Machine learning is a branch of AI that helps computers learn and derive meanings from complex and unstructured data. It makes use of various tools and techniques including machine learning libraries and programming languages to reach the correct output which helps in making predictions. A data scientist with a deep understanding of machine learning skills can manage time and work efficiently. 

Definition and Basic Concepts

Machine learning is completely different from traditional programming. Traditional programming requires codes that instruct the computer how to solve a problem manually. This takes a lot of time and energy and sometimes it can be very exhausting for the programmer. 

Machine learning uses algorithms that help the computer learn from the given data, generate structures and make predictions. Algorithms are the structures that are used to analyze data to create models from it. These models are used to make predictions in the future.

Types of Machine Learning

There are different types of machine learning. 

1. Supervised machine learning
2. Unsupervised machine learning
3. Semi-supervised machine learning
4. Reinforcement machine learning

Supervised Machine Learning:

In supervised machine learning, the computer is trained to find the map between the data input and the data output. This way, the computer can easily identify the output when new and unused data is presented. 

Supervised learning uses the methods of classification and regression to predict the values. It is an effective way of reaching the outputs, but it cannot be used on complex data sets.

Unsupervised Learning:

In unsupervised machine learning, the computer will not be trained to reach the output. This type of machine learning is used to find hidden connections and clusters from the given data, disregarding its complexity.

By applying methods of clustering, density estimation, visualization, and projection to identify the hidden patterns in the given data.

Semi-Supervised Machine Learning:

This type of machine learning uses a limited amount of labeled data to reinforce the learning process of the given unlabeled data and find patterns and meanings in it. This method is used when the given data is too complex, takes longer periods of time and requires expert knowledge.

Reinforcement Machine Learning:

Reinforcement machine learning does not train the computer to learn, instead, it uses the trial-and-error method. When the computer makes the right decision and reaches the tight output, a reward is given in the form of feedback. Through this method, the developer can find possible solutions to a given problem.

The Importance of Machine Learning in Data Science

Machine learning is an integral part of data science. Data scientists depend on the machine learning algorithms to extract data patterns from the complex data sets. It is also used in making predictions for the future. So when you study for a diploma in data science, you must give importance to learning machine learning as well.

Core Competency for Data Scientists

Machine learning is a vital skill for data scientists. Because machine learning and data science complement each other.

• Helps in working on unstructured and complex data
• Making accurate future predictions
• Make effective decisions based on the models and predictions.
• Automate the work using machine learning tools and techniques
• Test hypotheses
• Produce real-time insights

Industry Applications of Machine Learning

Machine learning helps in improving the work life of a data scientist. Being skilled in machine learning helps in making accurate decisions effectively.

Machine learning is a skill that is in high demand because of its applicability in various domains, including healthcare, finance, retail, media, and entertainment.

Healthcare: 

• Faster diagnosis
• Customized treatment plan
• Identifying risk factors
• Developing new drugs for diseases

Finance:

• Detects financial frauds
• Financial forecasting
• Predict future market movement
• Optimize investment portfolios

Retail:

• Price optimisation
• Fraud detection
• Inventory management
• Personalized marketing

Media and Entertainment:

• Helps in creating data driven content
• Personalized marketing
• Increase audience engagement
• Personalized content creation