Have you ever watched I-Robot and wondered if that type of reality could ever be possible? Sounds a little
science fiction and long off into the future right? Well, that’s not entirely accurate. We are actually much closer than you think to make that movie a reality. With modern advancements in AI and machine learning, we could just be a few decades away from having a fully autonomous robot.
The only question to ask yourself is do you want to be a part of the artificial intelligent movement? The article is for anyone who wants to get into AI/Machine learning and to start us off let’s go over the difference between (Artificial Intelligence) and machine learning.
When we say artificial intelligence, we are talking about an overall definition or concept that has to do with an intelligent machine. The actual definition of Artificial intelligence is: Intelligence demonstrated by machines, as opposed to the natural intelligence displayed by humans. So basically any advanced machine that can think on its own. Now in regards to machine learning, AI is an overall concept, whereas machine learning is a type of artificial intelligence.
Machine learning gathers large amounts of data to analyze and process faster than any human being. This data creation can help identify negative patterns, data skews, and anomalies. This ensures a more reliable pathway when it comes to machines making advanced decisions.
Example of Machine learning!
Automatic stock bots
Google’s search Algorithm
Tesla’s Auto drive
The first thing you want to consider when deciding to go down the Machine learning path is to learn what type of machine learning you want to do or what interests you. Machine learning covers a lot of different categories, from creating apps to programming games.
Find a Good Machine learning course
Machine learning is extremely popular and there are many courses out there. The key is to find a good course for beginners that teaches to basics and sets you up for success as you move on to more advance concepts of Machine learning. Typically these courses will introduce you to the different machine learning/AI algorithms such as linear regression, l-q.st.es regression. It’s also good to get some machine learning certifications. We will go over a few certifications further down in this article.
Choose a Machine Learning Language
It’s time to pick a primary language to use when creating models and Algorithms. the most popular 2nd easiest language to use is going to be python. python is wide-spread and is used in most industries. This program works well with machine learning and will be your best route to go. It takes about 3 to six month to learn the basics of pyton, but python is so easy, users can create a program within a matter of days. The best way to learn python is to immerse yourself in it by committing to do a little work everyday. Here is a great course on to udemy that will teach you how to use python for machine learning.
You can also join a python Community, where you’re able to learn with other people. This will help so that don’t have to learn alone.
Next, you want to tune up on your math skills. Machine learning has a lot of math associated with it, so to make things easier we recommend looking into some math courses or just going over math that has to do with machine learning. Although knowing math is not essential to your Machine learning success, it will greatly help speed up your work and understanding of machine learning. Here is a course on Udemy that teaches you the math that helpful to know and understand when using machine learning
Pick up some Machine learning certifications
The next step is to try to pick up some certifications. Many jobs are looking for Machine learning skills and having a certification will help you stand out more. Below are five recognized certifications that will help boost your machine learning skills and market value.
- Certificate in Machine learning by Stanford This is probably one of the most popular courses. It’s 11 weeks
and cost $79 to attain a certification. (Coursera)
- IBM Machine learning professional certificate. This is a machine learning certificate offered by IBM. It consist of 6 courses where ya will learn the them aspect of machine learning as well as the hands on. This course is also oh Coursera where you can Audit for free or pay $39 for certificate.
- The professional Machine learning Engineer by Google This particular certification doesn’t • have any courses associated with it all you -4bar to do is pass their test. Although they don’t have a particular course, Gaige still offers training material to help you with the exam. 4 T
- The AWS Certified Machine Learning certificate is a machine learning certificate provided by Amazon. This exam is more aimed at intermittent to senior level individuals, and will cost you around $400 to take. Similar to Google this exam does not come with a curse, so you will have to do your research.
- MIT Professional Machine learning Certificate Program.The last on the list is the professional Certificate program in Machine learning inn by MIT. This certificate does come with a course and is $325 dollars to register. The program consist of 16 or more days of completed courses. You will have a total of 36 months to complete this certification program. click here for more details.
Average Income Level of Machine learning Engineers
The market for Machine learning is booming at an astronomical rate. Companies are looking for individuals with big data, machine learning, and programming skills to help with filter and construct large amounts of data. Below are the typical levels of income you can expect if you plan on going down the machine learning route
- Entry Level- 0-4 Years $97,000- $130,000
- Mid Level- 5- 9 Years $112,000 – $153,000
- Senior Level- 9+ Years $132,000 – $181,000
Machine learning data process.
Now let’s go over the machine learning data process. This will give you an overall veiw of what to expect when you start the machine learning data process.
First you want to identify the problem and create steps to resolve the problem – This step runs in congruent to what you really want to do with Machine learning. You are going to have to gather the data now depending what you’re trying to accomplish, this could be loads of data that will hate to cleaned and applied.
Sometimes data doesn’t come easy, so there will be instances where you will have to create or take out new fields of data. Forth, it’s time to create a model using an Algorithm to analyses your data. There are many Machine learning algorithms to choose from. Here is a list of the most common:
- Linear Regression-
- Logistic Regression
- Decision Tree
- SUM At Gcrthm
- Nave Bayes
- KNN A/ Grithm
- IT Means
- Random forest At Gcrrthm
- Dimensionality Algorithm
to. Gradient boosting
Train the Model- Training your model happens when you feed your algorithm sufficient data to process. You’re basically processing all input data to get output data and then comparing the output data with the sample output data you’re trying to reach. Based on the conclusion you gather from the output data, will be used to modify your model. The better training you present to your algorithm, the more accurate your predictions will be.
Test and improve