Machine Learning with Scikit-learn – Edindx

Your Hands-On Guide to Building Intelligent Systems & Real-World AI Solutions

Implement key machine learning algorithms on real-world data for practical insights.

This course is overall structured with muliple projects and recap sessions which helps you to get how ML is being used.

By the end of this course, you’ll be profoundly comfortable with Machine Learning and Deep Learning concepts, equipped with the confidence and practical skills to start applying for jobs in the rapidly expanding AI field. You’ll not only understand the theory but also how to implement and optimize models, making you a valuable asset in any data-driven team. This journey will transform your understanding of AI into tangible, employable skills.

A computer with a stable internet connection. All lectures and materials are delivered online, and you will need to download software and datasets.
Basic computer skills. You should be comfortable with tasks like downloading and installing software, creating folders, and navigating your operating system (Windows, macOS, or Linux).
High school level mathematics. A basic understanding of algebra and concepts like variables and functions will be very helpful, but we will review key mathematical concepts as needed.
No prior programming experience is required! The course is designed to take you from the fundamentals of Python all the way to advanced AI topics. A strong desire to learn is the most important prerequisite.

Aspiring Data Scientists & AI Specialists: If you're looking to start a career in the exciting field of AI but don't know where to begin, this course provides the complete roadmap.
Programmers & Developers: If you already know another programming language, this course will get you up to speed with Python and show you how to apply your skills to the domains of Machine Learning and Deep Learning.
Students & Academics: This course will help you bridge the gap between theoretical knowledge and practical, hands-on application by working on real-world projects.

Course Content

Python for Applied Machine Learning – Getting Started & EDA

Import ML Libraries

19:38
Exploratory Data Analysis (EDA) – Part 1

17:01
Exploratory Data Analysis (EDA) – Part 2

12:53
Split Data

10:57
Develop ML Models and check accuracy score

10:47
Classification Report

06:49
Accuracy precision recall and f1

02:14
Recap

15:46
Confusion Matrix

06:47
Glimpses of hyperparameter training estimators

05:14
Save and load the model

04:08
LinearSVC

08:14
NeighborsClassifier

06:20
Run multiple models using Functions

11:47
Run multiple models using Parallel

10:09
Cross Validation Score

13:19
Predict vs Predict Proba

04:37
ROC Curve

15:22
Cross tab

01:59
Correlation Analysis

05:30
Feature Importance

10:35
Hyperparameter tuning – Randomized SearchCV

15:07
Hyperparameter tuning – GridSearchCV

08:19
Classification Project – Heart Disease dataset

32:25
Regressor – Train our model Part – 1

14:22
Regressor – Train our model Part – 2

11:55
Regressor – Recap

10:55
Fill missing values using Pandas

20:41
Fill missing values using scikit-learn libraries

21:47
Regression Project – California Housing dataset

25:26

A course by

Data Scientist

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