Packt
Python Fundamentals and Data Science Essentials
Packt

Python Fundamentals and Data Science Essentials

Packt

Instructor: Packt

Included with Coursera Plus

Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

16 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

16 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Run Python programs for tasks using numeric operations, control structures, and functions.

  • Analyze data with NumPy and Pandas for comprehensive data insights.

  • Evaluate the performance of linear regression and KNN classification models.

  • Develop optimized machine learning models using gradient descent.

Details to know

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Recently updated!

September 2024

Assessments

5 assignments

Taught in English

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This course is part of the Deep Learning with Real-World Projects Specialization
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There are 10 modules in this course

In this module, we will cover the essential Python programming concepts needed as a foundation for advanced topics. Starting from installation and basic syntax to detailed explorations of various data structures, this section ensures you have a solid grounding in Python.

What's included

18 videos2 readings

In this module, we will introduce NumPy, a powerful library for numerical computing in Python. Through a series of hands-on videos, you'll learn to perform essential NumPy operations and leverage its capabilities for data analysis.

What's included

3 videos

In this module, we will dive into Pandas, a key library for data manipulation and analysis in Python. You will learn how to work with Series and DataFrames, perform various operations, and handle real-world data sets efficiently.

What's included

12 videos1 assignment

In this module, we will cover essential linear algebra concepts that are foundational for machine learning. From vectors and matrices to multi-dimensional spaces, you'll gain the mathematical skills necessary for advanced algorithms.

What's included

5 videos

In this module, we will explore data visualization techniques using Matplotlib and Seaborn. Through practical examples and a case study, you'll learn how to create compelling visual representations of data to uncover insights.

What's included

4 videos

In this module, we will cover the basics of simple linear regression, a key statistical technique. Starting from machine learning concepts, you'll learn how linear regression works, the math behind it, and how to apply it through case studies.

What's included

10 videos1 assignment

In this module, we will focus on gradient descent, a crucial optimization algorithm. From understanding cost functions to applying gradient descent in practical scenarios, you'll gain a deep understanding of this essential technique.

What's included

8 videos

In this module, we will delve into the K-Nearest Neighbors (KNN) algorithm for classification. You'll learn the theory behind KNN, its practical applications, and how to measure its performance through various case studies.

What's included

14 videos1 assignment

In this module, we will cover logistic regression, a fundamental classification technique. You'll learn about the Sigmoid function, log odds, and how to apply logistic regression in real-world scenarios through case studies.

What's included

4 videos

In this module, we will explore advanced machine learning algorithms, focusing on regularization techniques and model selection. Through detailed examples and case studies, you'll learn how to apply these advanced methods to improve model performance.

What's included

10 videos1 reading2 assignments

Instructor

Packt
Packt
231 Courses3,858 learners

Offered by

Packt

Recommended if you're interested in Machine Learning

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