Hands-on Projects | Industry-Ready Skills | Certification

Machine Learning with Scikit-Learn

Master practical ML techniques and build real-world predictive models

Python Pandas NumPy Jupyter Flask Git
from sklearn import svm
model.fit(X_train, y_train)
accuracy_score(y_test, preds)
pca = PCA(n_components=2)
GridSearchCV()
pd.read_csv('data.csv')
plt.scatter(X[:,0], X[:,1])
joblib.dump(model, 'model.pkl')

Course Overview

This comprehensive course teaches you practical machine learning using Python and Scikit-Learn. From data preparation to model deployment, you'll gain hands-on experience with real-world datasets and build a portfolio of ML projects.

80+ Hours

Hands-on ML exercises

Real Datasets

Work with industry data

Model Building

Regression & classification

Certification

Industry-recognized credential

Machine Learning Course
Live Classes Starting Monthly
New
Batch!

What You'll Learn

Master these essential machine learning skills

Data Analysis

Clean and analyze data with Excel and Python

Python Programming

Master Python for data science

Scikit-Learn

Implement ML algorithms efficiently

Supervised Learning

Regression and classification techniques

Unsupervised Learning

Clustering and dimensionality reduction

Model Deployment

Prepare models for production

Machine Learning With Scikit-learn Syllabus

Comprehensive curriculum covering machine learning from fundamentals to deployment

Module 1: Introduction to Machine Learning

Module 2: MS Excel for Data Analysis

Module 3: Getting Started with Python

Module 4: Python Fundamentals

Module 5: Python Control and Logical Flow

Module 6: Advanced Python Programming

Module 7: Algorithmic Thinking with Python

Module 8: Introduction to Scikit-learn

Module 9: Supervised Learning - Regression

Module 10: Supervised Learning - Classification

Module 11: Unsupervised Learning Techniques

Module 12: Model Evaluation and Optimization

Module 13: Real-World Data Handling

Module 14: ML Deployment Essentials

Module 15: Capstone Project

from sklearn.ensemble import
X_train, X_test, y_train, y_test
pipeline.fit(X_train)
confusion_matrix()
joblib.dump(model)

Ready to Become a Machine Learning Engineer?

Join our next cohort and master practical ML with Scikit-Learn.

Limited seats available for the next batch starting 1, November 2025

Get In Touch

We'd love to hear from you

Our Location

1st Floor, Mallemudi Vari St, BesideNH5, Ramavarapadu, Vijayawada, Andhra Pradesh 521108

Call Us

+91 7702570023

Email Us

info@mohansoftwaresolutions.com

mohansoftwaresolutions@gmail.com