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This course is part of Deep Learning with Real-World Projects Specialization
Instructor: Packt - Course Instructors
Included with 
Recommended experience
Beginner level
Perfect for programming and data science beginners. No Python or data analysis experience needed, but basic math understanding is helpful.
Recommended experience
Beginner level
Perfect for programming and data science beginners. No Python or data analysis experience needed, but basic math understanding is helpful.
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.
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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.
Building on Python fundamentals, you will explore data analysis with NumPy and Pandas. You will learn about array operations in NumPy, manipulating and analyzing data using Pandas, including working with DataFrames, performing data operations, indexing, and merging datasets. These modules are designed to provide you with a strong foundation in data manipulation and analysis, critical for any data science role. The course culminates with an introduction to basic machine learning concepts. You will delve into linear regression, understanding its mathematical foundations and practical applications. Furthermore, you will explore gradient descent, a crucial optimization technique, and KNN classification, one of the simplest machine learning algorithms. Each topic is reinforced with case studies, ensuring you can apply theoretical knowledge to real-world scenarios. This course is ideal for beginners in programming and data science. No prior experience in Python or data analysis is required, but a basic understanding of mathematics will be beneficial.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
10 videos1 reading2 assignments
Packt helps tech professionals put software to work by distilling and sharing the working knowledge of their peers. Packt is an established global technical learning content provider, founded in Birmingham, UK, with over twenty years of experience delivering premium, rich content from groundbreaking authors on a wide range of emerging and popular technologies.
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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.
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