Python Data Science and Machine Learning: Zero to Production

Data Science and Machine Learning are transforming every industry, and there has never been a better time to build these skills. Whether you are a student, software developer, data analyst, or working professional, this course will take you from the fundamentals to building and deploying real machine learning applications.

This is not a course that only explains algorithms with slides. Instead, you'll learn by building practical projects and understanding why each concept matters in the real world. Every topic is explained in a simple, beginner friendly way with hands on coding examples.

By the end of this course, you'll have the confidence to analyze data, build machine learning models, evaluate their performance, and deploy them as production ready REST APIs using FastAPI and Docker.

What you'll learn

You will build a strong foundation in Data Science and Machine Learning, including:

• Python programming for Data Science

• Essential Mathematics and Statistics for Machine Learning

• Data Visualization using popular Python libraries

• Exploratory Data Analysis (EDA) with a real-world movie streaming customer churn dataset

• Data Cleaning and Feature Engineering techniques

• Supervised Machine Learning using Scikit-Learn

• Unsupervised Machine Learning and clustering techniques

• Model evaluation and performance metrics

• Building REST APIs with FastAPI

• Containerizing machine learning applications using Docker

• Deploying trained machine learning models for real-world use

Machine Learning Algorithms Covered

Regression

• Linear Regression

• Polynomial Regression

Classification

• Logistic Regression

• Support Vector Machine (SVM)

• K-Nearest Neighbors (KNN)

• Naive Bayes

• Decision Tree

• Random Forest

• AdaBoost

• XGBoost

Unsupervised Learning

• K-Means Clustering

• DBSCAN

• Hierarchical Clustering

Every algorithm is explained from the intuition behind it to the implementation using Scikit-Learn, helping you understand not just how to use the models but also when to choose them.

Build a Real Machine Learning Project

Learning theory is important but applying it is what makes you industry ready.

Throughout the course, you'll work with a movie streaming platform dataset to perform Exploratory Data Analysis and understand why customers leave the platform. You'll clean the data, engineer meaningful features, visualize trends, and extract business insights before training machine learning models.

Finally, you'll take your trained model beyond a Jupyter Notebook by creating a REST API with FastAPI and packaging the application using Docker, giving you experience with a production style machine learning workflow.

Who this course is for

• Beginners who want to start a career in Data Science

• Students preparing for internships and placements

• Software Engineers who want to transition into Machine Learning

• Data Analysts looking to expand into predictive modeling

• Working professionals who want practical, job ready skills

• Anyone interested in learning Data Science through hands on projects

Prerequisites

No prior Machine Learning experience is required.

Basic computer knowledge is enough. I'll guide you through Python fundamentals before moving into Data Science and Machine Learning concepts.

Why choose this course?

There are many courses that focus only on theory or only on coding. This course combines both.

You'll understand the concepts, write the code yourself, work on a realistic dataset, learn industry best practices, and finish by deploying a machine learning model as a REST API using FastAPI and Docker.

If you're looking for one course that takes you from Python fundamentals to production ready Machine Learning, you're in the right place.

Enroll today, and let's start your journey into Data Science and Machine Learning together.