Machine Learning Tutorial- A Complete Guide For Beginners

April 19th, 2024

Explore this machine learning tutorial crafted for beginners aiming to have a career in this field!

Machine Learning Tutorial

A sub-division of artificial intelligence, machine learning (ML) is a technology that builds computer systems that train on data. It has become a highly adopted aspect in businesses today, leading to numerous career opportunities. The kind of growth graph it has showcased in recent years speaks volumes on how its future could look like.

Its increasing use in different industries is all the push one needs to explore this field. This machine learning tutorial covers everything from what it is to its types and its features to why one should learn it.

Machine Learning Tutorial- Introduction to Machine Learning

Anyone who is aware of AI knows about ML too. In fact, these two share a unique relation to ensure success for organizations.

ML apps learn from existing data just like a human brain. Hence, it does not need to be directly and explicitly programmed everytime something new is inserted. Machine Learning algorithms learn by themselves and use pattern recognition to drive out reliable insights.

This introduction of machine learning is incomplete without understanding its history, features and types.

To learn more, you can enroll in our most opted Machine learning certification training program.

History of Machine Learning

Machine Learning History
Image Source- Marketcalls

The history of machine learning is quite amazing! The first even mention of the term ‘machine learning' was by Arthur Samuel in 1959. This IBM employee was a pioneer in AI and computer gaming.

However, it all began in 1943, when mathematician Walter Pitts and neurophysiologist Warren McCulloch wrote a paper on neurons and their working.

The Turing test was created in 1950 by Alan Turing. The very first computer learning program was written in 1952 by Arthur Samuel. In early 1960s, Cybertron, an experimental learning machine, which used rudimentary reinforcement learning, was developed by Raytheon Company.

The world has not looked back since then! ML has grown exponentially and is still said to be in its evolution phase.

How Does Machine Learning Work?

So, how does machine learning work?

How Does Machine Learning Work

ML utilizes a systematic approach for predicting new values by following a set of steps. These steps are -

  1. Data Collection- The data quality is imperative in determining the accuracy of the predictions. Data sets can be built-in or collected from websites, APIs, social media, etc.
  2. Data Preprocessing- Missing and duplicate values are removed, format is standardized and outliers are dealt with.
  3. Model Training- An algorithm is used to model the data set, which is divided into two parts namely training and testing sets. Different models and techniques are used here.
  4. Model Evaluation- This step determines the accuracy of the model. It is tested via different techniques and metrics.
  5. Model Deployment- Trained models are integrated into real-world issues with the aim of solving them. The models are practically used here.

You May Also Read- Top 6 AI and Machine Learning Trends For The Upcoming Year

Features of Machine Learning

There is no dearth of features of machine learning that make it such a sought-after technology. Here are five of those.


ML algorithms automate various processes like pattern finding in data. Consequently, less human involvement is required, which renders the analysis effective and precise.


Data is always changing and updating. ML algorithms automatically learn and adapt from it continuously. This enhances their performance over time, enabling them to become more efficient and precise.


ML techniques are crafted to process big data easily. This helps them in gaining better insights through gigantic data sets covering diverse information.

Predictive Modeling

ML algorithms use data for curating models with the prowess of forecasting future events. After being trained on humongous data sets, they become better at predicting the likely patterns.


These algorithms gain the capability to discover broad patterns and likeliness of outcomes, which are then used to analyze new and unexplored data. The goal is generally long term and not short term.

Types of Machine Learning Algorithms

There are basically four types of machine learning algorithms that one needs to know about at the beginning of their career. This machine learning tutorial covers all four of them.

Machine Learning Algorithms

Supervised Learning

Supervised learning, in simpler terms, refers to the ML approach wherein algorithms learn from and train on labeled data. Input data, along with corresponding correct output labels are fed to the algorithms. The goal is to train these algorithms to accurately predict labels for unseen data.

Some common examples of supervised learning are Decision Tree, Logistic Regression, Regression, KNN and Random Forest.

Unsupervised Learning

Unsupervised learning is the ML approach where the algorithms analyze unlabeled data that do not have predefined output labels. This leaves space to discover structures, relationships or patterns within the data.

Some common examples of unsupervised learning are K-means, apriori algorithm and hierarchical clustering.

Semi-Supervised Learning

Semi-supervised learning brings together labeled and unlabeled data to train algorithms. Limited labeled data is used with a broader set of unlabeled data to enhance the learning process. This type helps in overcoming the limitations tagged with only labeled data. These techniques work on tasks like regression, anomaly detection and classification.

Reinforcement Learning

Reinforcement learning reflects the learning pattern of humans i.e., trial and error. The machine here is exposed to a new environment, learning from past experiences and making changes in its ways accordingly. The Markov Decision process is an example of reinforcement learning.

You May Also Read- 20 Most Important Machine Learning Interview Questions

Why Do We Need Machine Learning?

Another important question that arises here is- why do we need machine learning?

  1. Accurate Predictions

Accurate predictions about customer preferences, the economy, the stock market, etc. are essential. Right implementation of ML algorithms makes it possible to identify patterns and trends, helping organizations and individuals.

  1. Speech Recognition

ML has helped speech recognition apps in enhancing the interpretation of voice-based inputs received from customers and other sources. This aids in decreasing the need to have humans do this task or recognition and interpretation. Consequently, the errors also go down.

  1. Autonomous Vehicles

With ML, vehicles can learn safe navigation in the real world. As ML gets better with time, these autonomous vehicles will also become better. This will ensure safer roads with less disruptions and collisions.

  1. Detecting Frauds

Many organizations and fields like banking, finance and education face fraud issues quite often. With ML algorithms, these frauds can be detected early through patterns and change in activities.

  1. Better Products

Companies want to appease their customers but the data is too huge to work with. In fact, making changes after something is done is a hefty task. With ML, predictions about trends and wants can be made way in advance.

Final Words

This machine learning tutorial is to throw light on the advantages organizations can get by implementing this technology. Those who learn machine learning with Python get better career opportunities as this programming language has various applications. Journey towards a bright future goes through ML.

Drop Us a Query

Fields marked * are mandatory

Your Shopping Cart

Your shopping cart is empty.