How to Start with Machine Learning?

In today's world, every day, we are getting new software, applications, gadgets, and many more technologies that make our lives easier and fast. New technologies are evolving day by day and changing the way of doing a task done in the traditional way, and one of such technologies is Machine Learning. Although machine learning is not a new technology, it is coming with new surprising innovations every day, making it one of the most popular and demanding technology among each tech enthusiast.

Currently, machine learning has become one of the best career choices among beginners and professionals. It is one of the best careers with great growth chances and packages per various surveys. However, many of us have difficulties taking a start with machine learning. Lots of beginners and professionals want to make their career in this field. Still, they don't have sufficient information as to how they can start, what should be the best path for machine learning, what technologies and mathematics they should know, and many more. So, in this topic, we will discuss how you can start with machine learning, what the prerequisites should be, etc. Let's start with the basic understanding of What Machine Learning is?

What is Machine Learning?

Machine Learning is a subfield of Artificial Intelligence that teaches machines how to make predictions using past data and experiences. For true machine learning, it doesn't need any human intervention and explicit programming to learn from data and identify the pattern. It combines or overlaps with other fields, including statistics, Mathematics, Data Science, Big data, etc. For example, different machine learning algorithms are used in data science.

A typical machine learning process starts with feeding good quality and pre-processed data; algorithms learn from this data and build the model to make predictions. The use of algorithms depends on the type of task or problem.

Machine Learning is broadly classified into four types:

1. Supervised Machine Learning

Supervised Learning is a type of machine learning that teaches machines with the labeled dataset. With the labeled datasets, the model makes predictions and provides an accurate output. Supervised learning helps organizations to solve various real-world problems, such as classifying emails as Spam or Not-spam.

Supervised machine learning is mainly classified into two types of problems, which are:

• Regression
• Classification

Supervised learning Algorithms:

Some of the commonly used supervised learning algorithms are as follows:

• Naïve Bayes
• Linear Regression
• Logistic Regression
• Support Vector Machines
• K-Nearest Neighbour
• Random Forest
• Neural Networks

2. Unsupervised Machine Learning

Unsupervised learning is a type of machine learning which uses an ML algorithm to analyze and group the unlabeled dataset. This machine learning technique aims to find the hidden pattern from the given dataset and group them together as per the similarities and differences between the data point. As it can find the hidden pattern from the data, it is suitable for complex tasks such as customer segmentation, image recognition, exploratory data analysis, etc.

Unsupervised learning technique is mainly classified into two types, which are:

• Clustering
• Association Learning

Unsupervised Learning Algorithms

Some of the commonly used Unsupervised Learning algorithms are as follows:

• K-means clustering
• KNN (k-nearest neighbors)
• Hierarchical clustering
• Anomaly detection
• Principal Component Analysis
• Independent Component Analysis
• Apriori algorithm
• Singular value decomposition

3. Semi-supervised Machine Learning

Semi-supervised learning combines both supervised and unsupervised learning techniques as it involves training of algorithm with an unlabelled dataset similar to unsupervised learning with a small amount of labeled dataset similar to supervised learning. It improves or overcomes the limitations of supervised and unsupervised learning. It enhances the learning accuracy of the model and is also much more cost-effective than the supervised learning technique.

4. Reinforcement Machine Learning

Reinforcement Learning is a feedback-based Machine learning technique in which an agent learns to behave in an environment by performing the actions and seeing the results. For each good action, the agent gets positive feedback, and for each bad action, the agent gets negative feedback or a penalty.

In Reinforcement Learning, the agent learns automatically using feedback without any labeled data, unlike supervised learning. RL solves a specific type of problem where decision-making is sequential and the goal is long-term, such as game-playing, robotics, etc.

How to start with Machine Learning: Learn ML by Self.

Now, let's directly jump to how one should start learning Machine Learning on their own. So below are the main four steps you should follow if you are a very beginner in this field and starting to learn on your own.

1. Step-1: Understand the Prerequisites
2. Step-2: Understand the Essential theory behind Machine Learning
3. Step-3: Practice the essential topics
4. Step-4: Build Machine Learning Projects

Now let's understand each step in detail.

Step-1: Understand the Prerequisites

Machine Learning might appear a scary field of study if you skip to understand and learn about its prerequisites, as these perquisites build a base for your learning, create interest in this field, and also boost your confidence for further. So, it is really important to first create your strong base by learning and understanding the prerequisites for Machine Learning. After completing these prerequisites, you will be able to understand the further concepts in a much easier way, irrespective of your educational background.

Below are some important prerequisites for Machine Learning:

• Mathematics for Machine Learning: Linear Algebra & Multivariate Calculus: All the machine learning concepts are based on Mathematics and Statistics; hence it is very important that you have a good understanding of some mathematics concepts and Statistics. In Mathematics, Linear algebra and Multivariate Calculus are the two important sections to understand. However, you don't need to be proficient or have a Ph.D. degree in these concepts, but a basic understanding with some practice is required.
• Statistics: Statistics is one of the core concepts of mathematics required to get started in the machine learning industry. As a machine learning professional, you must have a deep understanding of data-role because you are going to spend your maximum time playing with data analysis, collection, and cleaning. Hence, statistics is a field of mathematics that helps you a lot in preparing data to use in real-time Ml projects. So, before learning machine learning, you need to learn statistics as a prerequisite. It includes various important concepts required for ML, such as Statistical Significance, Probability Distributions, Hypothesis Testing, Regression, etc.
• Programming Language (Python/R): When we talk about the most important machine learning prerequisites, we cannot skip programming language, as it is one of the essential parts of learning machine learning. Among various programming languages, Python and R play an important role in learning ML, deep learning, and artificial intelligence. There are various Python libraries that play a crucial role in developing ML and AI projects, such as Keras, tensor-flow, Scikit-learn, NumyP, etc.

Sometimes, lots of people skip learning Linear algebra & Multivariate Calculus, and statistics, but you cannot ignore Python. However, there are a few languages used in ML-like Scala, Ruby, etc., but currently, Python and R are the most popular among them.

Step-2: Understand the Essential theory behind Machine Learning

After getting an idea about important prerequisites for learning Machine learning, it's time to focus on the essential machine learning concepts. To make your career in the ML industry, you need to know all essential concepts from beginning to advanced level. So, we have compiled a few important ML concepts and theories behind machine learning to help those who have decided to make a career in this domain. These are as follows:

1. Techniques in Machine Learning:

Machine learning is categorized mainly into four types as follows:

• Supervised learning
• Unsupervised learning
• Semi-supervised learning
• Reinforcement learning

2. Key terminologies used in ML:

Before landing in any domain, you must be aware of their key terms to understand their concepts very well. Similarly, in machine learning, there are so many important terminologies that you should have a look at, which are as follows:

• Model: Machine learning is a field of artificial intelligence that is used to develop intelligent models for predicting future results based on experience or previously used data. A model is just a hypothesis that is developed using machine learning algorithms and provides awesome results for future outputs.
• Training: It is a process of teaching machine learning models and preparing them to predict output based on given input data. The entire training mechanism is based on a given input (feature) which is responsible for getting the desired output. Hence, after training, we will have a model that will map new data to one of the categories trained on.
• Feature: A feature is defined as the input data required to train an ML model. Further, to be more precise, the feature is one column of the data in your input set. For e.g., if someone is trying to predict the type of product chosen, your input features might include quality, color, etc.
• Target: It is defined as the predicted output given by the ML model. It is also known as a label or final choice from the model. In the above-mentioned product example, the target with each set of inputs would be the name of the product, like the game product, jewelry product, fashion product, etc.
• Prediction: It is defined as the final desired output after training a model with given input data sets. If we get the exact prediction as actual, then our model is said to be an ideal machine learning model.

3. Resources to Learn Machine Learning Concepts

To understand and learn about the essential theory and concepts of Machine Learning, there is a variety of courses and books available online. These courses are provided by well-known universities and platforms like Udemy, Coursera, etc.

The two most popular courses in Machine Learning are given Below:

1. Machine Learning by Stanford University

This is one of the best courses on Machine Learning instructed and created by Andrew Ng, a pioneer in Machine Learning and Artificial Intelligence as well as one of Coursera's founders. The best thing about this course is that it is a freely available online course, and it provides a strong and clear explanation of the core concepts of Machine Learning.

2. Machine Learning A-Z: Hands-On Python & R In Data Science

After learning the basic concepts, if you want to spend some amount of money, then firstly go for the "Machine Learning A-Z course" on Udemy Platform. This will brush up on your theoretical concepts as well as give you practical exposure to implementing various ML algorithms, including simple linear Regression, Logistic Regression, Confusion Matrix, etc.

Apart from these courses, there are some popular books for Learn Machine Learning Concepts:

• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (First Edition): This is easily available and one of the most popular books among beginners of Machine Learning. Although it requires Python Programming knowledge as a prerequisite, it gives a clear explanation of mostly used ML libraries such as Keras, Scikit-Learn, and TensorFlow.
• An Introduction to Statistical Learning (with applications in R): As discussed above, to understand the ML concepts, it is very important to have a clear understanding of statistical and Mathematical Concepts. So, this book is one of the best tools for understanding the core concepts of statistical Learning. It enables both statisticians and non-statisticians to easily grasp all the concepts.

Step-3: Targeted Practice- Practice on Datasets

After learning the basic and required concepts of Machine Learning, the next step is to do practice on various core concepts and datasets. The targeted practice involves the below points:

• Targeted Practice on ML workflow: The first part involves practice on typical machine learning workflow, which involves data pre-processing, building datasets, model training and refinement, evaluation, and model deployment. Each step of the workflow requires a good time, resources (System, software, GPUs), and practice to understand and become the best of others.
• Targeted Practice on real datasets Each ML problem is different from the others, and hence the algorithm and dataset to be used are different for each problem. So, it is very important to practice with a good dataset in order to understand which type of data will be suitable for a specific problem. For building a strong foundation, you can use the dataset below resources:
  • UCI Machine Learning Repository. Here you will get the complete information of each dataset, and these datasets can be easily downloaded as ASCII files or CSV files.
  • Kaggle dataset: It is one of the best platforms to find, analyze, and explore different types of datasets. It provides high-quality datasets with different formats, and these datasets can be easily downloaded. This platform also hosts various data science competitions in which anyone can participate.
• Targeted practice on some individual concepts: Now, practice on individual algorithms and understand where they can be used as best. For example, you learned about Regression algorithms in Step-2, then you learned some more about these algorithms in Step-3 and now practice with different datasets on regression algorithms to see the performance of the algorithm.

Step-4: Build Machine Learning Projects- Take Part in Competitions

After completing the above steps, it's time to move on to evaluate your ML skills by building different ML projects and taking part in competitions.

This step will make you proficient in machine learning as you will be able to use your theoretical concepts with practical implementation. There are some popular projects available online that you can complete, which are given below:

• Titanic - Machine Learning from Disaster:
  This project is one of the most popular competition projects on the Kaggle platform for practicing Machine Learning. In this project, you will get a great tutorial on various concepts such as exploration, feature engineering, and model tuning.
• House Prices - Advanced Regression Techniques
  This is another beginner-level project for those who have completed ML basics and have some experience in Python or R.

Conclusion:

In this way, if you follow the above-given steps, then you will have great ML skills in comparison to others. Through this learning path, you will become a full-fledged Machine Learning Engineer, and you can continue practicing to enhance your skills for working in a more challenging environment.