How do I learn Machine Learning?

Machine Learning is the brain behind business intelligence. Machine Learning (ML) is a rapidly evolving field and the algorithms are increasingly being built keeping businesses in mind. Industries globally are making solid investments by applying Artificial Intelligence (AI) in their domain and using cutting edge technologies in various verticals, such as, cybersecurity, face recognition, etc. Machine learning uses the smartest algorithms which can automatically learn and improve from previous experiences and computations. This sizzling hot technology helps businesses meet their high-dimensional modern challenges by producing reliable decisions and results.

Machine learning falls under the umbrella of artificial intelligence and refers to the ability of systems to independently adapt by identifying patterns and making decisions independently using algorithms, with minimal human intervention. A real-time example to explain the wonder that is machine learning is Netflix. They use the sophistication and ingenuity of machine learning algorithms to personalize recommendations for customers over a heterogeneous language environment and cater to millions of viewers with specific needs and interest who are located all over the globe. 

Getting Started – Steps to learn machine learning

• Understanding math: It is imperative to have a solid foundation in mathematics to start the ML journey. Having a good understanding of the concepts will help help you gain a better understanding of the machine learning algorithms. 

Some of the concepts you need to master include:

• Linear Algebra – Learn the concepts of algebra, linear equations, matrix analysis, etc. 
• Calculus – Calculus is required to understand machine learning techniques and applications. You will be using calculus to calculate derivatives for optimization models
• Statistics – Knowledge of descriptive statistics and inferential statistics is required to draw out inferences and conclusions after the analysis of the data
• Probability: Knowledge of probability helps you derive insights from an available pool of data

• Understanding Machine Learning Algorithms: There are three widely used machine learning methods:
• Supervised learning: Machine learning algorithms consist of an outcome variable which is predicted from a given set of predictors. These help in generating a function that maps inputs to the desired output. Example: Regression, Decision Tree, KNN, etc.
• Unsupervised learning: This algorithm is used to cluster the population in different groups to segment customers into different groups. Example: K-means.
• Reinforcement learning: This algorithm is used to train machines to make specific decisions. The machine is trained to adapt and learn from its experiences to make best and accurate decisions based on knowledge capture. Example: Markov Decision Process.

Below is the list of commonly used Machine Learning Algorithms:

1. Linear Regression
2. Logistic Regression
3. Decision Tree
4. SVM
5. Naive Bayes
6. KNN
7. K-Means
8. Random Forest
9. Dimensionality Reduction Algorithms
10. Gradient Boosting algorithms

• GBM
• XGBoost
• LightGBM
• CatBoost
• Programming: There is a huge list of programming languages you can choose from, when it comes to machine learning. The language you must learn hugely depends on the project you’re working on. Programming is imperative because it helps you code in a language understandable by the machine. The most popular languages you could start with are Python, Java, and R.
• Books are your friend: Read as many books as possible and keep increasing your knowledge. Your main focus should be improving your technical knowledge and reviewing as many platforms as possible to understand the concepts. 
• Acquaint yourself with a community of experts: Participate in communities of machine learning experts because doing so will help you enhance your knowledge of technology and concepts. It would also help you in improving your network and providing you with the ins and outs of the industry. 
• Build your own project: Once you understand your machine learning tools, it is time to build your project and practice. You can take up and work on a variety of project ideas that employ different machine learning libraries, tools, etc. The community of machine learning professionals is really very active and supportive, and could possibly also assist you in suggesting good project ideas. 

These are some of the basic steps to understand Machine Learning Training and start your ML journey. However, it is important to understand that this is just the beginning and there is so much more to learn and understand in the field of machine learning. One of the most important takeaways from this blog must be the scope for continuous improvement and learning. Machine Learning is a huge topic with a wide array of selection. The key to becoming an expert is to keep learning.