What Does MVP Stand For? It’s Not What You Think.
October 7, 2024
Article
Instructor: Packt - Course Instructors
Included with
Recommended experience
Beginner level
Ideal for aspiring data scientists, ML enthusiasts, and developers with basic Python skills. No prior ML experience needed, beginner-friendly.
Recommended experience
Beginner level
Ideal for aspiring data scientists, ML enthusiasts, and developers with basic Python skills. No prior ML experience needed, beginner-friendly.
Understand and develop Python programs using fundamental data types and control structures
Apply machine learning concepts to analyze and process datasets effectively
Implement and execute Random Forest algorithms to build predictive models
Analyze and visualize data to clean and enhance model accuracy
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October 2024
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Embark on a journey through the exciting world of machine learning, starting with the foundations of Python programming. You'll begin by mastering Python’s essential data types, loops, and decision-making constructs, gaining a strong coding foundation. As you progress, you’ll dive into machine learning, exploring how it mimics human learning, processes datasets, and applies critical concepts like outliers, model training, and overfitting.
The course then transitions into an in-depth exploration of Random Forest, a powerful machine learning algorithm. You’ll learn how to implement Random Forest using Python libraries like NumPy and Pandas, visualize data with Matplotlib, and perform crucial steps like data cleaning, handling missing values, and converting categorical data to numeric forms. By the end of this course, you'll have hands-on experience in building and optimizing machine learning models, particularly using Random Forest, to solve complex problems. Designed for both beginners and those looking to deepen their understanding of machine learning, this course combines theory with practical application. Each concept is reinforced with real-life projects, enabling you to see firsthand how machine learning algorithms can be applied to various datasets. Whether you're interested in a career in data science or looking to enhance your programming skills, this course offers the tools and knowledge to succeed. This course is for you if you want to learn how to program in Python for machine learning or want to make a predictive analysis model. It is for someone who is an absolute beginner and has truly little or even zero ideas of machine learning or wants to learn random forest from zero to hero.
In this module, we will introduce the course and its objectives. You will gain insights into the benefits of learning machine learning, the evolution of this field, and what the course offers in terms of Python and machine learning knowledge.
4 videos1 reading
In this module, we will explore the fundamentals of Python programming. You will learn about Python’s various data types, logical and comparison operators, control structures, and basic functions. By the end of this module, you will apply your knowledge to create a simple calculator project.
18 videos1 assignment
In this module, we will delve into the basics of machine learning. You will learn about the significance of datasets, the differences between labels and features, and how models are trained. The module also covers critical concepts like overfitting, underfitting, and data formats essential for machine learning.
13 videos1 assignment
In this module, we will take a step-by-step approach to understanding and implementing Random Forest, a powerful machine-learning algorithm. You will learn to use Python libraries like NumPy and Pandas for data manipulation and Matplotlib for visualization. The module will guide you through building and tuning a Random Forest model to achieve high accuracy.
26 videos1 assignment
In this module, we will summarize the entire course and highlight the most important concepts and skills you have acquired. The concluding remarks will help you reflect on how to apply Python and machine learning techniques to solve practical problems in the future.
1 video1 assignment
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.
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.
If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.
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You will be eligible for a full refund until two weeks after your payment date, or (for courses that have just launched) until two weeks after the first session of the course begins, whichever is later. You cannot receive a refund once you’ve earned a Course Certificate, even if you complete the course within the two-week refund period. See our full refund policy.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
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