Today I’ll explain the basics of machine learning. We Will talk on all the basic step of Machine Learning that you need to know as a starter.
What is Machine Learning (ML)?
Machine Learning (ML) is a subset of AI.
Machine Learning essentially presents machines with the ability to learn on their own.
This learning is based on observations, experiences and analyzing patterns with a specified data set without the need for explicit programming.
So, you don’t need to write the code, you simply feed data to the generic algorithm.
The algorithm or machine then builds the logic based on the data you feed.
I’ll proceed to some elementary aspects of Machine Learning now and Five Steps Used to Execute a Machine Learning Task. Let´s Start.
1- Data Collection
Regardless of the source of raw data whether it’s from text files, excel, access etc this step collecting past data is the starting point of future learning.
The learning likelihood of the machine improves when the density variety and volume of relevant data is better.
2- Data Preparation
The success rate of any analytical process depends on the quality of the data you use.
Hence you need to spend time deciding the quality of data.
Only then can you take the steps required to fix issues such as missing data and handling outliers.
Exploratory analysis is the best method to study the variations of the data in great detail thereby improving the quality of the data.
3- Model Training
In this step you choose the most appropriate algorithm as well as the representation of the data in the form of the model.
You then split the clean data into two parts:
- Training Data
- Test Data
The proportion of the training data and test data depends on the prerequisites.
You use the first part (training data) for developing the model and you use the second part (test data) for reference.
4- Model Evaluation
Use the second part of the data test data and holdout data to test the accuracy of your model.
Evaluating the model is helpful in determining the precision in algorithm selection based on the outcome.
A more reliable test to check the accuracy of the model is to check its performance on data that was not used at all as the model was being built.
5- Performance Improvement
This final step might involve the selection of a different model altogether or else you might introduce more variables to improve efficiency.
That’s the reason you need to spend a lot of time during the data collection and data preparation steps.
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Regardless of any model you choose you can use these five steps to structure the technique.
When we cover algorithms, you will find the way these five steps appear in every model.
Main Types of Machine Learning Algorithms
Supervised learning (predictive models)
Supervised learning is essentially a learning in which a machine is trained using well-labeled data.
This means part of the data is already tagged with the right answer.
The machine is then presented with a new set of examples or data.
The supervised learning algorithm then analyzes the training data or the set of training examples and generates a correct outcome from the labeled data.
Similarly, a predictive model is used to predict future outcomes based on historical data.
Generally, a predictive model is given clear instructions right at the beginning.
The model knows what needs to be learned as well as how it needs to be learned.
This class of learning algorithms is termed supervised learning.
A marketing company uses supervised learning in trying to determine which customers are most likely to churn.
Supervised learning can also be used to predict the chances of the occurrence of natural disasters such as earthquakes, cyclones, tornadoes, volcanic, eruptions and much more for determining the total insurance value.
Some common supervised learning algorithms used are:
- Decision Trees
- Nearest Neighbor
- Naive Bayes
Unsupervised Learning (Descriptive Models)
Unsupervised learning is used to train a descriptive model in which no fixed target is set and no specific feature is more important than other features.
In this Machine Learning technique, you don’t need to supervise the model.
The model works on its own and discovers information and patterns that were previously undetected.
Unsupervised learning deals mainly with unlabeled data.
A typical use case scenario of unsupervised learning is where a retailer desires to find out the combination of products that online shoppers tend to buy more frequently.
In the pharmaceutical industry this learning technique is used to predict the diseases that are most likely to occur along with diabetes.
Examples of algorithms used in unsupervised learning are:
- Association Rules
- K-Means Clustering
- Anomaly detection
- A Priori Algorithm
- Neural Networks
Reinforced learning (RL)
In this Machine Learning technique you train the machine to make specific decisions.
The purpose of these decisions is based on the needs of the business with maximizing efficiency or performance being the sole motto.
That is finding a suitable action model that maximizes the total cumulative reward is the goal of this learning technique.
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In reinforcement learning (RL) the software agent or machine trains itself continuously based on the environment to which it is exposed.
The machine then applies this enriched knowledge to resolve business problems.
Since the learning process is continuous, the involvement of human expertise is minimal and this in turn saves a lot of time.
Examples of algorithms used in reinforcement learning are:
- Deep Q Network (DQN)
- State Action Reward
- State Action or SARSA
- Markov Decision Process
Business Applications of Machine Learning
There are numerous business applications of Machine Learning.
We will look at just a few of them.
1- Business Operations Automation
A host of business operations can be automated using Machine Learning.
Some of these business operations include:
- Document Processing
- Employee Analytics
- Database Analysis
- Spam Detection
- Systems Management
- Chat Bots
AI and Machine Learning solutions can now replace or at least support a lot of time consuming manual processes.
Some companies have unique business needs as they look to create or maintain a competitive edge and retain control of their intellectual property.
End-to-End service providers assist such companies in planning, developing and implementing customized solutions to fill their business needs.
2- Fraud Detection
The enormous amount of data on the internet today makes for an excellent case of data studies and analysis but this increases the likelihood of fraudulent activities as well.
Machine Learning is fast emerging as a potent technology to safeguard our cyberspace.
Machine Learning models both supervised and unsupervised are being used in fraud detection.
Fraud analysis across industries are dependent on Machine Learning tools for verifying the identity of users, screening online scams, investigating claims, filtering fake news and more.
3- Recommending the Right Product in E-Commerce
Product recommendation is an essential aspect of the sales and marketing strategy of an e-commerce store.
Upselling and cross-selling also fall under this category.
A Machine Learning model typically analyzes the purchase history of an online shopper and based on this history the model identifies the products in your product inventory that might interest the shopper.
The algorithm identifies hidden patterns among the items then similar products are grouped into clusters.
Such a Machine Learning model enables e-commerce stores to make better product recommendations to shoppers thereby motivating product purchase.
In this way a superior recommendation system is created.
4- Virtual Personal Assistant
This has emerged as a significant find of the 21st century.
In the fields of speech recognition text-to-speech as well as speech-to-text conversion and natural language processing ML algorithms have done phenomenal work.
As soon as you ask them a question they quickly scan through the internet and find the most relevant answers.
They also track your preferences, schedule and goals to recommend relevant information.
A virtual personal assistant feeds on all your inputs and queries.
Asking for directions or asking about the weather to constantly self learn and improve.
5- Market Research and Customer Segmentation
Machine Learning applications not only help businesses in setting prices but also help them in delivering the right products to the right places at the right time via predictive inventory planning and customer segmentation.
For instance, retailers use Machine Learning for determining the inventory that will sell best in specific stores based on seasonal factors that impact a particular store as well as the demographics of that region.
What Exactly Does a Machine Learning Expert Do?
The duties and responsibilities of a Machine Learning expert include designing and developing Machine Learning algorithms.
- Discovering, designing and developing analytical methods to assist innovative strategies of data and information processing.
- Designing and developing innovative algorithms
- Executing Explanatory Data Analysis Designing and Testing Working Hypotheses
- Putting Together and analyzing historical data and looking to identify patterns.
- Providing Technical Assistance for business development and program management activities including customer development and proposal writing.
I hope all this basic information about Machine Learning will be super helpful for you. If you are any query in mind leave a comment below.