Machine Learning, ML is a complex topic to learn if not done in a structured way. Though there are numerous online resources available, most of them have become redundant as ML is evolving quickly. Also, the content has a lot of technical jargon which makes it difficult to understand. To address these issues and to make the process of learning ML easy, follow the below steps.
Understand the basic concepts of ML
If you are a beginner to machine learning, it is essential to know about what it is and how it impacts the world. This will give you a newfound appreciation and motivation to learn even if you are only doing it because it is trending. Once you have understood the basic concepts then start learning about its applications which will help you understand the concepts in a better way.
Python is the most common machine learning programming language and is the first thing that is thought in any machine language course. Get a thorough knowledge of Python with emphasis on data structures which will also help you focus going forward in ML. The reason for choosing Python is that there is a good community/support for this language when compared to others. When you are stuck, these communities can help resolve issues. Other languages you can learn are R, Scala, and Julia.
Learn Data exploration
A good ML professional will know all about data cleansing and feature engineering. Spend a fair amount of time understanding the various data exploration stages like feature engineering, Outlier treatment, Missing values treatment, variable identification, and such. This can also be acquired when you are learning Python or R. A few other things to concentrate on are data analysis, data manipulation, and visualization. Use tools such as Panda for working with data frames, Numpy for performing mathematical operations, and Matpotlib for creating graphs and visualization of data.
Deep Dive Into ML Algorithms
Once you have completed the steps given above, you will be armed with the skills to manipulate and visualize data. It is now time to learn ML algorithms. Scikit-learn can be used which is a library in Python to learn in-built algorithms. It also has many other features and functions that can be utilized for learning. The main focus should be on algorithms used for classification and regression and not on understanding the algorithm. All the resources needed to learn and master are available online, but if you need handholding machine learning training is the only way forward.
After learning the basic machine language techniques, algorithms, and data structures, you can move ahead to more advanced ML topics like Deep Learning and ML with Big Data. These are important as you will be learning to work on data that is unstructured.
Some useful resources for deep learning are Tutorials and resources on deep learning, Basics of neural networks by Geoff Hinton, Text Mining Using Python, and Pattern recognition using Python.
Machine Learning with Big Data
Big data is full of raw data which is not useful in getting the needed insights. Machine learning helps in identifying patterns, generating insights, and also reading from data. A few handy tutorials to learn these concepts are Packages for Big Data in Python and Scalable Machine Learning.
Follow the above steps in sequence to master machine learning. Learning something new especially something like ML and mastering it will need a lot of patience and perseverance. Take your time but master it!