Machine Learning for Beginners: I am the Machine Series
Learn Machine Learning with Python over 14 weeks! Build models, work with data, and use tools like Pandas, NumPy, and Scikit-Learn to boost your career.
Hey there nerds! Welcome to our new series, I am the Machine.
The title of this series in inspired by the shirtless comedian, Bert Kreishcer. If you’ve seen his stand-up specials then you’ll know what The Machine is all about!
This series is an immersive 14 article journey into the world of Machine Learning with Python. You’re about to learn the cutting edge tools and tactics that are shaping the future of computing and business around the world.
I have a roadmap below of what you can expect over the course of this new series. I am beyond excited to kick things off as this topic builds on the previous two series’.
Starting from a Data Analytics standpoint we progressed into working with SQL and Databases in Python, now onto the next chapter of Machine Learning.
Each week, I dive deep into Python and beyond, breaking it down into bite-sized pieces. While everyone else gets just a taste, my premium readers get the whole feast! Don't miss out on the full experience – join us today!
Now that you have all this knowledge and you’re about to work with data like no other. The question becomes, now what? What can I do with all this data?
Welcome to “I am the Machine” our Machine Learning series only found here on The Nerd Nook, I’m stoked to have you here!
For my premium readers, I’ll be adding mini challenges, learning resources, and more at the end of each article to help you really build a strong foundation!
🔐 Premium Reader Benefits
Full-length articles every Friday
Mini challenges & additional resources
Lifetime access to archives, including Data Analytics and SQL in Python series
Monthly Python projects
and so much more!
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👉 This is my full-time job so I hope you will support my work.
If you’re a free reader, don’t worry—you’ll still get valuable bite-sized insights with each week. But keep in mind, the premium full-length content drops on Fridays, and older articles (over 6 weeks) will go behind a paywall.
Our premium readers, of course, have 24/7 access to everything from full access to these articles and all the code that comes with them, so they can follow along!
Plus, you’ll get access to so much more, like monthly Python projects, the '3 Randoms' series, and my complete archive!
And hey, here’s a lifetime discount for premium—come join the fun and take your skills to the next level! Premium members can also suggest topics for future articles, so don’t hesitate to share your thoughts at the bottom of any premium post.
Machine Learning or Learning Machine?
Back in the 1990s, Machine Learning sounded like science fiction, it just sounded weird. People would probably think of the films Back to the Future or how you’re a conspiracy theorist thinking computers will take over.
I mean come on, 20 years ago nobody was thinking of ML. Nowadays we say this and we turn some heads no matter the industry. Today, Machine Learning is booming, growing faster than ever across industries.
A segment of AI, the segment which allows a computer learn from studying data and statistics. This is beyond cool as we can now teach the computer things we never could before.
Learning Machine Learning will open a lot of doors and make you a go-to person in any business. And when we add Python into the mix, you’ll have an even stronger set of tools to work with your data like never before!
P.S. ~ I may cover off topic articles surrounding other areas of interest throughout, this will be covered in the free issues. I’ll wait to hear your feedback!
The “I am the Machine” Series
Kicking off your Machine Learning journey right from the start in week one as we build new skills each week you’ll be able to use in your career and beyond. Each article will build on the previous one to make sure you are get the chance to use the skills you are learning and more importantly comprehend what you’re learning.
What we’ll cover
We’ll start from the basics to give you a strong foundation and gradually move to more advanced topics. Each week, you’ll get hands-on practice with popular Python libraries like Pandas, NumPy, Scikit-Learn, and some Tensorflow!
This series will focus a lot on the types of learning, how to create and train ML models and creating pipelines to optimize the flow. But really, there is so much more we will cover, things you didn’t even know about yet!
Share this with those you know would get value from Machine Learning!
🛣 Machine Learning Roadmap
Here is the roadmap you can expect as we build out models and go through our series. This is a rough roadmap as there might be weeks where I expand on certain topics or include additional content requested by my premium readers.
Premium readers can leave insights and feedback at the bottom of all my articles so if there is something you want me to touch on or cover that fits into the series let me know and I’ll work it in there.
Week 1: Laying the ground work for Machine Learning
Learn what is Machine Learning, the different types of the tools we use.
Week 2: Setting up your Machine Learning environment
Learn to setup your environment the right way and start exploring data.
Week 3: The importance of data in the ML world
Discover how to source data and real world consideration such as ethics and privacy.
Week 4: What is Data Preprocessing?
Learn to standardize our data by handling missing values, outliers, and duplicates.
Week 5: Encoding Categorical Data
How to handle categorical data effectively and when to use each type of encoding.
Week 6: Exploratory Data Analysis (EDA)
Uncover insights and correlations in your data by establishing relationships.
Week 7: Immersion with Supervised Learning
Get immersed in Regression and Classification with real-world applications.
Week 8: Building Pipelines for Preprocessing
Learn to streamline and automate the preprocessing workflow with Pipelines.
Week 9: Advanced Supervised Learning Techniques
Learn advanced algorithms with Gradient Boosting using XGBoost.
Week 10: Evaluation Metrics for ML Models
How to best evaluate your Models performance with indicators.
Week 11: Feature Engineering and Selection
Learn to enhance your model performance through custom features.
Week 12: Time for Unsupervised Learning
The introduction to clustering, dimensionality reduction, and PCA.
Week 13: Handling Overfitting and Hyperparameter Tuning
Learn some common ML pitfalls and how to best optimize models.
Week 14: Capstone Project: End-to-End ML Pipeline
Combine everything into a comprehensive end-to-end ML project.
Join In!
This Machine Learning series will be easy for you to follow and really practical, with each week building on the last. By the end, you’ll have a solid grasp of Machine Learning and how to use it with Python.
The plan might change a bit as we go, and I’ll also be making some videos to go along with the content!
Whether you’re trying to level up your skills, switch careers, or just learn something new, come join us on this new journey to mastering Machine Learning.
Stay tuned, hope you all have an amazing week! ~ Josh
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