Essential Steps to Mastering Machine Learning with Python
Python enthusiasts are increasing in number day after day, especially in the last five years. It’s popularly favored over other programming languages, due to its concise design that allows code readability, increased productivity, and is easy to use. This open-source, object-oriented, reusable programming tool allows coders to write ML algorithms with ease which are computed pretty accurately. Its many versatile, ready-for-use libraries just add to the list of bonuses, as to why it’s advisable to turn to Machine Learning with Python training. Ready to get started? Let’s take a look at the essential steps to becoming a master in Machine Learning with this programming language.
#Step 1: Get the Basics of Python into Your Grip
Before you dive into the world of Machine Learning you need to bag a programming language. Python is preferable as it is a general-purpose programming language, easy, and is popularly in demand. There is plenty of books, online training, and certifications that can help you out here. You don’t necessarily have to become an expert, even bagging the basics of the language will be just enough.
#Step 2: Data Collection, Pre-Processing, and Analysis
Before you start with your Machine learning, these are skill sets that need special attention. Or you might become an expert but falter when asked to perform simple tasks like preparing the data for machine learning or sourcing it for third parties.
First, with the help of APIs or building web scrapers you need to become accustomed to external data collection, which is what most of today’s companies demand. Once you know the ABCs of Data Collection, you also need to learn to be able to transform this data into an ingestible format that is familiar to the machine learning models. When working with Machine Learning you need to understand the data in use. Find patterns, provide insights and learn to work with them, as Data Analysis is a major key to Machine Learning.
#Step 3: The Basics of Machine Learning
Now that we’re finally here, don’t dive headfirst into the extensive theories. To gain a certain amount of theoretical knowledge, your priority should be to experiment. Work models on datasets in the real world, build predictive models with the python packages, and so on. Understand what the world of machine learning looks like with implementation first. Study up on techniques, before you do on the methods and algorithms.
#Step 4: Understanding the Algorithms – Foundational to Deep Learning
Now you can delve deeper and understand the algorithms in work behind these predictive models. Some of the major algorithms you can look into are – Regression, Clustering, Classification, Ranking, Decision Tree, Association Rule Learning, etc. These will help you in building a strong foundation for machine learning. Once you are done with these ‘shallow’ algorithms, it’s time for some deep learning. Learn the benefits, applications, Artificial and Deep Neural Networks, and work further into the world of Deep Learning Algorithms.
#Step 5: Get Working!
To keep in tune with all that you learn, you need to put it to use. Build projects, get innovative! Make sure you put all your knowledge to complete use and work them up in your real-time projects.
There’s a lot of learning and practice to be done before you become an expert in Machine Learning. But get started right away as the rewards will more than make up for it.
