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The Complete Machine Learning Study Roadmap

Find out where you need to be to start your Machine Learning journey and what you need to do to succeed in the field.



The Complete Machine Learning Study Roadmap
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The Machine Learning field is a bit more specific than Data Science and Data Engineering which naturally include Machine Learning in the mix. So what do you need to do precisely to be part of this field?

 

1. Prerequisite

 

In order for you to understand the concept of Machine Learning and everything about it - you need to know the fundamentals in and out. This includes the theories, concepts, methods, and algorithms behind it - why they do what they do and how they all act as a building block to Machine Learning. These prerequisites are surrounding analytical work, which helps you further down the line as a Machine Learning Engineer: 

  • Standard Deviation
  • Linear Algebra
  • Statistics
  • Probability

Here are some resources that can help you get a more in-depth understanding:

  1. Mathematics for Machine Learning - Book
  2. Linear Algebra by Khan Academy - YouTube
  3. Statistics and Probability by Khan Academy - YouTube
  4. Mathematical Foundations of Machine Learning - Udemy

 

2. Stages of Machine Learning

 

Machine Learning is made up of the following stages:

 

1. Research/Gathering Data

 

During this stage, you will gain better knowledge of the problem or task at hand from a business perspective. This will then help you gather the correct data to input into your model. The quality and quantity of your data will determine how effectively your predictive produces trustworthy outputs.

 

2. Data Preparation

 

Preparing data takes up a lot of time, because data is never in the format we need it at. This will include de-duping, normalization, error correction and further areas of preparation such as feature engineering.

 

3. Building your Model

 

Depending on the task at hand, you will choose the correct model to help solve your problem. The next stage in this roadmap process goes through the different Machine Learning algorithms you will typically come across. 

 

4. Train and test your model

 

This stage is testing your model with the data you collected and prepared. The training dataset will be used to improve the model’s ability to make predictions. The test dataset is a subset of the training dataset, and provides us with an unbiased evaluation of the final model.

 

5. Model Optimization and Evaluation

 

Model optimization is the process of training the model iteratively, leading to a maximum and minimum function evaluation. Further evaluation includes testing the model using data that has not been used during the training phase to see the performance of the model against data it has not seen before.

 

6. Experiment tracking

 

In order to get to a point where your model is performing very well, you would have gone through a lot of tuning. All these components that have been tuned, from the model to metrics will need to be tracked to keep the project organized and provide easy access to the history of the model. 

 

7. Model Deployment

 

This is the last stage of the machine learning process. The point in which you put your machine learning model into production, so that it can be used to make business decisions based on data.

If you would like to gain a more in-depth understanding of the machine learning process, have a look at these resources:

 

3. Machine Learning Algorithms

 

Once you have a good understanding of the foundational math, you will then be able to whizz through understanding the machine learning algorithms - as it’s all based on Maths. 

As a Machine Learning engineer, you will always work with algorithms - they are the instructions to tell a computer what to do. Therefore, you need to understand those instructions. 

There are 4 different types of machine learning algorithms:

  • Supervised learning
  • Unsupervised learning
  • Semi-supervised learning
  • Reinforcement learning

The type of machine learning algorithms that you will frequently work with and are very popular are:

  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Random forest
  • Support Vector Machines (SVM’s)
  • Naive Bayes
  • KNN classification
  • K-Means
  • Artificial neural networks (ANNs)
  • Recurrent neural networks (RNNs)

Have a read of this article: Machine Learning Algorithms Explained in Less Than 1 Minute Each.

If you’re looking for a more in-depth explanation, have a read of this: Popular Machine Learning Algorithms

Here are some more resources for you to check out:

  1. Machine Learning Algorithms by Simplilearn - YouTube
  2. Understanding Machine Learning: From Theory to Algorithms By Shai Shalev-Shwartz, ?Shai Ben-David - Book

During this phase, it is also important to learn about the basis of the machine learning algorithms. This relates to the task at hand - is it a classification task, and if so, which algorithm would be best? Is it supervised or unsupervised learning? Through this, you will see the connections between the fundamentals and machine learning algorithms. 

 

4. The Libraries

 

As a Machine Learning engineer, you will spend a lot of your time building algorithms and applications. Therefore, you need to understand the libraries that help build these. Machine Learning libraries are a collection of functions that have been created to help develop machine learning applications - through their prepackaged functions. 

Here is a list of the most well-known and used libraries: 

The best way to learn about these libraries is through their User Guides and Documentation, which have all been linked above. 

 

5. Projects

 

So you’ve mastered the fundamentals, machine learning algorithms, and libraries - the next step is to take all that knowledge and skill and apply it to real-world cases. This not only tests your skills and presents you with your strengths and weaknesses in the field. But it also helps you add to your portfolio - which will help you in the next stage!

I came across Thecleverprogrammer, and he has a list of different types of Machine Learning projects, for beginners and intermediate learners. It is good to approach the challenge yourself and then reflect back on how he chose to handle the problem or task at hand.

Here is a list of Machine Learning projects for beginners

If you are ready to move onto something a bit more challenging, have a look at these projects:

With these projects, you are not only putting your skills to the test, but you are understanding your strengths and it helps define your weaknesses. Not only is it helping you on that end, but these projects will help you build up your portfolio/resume - getting you ready for interviews!

 

6. Interview Preparation

 

There are a lot of resources for you to help you prepare for your interview. There are books, PDFs, online courses, and webinars in which you can also gain insightful information by connecting and speaking with people in the industry. 

Below are some resources to help you prepare for your Machine Learning interview:

 

7. Further Reading

 

If you are looking for some extra resources to help you become more proficient in the Machine Learning sector.

 
 
Nisha Arya is a Data Scientist and Freelance Technical Writer. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.