How to start with Machine Learning ?

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The journey of machine learning started in 1959 when Arthur Samuel introduced the term called Machine Learning. It is defined as a Field of study that gives computers the capability to learn without being explicitly programmed. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. The main aim is to allow the machine to learn automatically from the examples that have been provided during learning.

Now, when the term Machine Learning has become familiar to everyone and has become the most popular career and research choice as it is getting adopted by many industries, it has become important for everyone working in all industries to learn and explore Machine Learning and see what it has to offer. Machine Learning engineer is surveyed as the best job of 2019 and has shown the growth rate above 300%.

Before you start learning something new, it is always better to know what background knowledge required to make the learning easy. So, this article will help you to set a path that you have to take to become a full-fledge Machine Learning Engineer. Following are the ideal three steps (you can modify them according to your desired goals) one should follow to learn Machine Learning and to reach the desired goals.

Linear Algebra is the key foundation in the field of machine learning. It is the sub-field of mathematics that deals with matrices, vectors, and transforms. The extent to which you should learn linear algebra depends on your focused work. If you going to apply machine learning algorithms directly, then basic knowledge will be sufficient. But if you are planning to create something new from scratch and want to focus on R&D then deep knowledge of Linear Algebra is essential. To improve your functionality in machine learning a few linear algebra topics such as Notation, Operations, and matrix factorization are important.

Calculus plays a major role in many of the machine learning algorithms. As a data scientist, one always gets curious about how a particular algorithm works. Calculus will help you to understand machine learning algorithms such as gradient descent, backpropagation, etc.

Learning Python is very essential to start with machine learning. The journey of Python started in the late 80s and the first implementation started in December 1989. Since then, there is no looking back for Python. It is the language that has shown tremendous growth by overtaking languages like Java, C, etc. and still the popularity is increasing day by day. There are many other programming languages like R, Scala, etc. are available for machine learning, but the growth and functionality Python has shown in machine learning is tremendous. This is because the libraries that Python provides are very useful for machine learning purposes. Libraries such as Scikit-learn, TensorFlow, Keras, etc. are dedicated to machine learning.

The reason behind the popularity of Python is simple syntax. As a result, it can be learned by anyone through various resources available online and offline.

Statistics is the field that handles the collection, analysis, presentation of the data in a suitable form. Data plays a very big role in Machine learning as most of the machine learning work circles around collecting the relevant data, cleaning the data, and putting the data into machine learning algorithms.

In statistics, knowledge of probability distributions, statistical significance, hypothesis testing, regression, Bayesian thinking, conditional probability, priors and posteriors, and maximum likelihood is important. It is not necessary to learn of all the above-mentioned topics all at once. It can be learned gradually along with experience and as per your need.

After learning all the prerequisites, its time to start learning machine learning. Once you acquire the knowledge of Python, Machine Learning is all about learning some terminologies and applying algorithms that are pretty much easy to understand. Following are the step that you should learn to get hands-on machine learning,

Data is an essential part of machine learning as most of the machine learning work circles around collecting, arranging, and cleaning of the data. For building a machine learning algorithm, you have to collect the data by yourself or get the data from a reliable source. The data is then divided into three categories i.e., training data, validation data, and test data. As the name says, training data is used to train the data and it has a share of around 60–80% of the dataset. By learning from training data, the machine learning model tries to detect important features and determine the pattern of the data. Validation data is used to validate the model by comparing models based on different machine learning algorithms to determine the best ones by comparing parameters like efficiency. Test data is a set of data that is kept completely aside during training and validation. It is used on the final model to simulate the model’s behavior.

It is a representation of the hypothesis which has been generated after feeding the training data to the machine learning algorithm.

It is the most basic type of machine learning type and used by many machine learning users. In supervised learning, a model learns by examples. The training data consisting of 60–80% of data set with clearly defined output or also known as labeled data is given to the model and direct feedback is given. Based on the output, supervised learning is classified into the classification and regression problem. In a classification problem, the output variable falls in two particular categories such as “yes” or “no”. In the regression problem, the output variable consists of real value. The algorithms which follow under supervised learning are Decision Trees, Naive Bayes Classification, Support vector machines for classification problems, Random forest for classification and regression problems, Linear regression for regression problems, Logistic Regression, etc.

In unsupervised learning, a model teaches itself by observation. In this, you only have to feed input data and no corresponding output data are to be feed. This is different from supervised learning as there is no correct answer as the algorithm tries to discover a hidden structure in the data. With various assigned weights, the algorithm tries to find the relationship between different inputs. This quality of unsupervised learning makes it versatile with its dynamic working.

“Clustering” is the most important type of unsupervised learning techniques. In clustering, the model tries to create different clusters of input data and later it can fit any unseen data in the appropriate cluster. K- means for clustering is the most used algorithm.

Reinforcement learning resembles a human being learning technique. It improvises itself with every input data by using a trial and error method. It works like a weighted average method where favorable outputs are given credits or ‘reinforced’ and non-favorable outputs are punished. The algorithm tries different approaches by using trial and error method and tries to reach the favorable or expected output.

Once you finished Machine learning basics and various terms and algorithms, it is time to know more about machine learning and its implementation in various industries. Also, practicing different machine learning algorithms and finding ways to improve its efficiency is important. To improve yourself in programming, it is very important to run different models and learn from the error and mistakes. The following are the steps one should take to practice machine learning,

Collection, cleaning, and preprocessing of data consumes most of the time. So, it is important to practice collecting high-quality data rather than wasting most of the time cleaning the data. Most of the data are often dirty so it is important to set some standard practices for yourself to clear data on which analysis can be done. There are various online sources which provide real data and can be used for practicing purpose.

After getting a data set, it is important to practice different models on the same data set to know and compare the results. This will help you to understand which model works better and how results or accuracy can be improved on a particular type of data set which sometimes can be a tedious process.

It always better to learn from other’s code. Such a practice will help to understand how theoretical knowledge can be implemented in practice and to get the most out of it. It will prepare you for various challenges you may face in the future. Another way of improving and learning is to take part in different competitions on platforms such as Kaggle.

After finishing all the above steps, you have set a path to become a full-fledge machine learning engineer. In the journey, you can continue to learn more and keep enhancing your skills by taking a new challenging project and building more complex and efficient models.

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