Learn & Build Machine Learning Models with Python

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Learn & Build Machine Learning Models with Python

This course is part of Apply AI Foundations with Python and AWS Specialization

Instructor: EDUCBA

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

9 hours to complete

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

9 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Explain core machine learning concepts and prepare data using Python libraries.
Visualize and analyze datasets using NumPy, Pandas, and Matplotlib.
Build and evaluate basic machine learning models using Scikit-learn.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

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This course is part of the Apply AI Foundations with Python and AWS Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

By the end of this course, learners will be able to explain core machine learning concepts, prepare and analyze data using Python libraries, visualize insights effectively, and build and evaluate basic machine learning models using industry-standard tools.

This beginner-friendly course is designed to provide a clear, structured pathway into machine learning with Python, making it ideal for students, aspiring data scientists, and professionals transitioning into data-driven roles. Learners start with foundational machine learning principles and gradually progress through numerical computing with NumPy, data manipulation with Pandas, and data visualization using Matplotlib. Unlike theory-heavy courses, this program emphasizes practical understanding and hands-on workflows, helping learners connect concepts to real-world applications. The course also introduces essential preprocessing techniques, Scikit-learn pipelines, and linear regression modeling, ensuring learners understand not just how to build models, but why each step matters. What makes this course unique is its step-by-step learning progression, well-structured modules, and assessment-aligned objectives, enabling learners to build confidence as they move from data preparation to model evaluation. Upon completion, learners will have a strong foundation to pursue advanced machine learning topics or apply their skills in real projects.

This module introduces learners to the core concepts of machine learning and establishes a strong foundation in numerical computing using Python. Learners gain an understanding of how machine learning works, its life cycle, and how NumPy is used to create and manipulate numerical data essential for ML workflows.

What's included

6 videos4 assignments

6 videos Total 34 minutes

Introduction to Course 7 minutes
What is Machine Learning 5 minutes
Life Cycle 5 minutes
Introduction to Numpy Library 7 minutes
Creating Arrays from Scratch 6 minutes
Creating Arrays from Scratch Continued 5 minutes

4 assignments Total 60 minutes

Getting Started with Machine Learning 10 minutes
Understanding the ML Workflow 10 minutes
Creating Data with NumPy 10 minutes
Foundations of Machine Learning & Numerical Computing 30 minutes

This module focuses on efficient data manipulation using NumPy and introduces Pandas for structured data handling. Learners develop skills in array operations, vectorized computations, and DataFrame-based data exploration, which are critical for data preprocessing in machine learning.

What's included

6 videos4 assignments

6 videos Total 49 minutes

Array Indexing and Slicing 10 minutes
Numpy Array Functions and Shape Modification 9 minutes
Mathematical Operations on Numpy Arrays 7 minutes
Introduction to Pandas Library 10 minutes
Working with Pandas DataFrames 7 minutes
Slicing and Indexing with Pandas 7 minutes

4 assignments Total 60 minutes

Working with NumPy Arrays 10 minutes
Numerical Operations & Data Handling 10 minutes
Exploring Data with Pandas 10 minutes
Mastering NumPy & Introduction to Pandas 30 minutes

This module equips learners with practical data analysis and visualization skills using Pandas and Matplotlib. Learners explore datasets, generate statistical insights, handle missing values, and create meaningful visualizations to communicate data-driven findings effectively.

What's included

6 videos4 assignments

6 videos Total 48 minutes

Create DataFrame and Explore Dataset 8 minutes
Data Analysis with Pandas DataFrame 12 minutes
Other Useful Methods in Pandas Library 4 minutes
Introduction to Matplotlib 6 minutes
Customizing Line Plots 8 minutes
Create Plot Using DataFrame 9 minutes

4 assignments Total 60 minutes

Exploring and Analyzing Data 10 minutes
Advanced Pandas & Plotting Basics 10 minutes
Creating Meaningful Visualizations 10 minutes
Data Analysis & Visualization 30 minutes

This module introduces practical machine learning implementation using Scikit-learn. Learners focus on data preprocessing, pipeline construction, model evaluation, and linear regression, enabling them to build, evaluate, and interpret machine learning models with confidence.

What's included

6 videos4 assignments

6 videos Total 58 minutes

Standard Scaler to Scale the Data 6 minutes
Encoding Categorical Data 11 minutes
Sklearn Pipeline and Column Transformer 12 minutes
Evaluation Metrics in Sklearn 7 minutes
Linear Regression 12 minutes
Evaluation of Linear Regression Model 11 minutes

4 assignments Total 60 minutes

Data Preprocessing Techniques 10 minutes
Building Robust ML Pipelines 10 minutes
Linear Regression in Practice 10 minutes
Machine Learning with Scikit-Learn 30 minutes

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Instructor

EDUCBA

1,050 Courses 246,550 learners

Offered by

EDUCBA

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