Machine learning is a huge field with many different machine learning techniques, machine learning applications, and machine learning algorithms. It can be daunting for someone to know where to start when they are trying to learn machine intelligence. Since so much of machine intelligence has changed in recent years, it’s important that you have the latest information available on how to get started. That’s why we created this blog post about the machine learning roadmap.
What is Machine Learning?
Machine learning is a machine’s ability to learn from data and make predictions. Machine intelligence is machine learning that starts to learn from its environment and can be self-improving.
Benefits of ML
Benefits of machine learning include the ability to turn data into actionable insights, making machine intelligence a valuable business tool. Machine learning has been used for many different machine learning applications from traffic predictions and emails spam detection to face recognition and personalized recommendations on social media sites.
Machine Learning Roadmap
If you are a beginner in the world of machine learning, there is no need to get confused. It can be difficult knowing how best to learn from where and what exactly should one focus their attention on? With so many options available for courses as well as books with algorithms that could lead them down any number of paths it might seem overwhelming at first glance but if we break things into small steps then mastering this field will become much more manageable! Let me share my plan below:
Step 1: Pick a programming language & Get Started
The first step to start learning machine learning is picking up your desired programming languages. There’s many out there, but I recommend Python since it’s popular and future-ready with frameworks like Django or Flask for backend development as well Tkinter which offers easy GUI creation too.
There are many machine learning libraries in Python, but if you want to go with a modern one and have most of the algorithms written for your needs then Sklearn would be best. The thing about this library is that it has classes like preprocessing tools which make data analysis easier on top of its wide range of features provided by different models across multiple industries including healthcare, finance, etc!
Step 2: Learn Linear Algebra
If you want to tune your models with maximum flexibility, it is essential that you know how they work. And knowing linear algebra will help in doing just that!
Step 3: Learn Statistics
With a basic understanding of probability and statistics, you can master machine learning.
Step 4: Learn Core Machine Learning Algorithms
Once you have some idea of using sklearn after learning python, why not start looking into how these machine-learning algorithms work?
These are the black boxes written by their developers which do all sorts of stuff for us without any input or output.
In order to get an idea of how these Machine learning algorithms work from within, look into the: following algorithms.
- Gradient Descent
- Supervised vs Unsupervised learning
- Reinforcement Learning
- Basic Linear Regression
- Working of all such similar models
Step 5: Learn Python Libraries
It’s my opinion that learning Numpy and Pandas will be helpful for you to debug the python code.
Step 6: Learn Deployment
To host your machine learning models with a powerful backend, take advantage of frameworks like Django and Flask. Docker provides an easy way for you to ship the model back into production quickly.
I found some great resources online, and believe me this are best courses out there. This is not affiliate links.
Stanford machine learning course (Coursera)
Machine Learning A-Z™: Hands-On Python & R In Data Science (Udemy)
Machine learning is a hot topic these days, but it can be hard to know where you should start. This machine roadmap will help! We’ll go over the different steps that are needed for someone who wants to become an expert in this field and take their career from “beginner” all the way up to “expert” level!