 
This course is ideal for:
Beginners with a basic understanding of Python and math
Data enthusiasts who want to move into predictive modeling and ML
Working professionals seeking a structured ML learning path
Students or graduates in engineering, science, math, or analytics fields
 
What Will You Get?
 
Complete ML Workflow Training: From data preprocessing to model building, tuning, evaluation, and deployment
Live Projects & Real Datasets: Predictive modeling in domains like healthcare, marketing, and finance
Toolset Mastery: Python, Pandas, Scikit-learn, Jupyter, Flask, and basics of cloud platforms
Mentor Guidance: Expert-led live classes with personalized feedback
Soft Copy of Certification: Industry-recognized and LinkedIn-shareable
 
Post-Certification Benefits:
 
Interview-Ready Projects: Present fully built ML models with business context and metrics
Credibility with Recruiters: Certification from a leading edtech brand
Confidence to Crack ML/DS Interviews: Learn how to speak the language of ML fluently
 
TOC: Curriculum Overview
 
Module 1: Introduction to Machine Learning
What is Machine Learning?
Supervised vs Unsupervised Learning
Use Cases Across Industries
Module 2: Python for ML Refresher
Numpy, Pandas, and Matplotlib basics
Exploratory Data Analysis (EDA)
Data Cleaning and Preprocessing
Module 3: Supervised Learning Fundamentals
Linear & Logistic Regression
Decision Trees and Random Forest
Evaluation Metrics: Accuracy, Precision, Recall, F1
Module 4: Unsupervised Learning Essentials
K-Means Clustering
Dimensionality Reduction (PCA)
Anomaly Detection
Module 5: Feature Engineering & Model Tuning
Handling Missing Data & Outliers
Encoding, Scaling, and Transformation
Hyperparameter Tuning with GridSearchCV
Module 6: End-to-End ML Pipeline
Building Full Pipelines in Scikit-learn
Train-Test Splits, Cross-Validation
Model Selection Strategy
Module 7: Intro to Deep Learning (Bonus)
Understanding Neural Networks
Basic implementation with TensorFlow or Keras
Use Case: Image or Text Classification
Module 8: Model Deployment
Saving Models with Pickle/Joblib
Creating Web APIs using Flask
Introduction to Deployment on Streamlit or Heroku
Module 9: Capstone Projects
Build a real-world ML project with business context
Domains: Healthcare prediction, Customer Churn, Sales Forecasting
Live mentor feedback & walkthrough
 
Certification:
 
You will receive a digital certificate upon course completion that you can showcase on LinkedIn, add to your resume, or share with employers.
 
Course Duration & Schedule:
 
🗓 Total Duration: 24 Days
🕒 Schedule: Weekend Only
📅 Timings: Saturday & Sunday – 2 Hours per Day
📍 Mode: Live Online Classes (Interactive + Mentor-led)