Before discussing the ways in which you can learn all you need to know about machine learning, we would like to discuss what the subject matter actually is. Machine learning is essentially teaching a computer how to make decisions with the help of relevant data. It is very important for the computer to be able to understand patterns without being fully programmed. The demand for machine learning is an all-time high. It is a skill set which you want to possess, especially in this computer savvy era. No matter if you want to be a software engineer, a business analyst or for that matter, a data scientist machine learning will lay a strong foundation for all that and more. It is very important to note, that data is king. From the smallest of companies to giant conglomerations, everyone wants to harness their data, so a course in machine learning will help you get a good job and if not that, then an internship at a good firm. This a one-of-a-kind course, which can be a lot of fun if you go about it right, so pull up your socks and we will point out 7 lessons which can teach you more about machine learning basics without spending a lot of money.
1. Have an understanding of python
Understanding Python programming will take you a very long way as far as machine learning is concerned. If you do not know about Python, then don’t worry, you won’t have to join a course for understanding it. There is a lot of online study material available with regards to Python programming. You can go through tutorials and virtual classes, and gather a basic understanding of the same. Once you have ample knowledge and experience in Python as well as computer programming, machine learning will certainly become easier for you to grasp.
2. Deepen your understanding of statistics
A good understanding of descriptive statistics, data visualization as well as data distribution will take you a long way. This method becomes a prerequisite when evaluating the skill of a machine learning model it also helps when selecting the model configuration as well as the final model. Statistics also comes in very handy, when the presentation of the model is made in front of a stakeholder. Statistics are definitely one of the most important requirements, so browse through your old books, hone your skills by practicing before dipping your toes into the subject of machine learning.
3. Learn as much theory as you possibly can
We know this isn’t the fun part, but once you get through this, you will be able to understand machine learning basics a lot better. Learn the fundamentals of machine learning and go through all the theories, in fact, memorize them. Trust us when we say that they will come in handy. There is a lot of study material available on the internet, so you have no excuses but to absorb all that knowledge and charge ahead into the world of machine learning.
4. Dive into target practice
As the cliché goes, practice makes perfect and machine learning is no different. Start by learning and practicing the workflow. Try doing everything first hand, you will make mistakes, but you will learn from them. Go ahead and practice data cleaning and collection, model building, evaluation, tuning, etc. This way you will become more intuitive with regards to the many models and try to work on your own.
5. Skills which will help to lay a foundation in machine learning
You need to have a deep understanding of the theories and know how to use them in practical application. Having an understanding of algorithms is what you will have to develop eventually with learning and practice. You have to give this subject ample time and invest in a more academic setting. Go through the course notes well, and practice them to hone your skills
6. Go through Python packages
As mentioned before, the understanding of Python programming is absolutely imperative before venturing into machine learning. You should also learn about Numpy, Pandas, and Matplotlib as well.
7. Deep learning in Python
If you want to dig much deeper into machine learning basics, there are few online books which you must go through.
- Theano: a Python library which allows you to evaluate mathematical expressions efficiently.
- Caffe: speed and modularity are kept in mind as far as Caffe is concerned.
Once you go through the aforementioned steps thoroughly, you will definitely develop a fine understanding of machine learning. The use of algorithms in Python, and understanding machine learning about algorithms will become very simple for you.