Advanced Python + Machine Learning Course

About Course

5-Day Workshop | 🕒 1–1.5 Hours/Day

Course Content

Day 1: Advanced Python Programming

Recap of Python basics
Functional programming: lambda, map(), filter(), reduce()
Comprehensions: list, dict, set
Modules, packages, and virtual environments
Error handling & decorators
Hands-on: Build a custom Python utility function

Day 2: Data Handling with Pandas & NumPy

Day 3: Introduction to Machine Learning

Day 4: Classification & Clustering Algorithms

Day 5: Real-world Project & Deployment Intro

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INNOVATIVE

RESEARCH CENTER

About Course

Course Content

Day 1: Advanced Python Programming

Recap of Python basics

Functional programming: lambda, map(), filter(), reduce()

Comprehensions: list, dict, set

Modules, packages, and virtual environments

Error handling & decorators

Hands-on: Build a custom Python utility function

Day 2: Data Handling with Pandas & NumPy

Working with large datasets using pandas

Data cleaning: missing values, filtering, transformation

Aggregation and grouping

Numerical computations with NumPy arrays

Hands-on: Exploratory data analysis on a CSV dataset

Day 3: Introduction to Machine Learning

What is ML? Supervised vs Unsupervised Learning

Machine learning workflow

Introduction to scikit-learn

Building a simple ML model (Linear Regression / KNN)

Model training, prediction & evaluation metrics

Hands-on: Build and test a prediction model

Day 4: Classification & Clustering Algorithms

Classification: Decision Tree, Random Forest

Clustering: K-Means basics

Confusion matrix, accuracy, precision, recall

Cross-validation & model improvement

Hands-on: Compare two models using real data

Day 5: Real-world Project & Deployment Intro

Mini-project: End-to-end ML pipeline (data → model → evaluation)

Saving models using joblib or pickle

Introduction to model deployment (basic API with Flask)

Best practices and learning roadmap

Earn a certificate

About Course

Course Content

Day 1: Advanced Python Programming

Recap of Python basics

Functional programming: lambda, map(), filter(), reduce()

Comprehensions: list, dict, set

Modules, packages, and virtual environments

Error handling & decorators

Hands-on: Build a custom Python utility function

Day 2: Data Handling with Pandas & NumPy

Working with large datasets using pandas

Data cleaning: missing values, filtering, transformation

Aggregation and grouping

Numerical computations with NumPy arrays

Hands-on: Exploratory data analysis on a CSV dataset

Day 3: Introduction to Machine Learning

What is ML? Supervised vs Unsupervised Learning

Machine learning workflow

Introduction to scikit-learn

Building a simple ML model (Linear Regression / KNN)

Model training, prediction & evaluation metrics

Hands-on: Build and test a prediction model

Day 4: Classification & Clustering Algorithms

Classification: Decision Tree, Random Forest

Clustering: K-Means basics

Confusion matrix, accuracy, precision, recall

Cross-validation & model improvement

Hands-on: Compare two models using real data

Day 5: Real-world Project & Deployment Intro

Mini-project: End-to-end ML pipeline (data → model → evaluation)

Saving models using joblib or pickle

Introduction to model deployment (basic API with Flask)

Best practices and learning roadmap

Earn a certificate

Student Ratings & Reviews

Company Information

Registered Company Name:

Innovative Research Center

Trade License Number:

TRAD/DNCC/047184/2024

TIN Number:

380164652827