Machine learning is the capability of a computer to learn from data without being explicitly programmed. ML helps to automate the process of decision-making. If you want to learn machine learning, I recommend you get the “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” book.
Why Machine Learning?
Everyone in the world is producing some sort of data. Humans cannot be able to look at all the data and extract useful knowledge or hidden patterns from the data. In the world, data is abundant and cheap, but the extracted knowledge is expensive and scarce. Without knowledge extraction, data is useless.
ML makes the data valuable and predicts the future with the help of past or historical data. ML can be used automatically anywhere from worldly tasks to intelligent decisions.
How does Machine Learning work?
Machine learning is a subset of artificial intelligence that focuses on teaching the machine or algorithm to learn from data without being explicitly programmed. In general, the goal of ML is to understand the pattern of data and fit this data into models so that common people can use it.
In simple words, the machine learns from past data and predicts future data.
Examples of Machine Learning
ML is used in numerous contexts. Following are a few instances:
- Weather Forecasting
For example, weather forecasting is the best example of ML. In weather forecasting, we have a lot of past weather data, we want to forecast the weather for upcoming days. ML proved very helpful in these situations.
- House Price Prediction
For Example, you want to purchase a house in a specific town. But you don’t know how much you should pay for the house. For this problem, you should collect some data from different houses in this town. Let’s assume you collected the data of different houses, and you write down the area of the house and the price of the house.
Now you can use ML to estimate the price of a new house in this town. For better results, you must collect other features of houses as well such as the number of rooms, number of floors, etc.
- Google Home (Home Assistant)
- Google Lens
- Google Map
When should we use Machine learning?
We should use ML when we don’t know much about the problem and cannot write code by hand and have a lot of training examples. In simple words, when we can’t find output with the help of a set of rules.
Why Machine Learning is so popular now?
ML is so popular these days because of three reasons.
- Large amounts of data are producing
- Increase in Computational Power (GPUs)
- Algorithms
Applications of Machine Learning
ML has so many uses in modern society. These are some well-known applications of ML.
- Email classify into spam/anti-spam
- Product Recommendations
- Web Search
- Language Translation
- Autonomous car / self-driving car
- Credit card fraud detection
- Handwriting recognition
- Social Media Features
- Photo tagging
- Predict Heart Failure
- Sentiment Analysis
- Image Recognition
- Virtual Personal Assistants
- Traffic Alerts
- Cancer Diagnosis
- Facial Recognition
- Fitness Tracker
- Speech Recognition
Types of Machine Learning
There are four main types of ML.
- Supervised Learning
- Unsupervised Learning
- Semi-Supervised Learning
- Reinforcement Learning
What is Supervised Learning?
In supervised learning, we have labeled data i.e., the dependent variable is labeled. The goal of supervised learning is to generalize from training examples.
Types of Supervised Learning
There are two types of supervised learning.
- Classification
- Regression
What is Unsupervised Learning?
In unsupervised learning, we have unlabeled data. There is no training example in unsupervised learning. The goal of unsupervised learning is to find hidden patterns in unlabeled data.
Types of Unsupervised Learning
There are two types of unsupervised learning.
- Clustering
- Association
What is Semi-Supervised Learning?
Supervised and unsupervised learning methods are combined to create semi-supervised learning. In training, it uses a small amount of manually labeled data with a large amount of unlabeled data.
What is Reinforcement Learning?
How intelligent agents behave in their environment to maximize reward is the subject of reinforcement learning. ML models can be trained to make a series of judgments using reinforcement learning.
Steps of Machine Learning
There are seven steps of ML.
- Data Collection
- Prepare that data
- Choose a Model
- Training
- Evaluation
- Hyperparameter tuning
- Prediction
Top 5 Programming Languages for Machine Learning
- Python
- Java
- R
- C++
- Julia
Python language is widely used for ML. Python is very popular for ML because of its deep learning frameworks TensorFlow, PyTorch, and Keras.
Conclusion
ML is a subset of artificial intelligence that focuses on teaching the machine or algorithm to learn from data without being explicitly programmed. The goal is to understand the pattern of data and fit this data into models so that common people can use it. ML makes the data valuable and predicts the future with the help of past or historical data.
