What is Machine Learning? Machine learning is the process of making systems that learn and improve by themselves, by being specifically programmed. The primary objective is to develop algorithms that can gather and utilize data to enhance their knowledge. These systems analyze data patterns and use the insights to make important decisions independently.

Machine learning has enabled the development of design systems that can mimic human-like thinking, allowing them to perform various tasks such as:

• Object and image recognition
• Detecting fake news
• Understanding written or spoken words
• Creating website bots that interact with humans just like humans
• Powering self-driving cars

Machine Learning Steps
Imparting intelligence to machines may seem daunting, but it can be broken down into 7 major steps:

1. Collecting Data
   The initial learning of machines relies on the data provided. Collecting reliable data is crucial for the machine learning model to identify accurate patterns. The quality of the data influences the model’s accuracy. Incorrect or outdated data can lead to irrelevant or inaccurate predictions.
2. Preparing the Data
   After obtaining your data, the next step is to prepare it. This involves:
   Combining all the data and randomizing it to ensure even distribution, preventing any bias in the learning process due to the data order.
   – Combining all the data and randomizing it to ensure even distribution, preventing any bias in the learning process due to the data order.
   – Cleaning the data by removing unwanted information, handling missing values, eliminating duplicate values, performing data type conversions, and potentially restructuring the dataset by adjusting rows, columns, or indices.
   – Visualizing the data to gain insights into its structure and understand the relationships between different variables and classes.
   – Splitting the cleaned data into two sets – a training set and a testing set. The training set is used for the model to learn from, while the testing set is employed to assess the accuracy of the model after training.
3. Choosing a Model
   A machine learning model plays a critical role in determining the output when applying a machine learning algorithm to the collected data. Selecting a relevant model is essential for the task at hand. There are diverse models suited for tasks such as speech recognition, image recognition, and prediction. Additionally, it is crucial to consider whether your model is well-suited for numerical or categorical data and make the appropriate choice based on your specific needs.
4. Training the Model
   Training is the pivotal step in machine learning. During training, you feed the prepared data to your machine learning model, enabling it to identify patterns and make predictions. This process enables the model to learn from the data and achieve the set task. As the model undergoes training over time, it continuously improves its predictive capabilities.
5. Evaluating the Model
   Once your model is trained, it’s crucial to assess its performance. This is achieved by testing the model on previously unseen data, specifically, the testing set you separated earlier. Testing the model on the same data used for training would yield inaccurate results, as the model is already familiar with the data and would identify the same patterns it learned during training, leading to artificially high accuracy. By using the testing data, you obtain a reliable measure of your model’s performance and speed. This evaluation provides a better understanding of how well your model will perform in real-world scenarios.
6. Parameter Tuning
   After creating and evaluating your model, explore opportunities to improve its accuracy. This involves tuning the parameters within your model. Parameters are variables that the programmer can set, and they influence the model’s behavior. By adjusting these parameters, you can find the values that result in maximum accuracy for your specific task. This process of finding the optimal parameter values is known as parameter tuning.
7. Making Predictions
   Finally, once your model is trained and optimized, you can confidently use it to make accurate predictions on unseen data. This ability to make reliable predictions is the ultimate goal of machine learning, allowing you to apply your model to real-world scenarios and gain valuable insights.

Exploring the Different Types of Machine Learning
Machine Learning has become a crucial component across various industries, employing a multitude of algorithms to construct sophisticated models. These algorithms can be categorized into distinct types, each serving specific purposes. In this article, we will explore the different types of machine learning, providing you with valuable insights into this rapidly evolving field.

What is Machine Learning?
Machine Learning, a subset of Artificial Intelligence, empowers systems to learn and tackle specific challenges by analyzing extensive datasets. By employing computer algorithms, these systems continuously enhance their performance through experience and data-driven insights.
Types of Machine Learning:

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning

Supervised Learning
Supervised learning is a machine learning technique that relies on labeled data to train models. With labeled data, the corresponding outputs are already known, and the model’s task is to map the inputs to their respective outputs.
An example of supervised learning is to train a system that identifies the image of a plant.
Some of the most popularly used supervised learning algorithms are:

• Linear Regression 
• Logistic Regression 
• Support Vector Machine
• K Nearest Neighbor 
• Decision Tree
• Random Forest
• Naive Bayes

In supervised learning, algorithms work with labeled inputs to map them to known outputs, where the target variable is already known. Supervised learning methods require external supervision to train machine learning models, hence the name “supervised.” They rely on guidance and additional information to achieve the desired results.
These algorithms are commonly used for solving classification and regression problems. Some popular applications include weather prediction, sales forecasting, and stock price analysis.

Unsupervised Learning
Unsupervised learning is a machine learning approach that utilizes unlabeled data for training. Unlike supervised learning, where the output is known, unsupervised learning relies on the model to discover patterns and features in the data on its own and generate output.
For instance, an unsupervised learning technique can be used to classify images of vehicles into buses or trucks. The model learns by identifying various features of the vehicles, such as length, width, front and rear end covers, roof hoods, and types of wheels. Based on these features, the model can classify the vehicle as a bus or a truck.
Choosing the appropriate algorithm depends on the specific problem you aim to solve. Here are some typical instances of unsupervised learning:

• K Means Clusterin
• Hierarchical Clustering 
• DBSCAN 
• Principal Component Analysis

In unsupervised learning, patterns, and trends in the data are identified to determine the output. The model labels the data based on the input data’s features. Unlike supervised learning, unsupervised learning techniques do not require any supervision during the training process. They learn and predict the output independently.
Unsupervised learning is applied to clustering and association problems. Some applications of unsupervised learning include customer segmentation, where similar customers are grouped based on behavior, likes, dislikes, and interests. Another example is churn rate analysis, which helps identify potential customers likely to stop using a service or product.

Reinforcement Learning
Reinforcement Learning involves training a machine to take actions that maximize rewards in a specific situation. It uses an agent and an environment to produce actions and rewards. The agent aims to reach an end state, but there may be multiple paths, similar to a maze. Unlike supervised learning, there is no predefined target variable in reinforcement learning. An example of reinforcement learning is training a machine to identify the shape of an object from a list of different objects.
Here are some important reinforcement learning algorithms:

• Q-learning 
• Sarsa 
• Monte Carlo 
• Deep Q network

Reinforcement learning employs trial-and-error approaches to achieve the desired outcome. When the agent accomplishes a task, it receives a reward, like training a dog to catch a ball and rewarding it with a biscuit upon success.
Reinforcement learning does not require external supervision to train models. The problems in reinforcement learning are reward-based, where completing tasks or steps yields rewards for the agent, while incorrect outcomes result in penalties.
Reinforcement learning algorithms have extensive applications in the gaming industry for game development. Additionally, they are utilized to train robots to perform human-like tasks.

To learn more about AI, landscape, and trends we recommend to read our previous articles:

Part 1. What is AI? AI & ML Landscape
Part 2. Investments, Fundraising, Insights and trends
Part 3. AI Regulation, privacy and other concerns