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At its core, Machine Learning functions to answer questions by "learning" from data.
It may sound simple, but knowing Machine Learning requires that you have the perseverance to learn concepts that you might be oblivious about and that you invest a significant amount of your time to have a firm grasp of the principles behind it.
To help you with your journey towards joining the Machine Learning bandwagon, here are the top ten tips for beginners to learn Machine Learning.
If you're like some people who tend to shy away from numbers and statistics, then I have some good news for you.
You don't need to be an expert statistician to process your data for machine learning purposes.
However, you still need to understand some statistical concepts to help you know how and when to apply or use your data effectively for machine learning.
Some of the ideal statistical learning that you can prioritize learning are:
• Mean and distribution
• Statistical decision theory
• Regression
• Mean Square Error, Least Squares
Learning a programming language can seem like a long and painful process, but it doesn't have to be. The key is to find a programming language that is popular, easy to learn, and those that are commonly used for data analysis and machine learning like Python or R.
If you're a newbie to the programming language and how it's applied in machine learning, you can learn through a machine learning course. With these courses alone can help you learn how to develop machine learning algorithms using concepts of time series modeling, regression, etc.
With that said, since programming language is used to analyze and manipulate data for statistical reasons, you must learn to "speak" the language of machine learning.
Machine learning is a rich, and broad field that will continue to expand in the coming years. It's because of this that you'll have a big chance of getting overwhelmed and lose focus as you learn it.
To keep this from happening, you need to set concrete goals first before diving into machine learning.
This can help you stay on track, avoid wasting your time and keep you moving forward.
You can think about which specific sector in the industry you'll focus on, the tools, the problems that you'd like to solve through Machine Learning, etc. You can use these as your guiding compass in your journey towards mastering Machine Learning.
Machine learning deals with processing a lot of data, and it involves specific steps that can be complicated for the untrained.
As a beginner, you will need to invest some time and effort in understanding the basics of machine learning and data science.
You need to understand the basic concepts of essential aspects in machine learning like data science, programming, algorithms, and more.
Exploratory data analysis deals with studying a dataset to understand the shape of data, feature correlations, and signals within the data that can be used to build predictive models.
Performing this analysis can help you determine how to improve your products, understand user behavior, and check if the data can give useful signals for data product building.
It can include a bit of lightweight modeling to help you determine the importance of various features within datasets, but it's one of the essential competencies of startup data scientists.
Here's a concise version of some of the crucial things that you need to know:
• Autoencoders. It allows you to encode your features in a way that they don't take up too much space but still represent the same features.
• Clustering. Using a clustering algorithm helps you classify your data points in specific groups.
• Feature separation techniques. This helps you see how each of your features contribute to dataset formation, determine which ones are crucial, and the role each one plays in your overall data.
The goal of supervised learning is to use an algorithm to learn and estimate the mapping function well enough that when you add new input, the algorithm can predict the output variables for the specific data.
You can think of the process where the algorithm learns from your training data as a teacher supervising his or her students' learning process.
The learning process stops once the algorithm reaches an adequate level of success.
You can have access to significant amounts of data that you can use for algorithms to come up with the valuable output.
That being said, this means you need to know how to handle big data systems effectively.
You need to understand how to store substantial amounts of data and efficiently access and process them.
Doing so can help you create solutions that you can implement in practice and not just theory.
The deep learning algorithm is built with connected layers that allow its neural network to learn increasingly complex data features as it goes through each layer.
With deep learning, you can turn predictions into actionable results since it can perform knowledge-based predictions and pattern discovery.
Also, by feeding deep learning with big data, you can get remarkable results in terms of your management, innovation, sales, and productivity.
Finally, you will need to complete a data project to apply what you have learned so far from the nine tips above.
Start small and look for sample machine learning projects for beginners like a social media sentiment analysis using Twitter or Facebook dataset.
After all, what is machine learning without real-life application, right?
Machine learning is an expanding field that is showing no signs of stopping any time soon, so get on board and follow these ten tips for beginners.
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