Scikit-learn Course Online

9 Lesson

25 Hours

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Advance your machine learning expertise with igmGuru’s Scikit-learn Course. This training provides a complete foundation in traditional machine learning using Python’s most powerful and accessible ML library. Through hands-on projects and expert guidance, you will learn core techniques such as data preprocessing, model building, training, evaluation and optimization. Designed for learners and professionals aiming to excel in data science and machine learning, this course builds the practical skills needed to create reliable predictive models and drive real-world data-driven solutions.

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Scikit Learn Course Overview

Prerequisites:

Python Programming
NumPy
Pandas
Matplotlib
Basic Statistics, Linear Algebra, Calculus
Basic Machine Learning Knowledge

What Will You Learn:

Machine Learning Fundamentals
Data Handling
Data Preprocessing
Exploratory Data Analysis
Model Building
Model Training
Classification Algorithms
Regression Algorithms
Clustering Algorithms
Dimensionality Reduction
Model Evaluation
Model Selection
Cross-Validation
Hyperparameter Tuning
Real-World Machine Learning Applications

Who Should Do This Course:

Machine Learning Aspirants
Data Science Enthusiasts
Python Developers
Data Scientists
Data Analysts
AI/ML Engineers
Team Leaders
Researchers

Key Features

1 On 1 Training Option Available
24 X 7 Lifetime Support & Access
Small Batches Upto 10 Participants
Experienced & Professional Trainers
100% Job Assistance
Flexible Schedule

Scikit-Learn Course Modules

Lesson 1 - Introduction to Scikit-learn

1. Overview of the library

2. Scikit-learn workflow and API structure

3. Working with datasets

Lesson 2 - Data Preprocessing

1. Handling missing values

2. Encoding categorical variables

3. Feature scaling and normalization

4. Train-test split and data pipelines

Lesson 3 - Supervised Learning - Classification

1. Logistic Regression

2. K-Nearest Neighbors (KNN)

3. Decision Trees

4. Random Forest Classifier

5. Support Vector Machines (SVM)

6. Model evaluation metrics (accuracy, precision, recall, F1-score)

Lesson 4 - Supervised Learning - Regression

1. Linear Regression

2. Ridge, Lasso and ElasticNet

3. Decision Tree Regressor

4. Random Forest Regressor

5. Evaluation metrics (MAE, MSE, RMSE, R²)

Lesson 5 - Unsupervised Learning

1. K-Means Clustering

2. Hierarchical Clustering (if needed via compatible tools)

3. DBSCAN

4. Dimensionality Reduction with PCA

Lesson 6 - Model Selection and Optimization

1. Cross-validation

2. GridSearchCV

3. RandomizedSearchCV

4. Hyperparameter tuning

Lesson 7 - Working with Pipelines

1. Building ML pipelines

2. Combining preprocessing and model steps

3. End-to-end workflow automation

Lesson 8 - Advanced Scikit-learn Features

1. Feature engineering tools

2. Ensemble methods

3. Handling imbalanced data (class weights, resampling strategies)

Lesson 9 - Real-World Machine Learning Projects

1. Building complete ML pipelines

2. Solving classification and regression problems using real datasets

3. Model interpretation and reporting

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Scikit-Learn Online Training Fees

Online Class Room Program

US $ 799.00

100% Money Back Guarantee

Duration : 25 Hrs
Plus Self Paced

Classes Starting From

Fast Track Batch 23 Jun 2026
Weekday Batch 29 Jun 2026
Weekend Batch 27 Jun 2026

Corporate Training

Customized Training Delivery Model
Flexible Training Schedule Options
Industry Experienced Trainers
24x7 Support

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Scikit Learn Certification Online Training

After completing the Scikit Learn Training Course and hands-on projects, learners receive a Course Completion Certificate from igmGuru. This certification validates your ability to implement machine learning workflows, build predictive models and apply Scikit-learn techniques to real-world data science and AI projects.

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Scikit-learn Course Online