At a very high-level, you can think of AI/ML as prediction. Prediction is the process of filling in missing information. It takes the data and uses it to generate information you don't have. Most ML algorithms are mathematical models that predict outcomes.
Machine learning is part of Artificial Intelligence. AI can be defined as software/computer intelligence which can mimic human intelligence in problem solving and decision making. Machine learning was defined by AI pioneer Arthur Samuel
as “the field of study that gives computers the ability to learn without explicitly being programmed.”?
Machine Learning models are classified into 3 categories. Supervised, Unsupervised and Reinforcement Learning
1.Supervised learning : Supervised machine learning models are trained with labeled data sets, which allows the model to lean and grow more accurate over time. Supervised learning is further classified into classification and regression.
Classification deals with predicting categorical?target variables, which represent discrete classes or labels. For instance, classifying emails as spam or not spam, or predicting whether a patient has a high risk of heart disease.
Here are some classification algorithms:
- Logistic Regression
- Support Vector Machine
- Random Forest
- Decision Tree
- K-Nearest Neighbors (KNN)
- Naive Baye
Regression deals with predicting?continuous?target variables, which represent numerical values. For example, predicting the price of a house based on its size, location, and amenities, or forecasting the sales of a product
Here are some regression algorithms:
- Linear Regression
- Polynomial Regression
- Ridge Regression
- Lasso Regression
- Decision tree
- Random Forest
Supervised learning is used in a wide variety of applications, including:
- Image classification: Identify objects, faces, and other features in images.
- Natural language processing:?Extract information from text, such as sentiment, entities, and relationships.
- Speech recognition: Convert spoken language into text.
- Recommendation systems: Make personalized recommendations to users.
- Predictive analytics: Predict outcomes, such as sales, customer churn, and stock prices.
- Medical diagnosis: Detect diseases and other medical conditions.
- Fraud detection: Identify fraudulent transactions.
- Autonomous vehicles: Recognize and respond to objects in the environment.
- Email
spam detection: Classify emails as spam or not spam.
- Quality control in manufacturing: Inspect products for defects.
- Credit
scoring: Assess the risk of a borrower defaulting on a loan.
- Gaming: Recognize characters, analyze player behavior, and create NPCs.
- Customer support: Automate customer support tasks.
- Weather
forecasting: Make predictions for temperature, precipitation, and other meteorological parameters.
- Sports analytics: Analyze player performance, make game predictions, and optimize strategies.
Unsupervised machine learning used unlabeled data to can find patterns, trends and relationships? in data. Unsupervised learning is further classified into clustering and associations.
Clustering?is the process of grouping data points into clusters based on their similarity. This technique is useful for identifying patterns and relationships in data without the need for labeled examples.
Here are some clustering algorithms:
- K-Means Clustering algorithm
- Mean-shift algorithm
- DBSCAN Algorithm
- Principal Component Analysis
- Independent Component Analysis
Association rule learning is a technique for discovering relationships between items in a dataset. It identifies rules that indicate the presence of one item implies the presence of another item with a specific probability.
Here are some association rule learning algorithms:
- Apriori Algorithm
- Eclat
- FP-growth Algorithm
Here are some common applications of unsupervised learning:
- Clustering: Group similar data points into clusters.
- Anomaly detection: Identify outliers or anomalies in data.
- Dimensionality reduction: Reduce the dimensionality of data while preserving its essential information.
- Recommendation systems: Suggest products, movies, or content to users based on their historical behavior or preferences.
- Topic modeling: Discover latent topics within a collection of documents.
- Density estimation: Estimate the probability density function of data.
- Image and video compression: Reduce the amount of storage required for multimedia content.
- Data
preprocessing: Help with data preprocessing tasks such as data cleaning, imputation of missing values, and data scaling.
- Market
basket analysis: Discover associations between products.
- Genomic data analysis: Identify patterns or group genes with similar expression profiles.
- Image segmentation: Segment images into meaningful regions.
- Community
detection in social networks: Identify communities or groups of individuals with similar interests or connections.
- Customer behavior analysis: Uncover patterns and insights for better marketing and product recommendations.
- Content recommendation: Classify and tag content to make it easier to recommend similar items to users.
- Exploratory data analysis (EDA): Explore data and gain insights before defining specific tasks
3. Reinforcement machine learning trains machines through trial and error to take the best action by establishing a reward system. Reinforcement learning can train models to play games or train autonomous vehicles to drive by telling the machine when it made the right decisions, which helps it learn over time what actions it should take.
Here are some of most common reinforcement learning algorithms:
- Q-learning:?Q-learning is a model-free RL algorithm that learns a Q-function, which maps states to actions. The Q-function estimates the expected reward of taking a particular action in a given state.
- SARSA (State-Action-Reward-State-Action):?SARSA is another model-free RL algorithm that learns a Q-function. However, unlike Q-learning, SARSA updates the Q-function for the action that was actually taken, rather than the optimal action.
- Deep Q-learning:?Deep Q-learning is a combination of Q-learning and deep learning. Deep Q-learning uses a neural network to represent the Q-function, which allows it to learn complex relationships between states and actions.
Here are some applications of reinforcement learning:
- Game Playing: RL can teach agents to play games, even complex ones.
- Robotics: RL can teach robots to perform tasks autonomously.
- Autonomous Vehicles: RL can help self-driving cars navigate and make decisions.
- Recommendation Systems: RL can enhance recommendation algorithms by learning user preferences.
- Healthcare: RL can be used to optimize treatment plans and drug discovery.
- Natural Language Processing (NLP): RL can be used in dialogue systems and chatbots.
- Finance and Trading: RL can be used for algorithmic trading.
- Supply Chain and Inventory Management: RL can be used to optimize supply chain operations.
- Energy Management: RL can be used to optimize energy consumption.
- Game AI: RL can be used to create more intelligent and adaptive NPCs in video games.
- Adaptive Personal Assistants: RL can be used to improve personal assistants.
- Virtual Reality (VR) and Augmented Reality (AR):?RL can be used to create immersive and interactive experiences.
- Industrial Control: RL can be used to optimize industrial processes.
- Education: RL can be used to create adaptive learning systems.
- Agriculture: RL can be used to optimize agricultural operations.
