Machine Learning Tutorial for Beginners

Machine Learning Tutorial for Beginners

This Machine Learning tutorial is intended to introduce some of the basic concepts of machine learning. It will cover the different learning paradigms and some of the popular algorithms used in machine learning. The target audience for this tutorial is students and working professionals who are complete beginners. When we reach the end of the machine learning tutorial, you will be confident about machine learning concepts, tools, and its applications and will be able to make models.

To effectively understand the tutorial, it is imperative to be familiar with pandas, NumPy, and Matplotlib. Pandas will help to work with data frames. NumPy is needed to perform numerical operations on the data, and Matplotlib will help make the graphs and visualizations of the data. There are no other prerequisites to venture into learning the machine learning concepts that would be covered. Listed below are the sub-topics that will form a part of this tutorial:

Table of Machine Learning Tutorial

1.What is Machine Learning?

  1. Need for Machine Learning
  2. Types of Machine Learning
  3. Machine Learning algorithms
  4. Python and its implementation for Machine Learning
  5. Advantages and Disadvantages of Machine Learning
  6. Future of Machine Learning

What is Machine Learning?

Machine learning is a part of artificial intelligence that combines data and statistical tools to predict output and make actionable insights. In this process, the machine learns from the data to produce accurate results. Thus, we can say that Machine Learning is a study of making machines more human-like in their behaviour and decision-making with minimum human intervention.

Need for Machine Learning

The nearly limitless quantity of available data and its complexity has propelled the growth of machine learning. Most industries in today’s time depend on vast quantities of data. The need to analyze the data efficiently and accurately has led industries to embrace machine learning as the best way to build models and draw insights from the data to identify profitable opportunities and potential risks.

Types of Machine Learning

Machine learning is divided into three main areas:

  1. Supervised Learning
  2. Unsupervised Learning
  3. Reinforcement Learning

Supervised Learning

We use known or labelled data in Supervised Learning. Since data is known, the learning is therefore Supervised. The input data passes through the machine learning algorithm and is used to train the model. Once the model is trained based on the labelled or known data, unknown data can be fed into the model to get a new response.

Unsupervised Learning

In this case, training data is unknown and unlabelled. Data is inputted using a machine learning algorithm and is used to train the model. The trained model tries to search for patterns from the data and produce the desired response.

Reinforcement Learning

In this learning mechanism, the algorithm discovers the data through the process of trial and error and then decides which actions lead to higher rewards. The agent, the environment, and the actions are their major components that constitute reinforcement learning. Reinforcement learning happens when an agent chooses actions that optimize the expected reward over a given time.

Machine Learning algorithms

There are several commonly used machine learning algorithms as listed below. These algorithms can be applied to various data problems. The algorithms which are used for various types of machine learning are as follows:

Top algorithms for supervised learning:

  • Linear Regression
  • Logistic Regression
  • K-nearest Neighbors
  • Decision Trees
  • Random Forest
  • Naïve Bayes
  • Support Vector machine

Top algorithms currently being used for unsupervised learning are:

  • K-means Clustering
  • Hierarchical Clustering
  • Principal Component Analysis
  • Singular Value Decomposition

The choice of algorithm is a tough task and will depend on the objective of the problem you are trying to solve. There are plenty of machine learning algorithms that can be put to use depending on the problem statement at hand to be solved.

Python and its implementation for Machine Learning

Python is an easy to learn, powerful programming language. Machine learning with python dives into the basics of machine learning using an approachable programming language. Machine learning applications involve complex concepts like linear algebra and calculus. The use of python helps in reducing the complexity and quick implementation of these concepts. Python also has pre-built libraries, which further makes the task simpler.

Machine learning has both its advantages and disadvantages as enumerated below.

Advantages of Machine Learning

  1. Easy identification of trends and patterns- Machine learning can review large amounts of data and discover trends and patterns that would not be apparent to humans.
  2. Automation- Machine learning enables machines to learn without much human intervention. It allows the machines to learn, make predictions, and improvements on their own.
  3. Continuous improvement- Machine learning is a continuous improvement process. As machine learning algorithms are exposed to more and more data, it learns and gains from experience thus improving accuracy and efficiency. This process also enables the machines to produce better results leading to better decision-making by business owners.
  4. Handling multi-dimensional and variety of data- machines can learn and produce results in dynamic and uncertain environments.
  5. Wide applications- Machine learning has a wide range of applications and is capable of delivering personal experience and targeting the right set of customers.

Dis-advantages of Machine Learning

  1. Data acquisition- machines need a large amount and good quality data which is challenging from time to time.
  2. Time and resources- machines require time and vast resources to train the data. It involves time and space complexity and hence this needs to be addressed on an ongoing basis.
  3. Interpretation of results- sometimes it is a major challenge to accurately interpret the results generated by algorithms. Choice of the algorithm is critical to produce appropriate results and infer from them.

Future of Machine Learning

Machine learning is an incredibly powerful tool and essentially the future. In the coming years, it promises to open up a whole new world of opportunities. It will continue to grow and is a revolution that will stay with us for long. This brings us to the end of the machine learning tutorial. We hope that you were able to learn a few of the basic concepts in this domain.


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