This course starts with an introduction to Python programming, covering everything from installation and setup of Python and Anaconda to fundamental concepts such as variables, numeric and logical operations, control structures like if-else and loops, and defining functions. The journey continues with in-depth modules on strings and lists, ensuring a solid understanding of these core components.
Python Fundamentals and Data Science Essentials
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Python Fundamentals and Data Science Essentials
This course is part of Deep Learning with Real-World Projects Specialization
Taught in English
Instructor: Packt
Included with 
Course
Gain insight into a topic and learn the fundamentals
Beginner level
Recommended experience
16 hours (approximately)
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
Course
Gain insight into a topic and learn the fundamentals
Beginner level
Recommended experience
16 hours (approximately)
Flexible schedule
Learn at your own pace
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
This course is part of the Deep Learning with Real-World Projects 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
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Share it on social media and in your performance review
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
Recommended if you're interested in Machine Learning
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Frequently asked questions
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.