How to learn Machine Learning?

Machine Learning is one of the many applications of Artificial Intelligence which offers the system to learn and improve through experience just like a living being. It helps to make a system to be explicitly programmed to answer to different types of situations by learning to be better each time. It focuses on the development of computer programs which can access data and use it to learn for themselves. The programs look at the data and make observations. For instance, it would look at the patterns in data and would be better equipped in the future to make decisions based on the examples already there. The primary goal is to make the computers smarter by allowing them to learn automatically. No human intervention or assistance is made, and actions are taken accordingly.

What are some of the machine learning methods?

Unsupervised machine learning algorithms- This is used when the information that is being used is not labeled or classified. The unsupervised learning concentrates on how the systems can infer a function to describe the hidden structure from unlabelled data. Even though the system is not able to figure the right output at times, it can explore the data patterns and draw the significant inference from the datasets and describe the hidden structures.

Semi-supervised machine learning algorithms- These algorithms lie between the unsupervised and supervised ones. This is because both labeled and unlabeled data is used for training. Mostly a small amount of labeled information is used with a large amount of unlabeled data. The systems would use these methods to improve the accuracy of learning. It is used when the labeled data would require resources for the training, while the unlabelled data would not need additional resources.

Supervised machine learning algorithms- This technique applies what had been learned in the past to predict the future events using labeled examples. In this variant, you would need to start by analyzing a known training dataset. An inferred function is produced by the algorithm to make predictions output values. After sufficient training, the system can provide targets for any new input. It also compares the correct and intended output to find errors to modify the model accordingly.

Reinforcement machine learning algorithms- This learning method interacts with the environment by producing specific actions and finding out the rewards and errors. The trial and error search and the delayed reward mechanisms are some of the most relevant characteristics of this method. It allows machines and the software agents to determine the ideal behavior automatically within a specific context and maximize performance.

How and where should you start learning machine learning?

Here are a few steps that you would like to follow to get started with Machine Learning.

Step 1: Adjusting your Mindset- You need to get ready for a beautiful course and start preparing yourself for the journey. You need to believe in yourself that you practice and apply Machine learning to different applications.
You would need to find out what is stopping you from getting started and focus on them. Anything is just as hard as you think it to be. You would need to understand how to approach the topic. Last but not the least, you need to find people who are on the same course. You can find friends and can help yourselves out if you get stuck or find a mentor in someone.

Step 2: Approach it through a process- Use a systematic process to proceed through the problems.
Do not get overwhelmed by any particular topic or a problem. Learn how to break it down into bits and pieces and how you can solve them.

Step 3: Pick a Tool- Learning Machine Learning is all about getting comfortable with the tools and understanding of when to rely on which.
Select a tool that you can cope up with at your level and map it into the process that you were following. There are different tools available. While beginners can try out the Weka Workbench, the intermediate guys can try out python Ecosystem, and the advanced people should be moving to the R Platform.

Step 4: Practice on available Datasets- Learning would not help much until and unless you have hands-on experience.
Select the datasets that you can work on and practice the process that you are using. Start practicing with the in-memory datasets and then take tours of the real-world machine learning problems. Then start working on those problems that you think can make a difference. Work on those that matter to you.

Step 5: Build a portfolio- Gather all the results you generate and demonstrate the skills you have earned.
Take your time in building a vast machine learning portfolio and then find out how you can use that to get employed or earn money.

How to get more into the Machine Learning Process?

Let us get a little more into how to get involved with a machine learning process.

Step 1: Defining the problem- First, you need to define the problem that you would like to solve.

Step 2: Prepare the data- The next step involves the collection of data. You need to understand and learn how to prepare the data for you to perform the process on it. You need to improve the accuracy of your model and discover the feature engineering. You would also need to be careful about the data leakage in machine learning.

Step 3: Check the algorithms- Learn to evaluate the algorithms by choosing the right test options. You would understand the data-driven approach to machine learning and how it can benefit any situation.

Step 4: Improving results- The next thing you need to learn is how to improve your outcomes and improve the deep learning performance.

Step 5: Learn to present results- The final step is to learn how to improve the machine learning results and deploy the predictive model to production.

What would you need to learn along the way?

There are some skills you would need to pick up along the way:

Linear Algebra- Linear Algebra is one of the subjects you would need to focus from now on.

Statistical Methods- Statistics deals mostly with data, and it is one of the most significant things you would need to deal with. You would learn about the different distributions of data and how you should handle them. You would also learn about various tools that you can use and how to use them.
Algorithms - The algorithms are the next things that would present themselves in front of you. Learn them, understand what is being done in each and when you should be implementing them.

This is one of the most effective approaches to get started with Machine Learning and would help you get a good understanding of the underlying concepts.