Machine Learning Tutorial – Excellence Technology

Machine Learning Tutorial

Machine Learning tutorial covers basic and advanced concepts, specially designed to cater to both students and experienced working professionals.

This machine learning tutorial helps you gain a solid introduction to the fundamentals of machine learning and explore a wide range of techniques, including supervised, unsupervised, and reinforcement learning.


Machine learning (ML) is a subdomain of artificial intelligence (AI) that focuses on developing systems that learn—or improve performance—based on the data they ingest. Artificial intelligence is a broad word that refers to systems or machines that resemble human intelligence. Machine learning and AI are frequently discussed together, and the terms are occasionally used interchangeably, although they do not signify the same thing. A crucial distinction is that, while all machine learning is AI, not all AI is machine learning.

What is Machine Learning?

Machine Learning is a branch of artificial intelligence that develops algorithms by learning the hidden patterns of the datasets used it to make predictions on new similar type data, without being explicitly programmed for each task.

Traditional Machine Learning combines data with statistical tools to predict an output that can be used to make actionable insights.

Machine learning is used in many different applications, from image and speech recognition to natural language processing, recommendation systems, fraud detection, portfolio optimization, automated task, and so on. Machine learning models are also used to power autonomous vehicles, drones, and robots, making them more intelligent and adaptable to changing environments.

A typical machine learning tasks are to provide a recommendation. Recommender systems are a common application of machine learning, and they use historical data to provide personalized recommendations to users. In the case of Netflix, the system uses a combination of collaborative filtering and content-based filtering to recommend movies and TV shows to users based on their viewing history, ratings, and other factors such as genre preferences.

Reinforcement learning is another type of machine learning that can be used to improve recommendation-based systems. In reinforcement learning, an agent learns to make decisions based on feedback from its environment, and this feedback can be used to improve the recommendations provided to users. For example, the system could track how often a user watches a recommended movie and use this feedback to adjust the recommendations in the future. 

Personalized recommendations based on machine learning have become increasingly popular in many industries, including e-commerce, social edia, and online advertising, as they can provide a better user experience and increase engagement with the platform or service.

 

The breakthrough comes with the idea that a machine can singularly learn from the data (i.e., an example) to produce accurate results. Machine learning is closely related to data mining and Data Science. The machine receives data as input and uses an algorithm to formulate answers.

How machine learning algorithms work

Machine learning (ML) algorithms are a subset of artificial intelligence that enable systems to learn and improve from experience without being explicitly programmed. These algorithms use statistical techniques to enable computers to identify patterns, make predictions, and make decisions. Here’s a simplified explanation of how machine learning algorithms work:

1. Data Collection:

  • The first step involves collecting and preparing the data relevant to the problem at hand. This data should include features (input variables) and labels (output variables) for training the model.

2. Data Preprocessing:

  • Raw data often needs cleaning and preprocessing. This includes handling missing values, normalizing or scaling data, and converting categorical variables into a format suitable for the algorithm.

3. Training Data and Testing Data:

  • The dataset is typically divided into two sets: training data and testing data. The training data is used to train the model, while the testing data is used to evaluate its performance on new, unseen data.

4. Choosing a Model:

  • Depending on the problem (classification, regression, clustering, etc.), a suitable machine learning model is selected. Common models include linear regression, decision trees, support vector machines, and neural networks.

5. Model Training:

  • The chosen algorithm is trained on the training data. During training, the algorithm adjusts its internal parameters based on the input data and the corresponding known output (labels). The goal is to minimize the difference between the predicted output and the actual output.

6. Evaluation:

  • The model’s performance is assessed using the testing data to ensure that it generalizes well to new, unseen data. Common metrics include accuracy, precision, recall, and F1 score for classification tasks, and mean squared error for regression tasks.

7. Model Adjustment:

  • If the model’s performance is not satisfactory, adjustments are made. This may involve fine-tuning hyperparameters, changing the model architecture, or increasing the amount of training data.

8. Prediction/Inference:

  • Once the model is trained and evaluated, it can be used to make predictions or inferences on new, unseen data. The model applies what it learned during training to make predictions without explicit programming.

9. Iterative Process:

  • Machine learning is often an iterative process. The model is refined based on feedback and new data, continuously improving its performance.

Key Concepts:

  • Features and Labels: Features are input variables, and labels are the output variables. The algorithm learns the mapping between features and labels during training.

  • Loss Function: A measure of the difference between the predicted output and the actual output. The goal is to minimize this loss during training.

  • Gradient Descent: An optimization algorithm used to adjust the model’s parameters to minimize the loss function gradually.

  • Overfitting and Underfitting: Overfitting occurs when a model performs well on the training data but poorly on new data. Underfitting occurs when a model is too simple to capture the underlying patterns in the data.

  • Hyperparameters: Parameters of the machine learning model that are set before training and not adjusted during training. Examples include learning rate, depth of a decision tree, or the number of hidden layers in a neural network.

Understanding the specifics of machine learning algorithms requires a deeper dive into the particular algorithm being used, but this general process applies to various types of machine learning models.

 
 

Machine Learning lifecycle:

The lifecycle of a machine learning project involves a series of steps that include: 

  1. Study the Problems: The first step is to study the problem. This step involves understanding the business problem and defining the objectives of the model. 
  2. Data Collection: When the problem is well-defined, we can collect the relevant data required for the model. The data could come from various sources such as databases, APIs, or web scraping. 
  3. Data Preparation: When our problem-related data is collected. then it is a good idea to check the data properly and make it in the desired format so that it can be used by the model to find the hidden patterns. This can be done in the following steps: 
    • Data cleaning
    • Data Transformation
    • Explanatory Data Analysis and Feature Engineering
    • Split the dataset for training and testing. 
  4. Model Selection: The next step is to select the appropriate machine learning algorithm that is suitable for our problem. This step requires knowledge of the strengths and weaknesses of different algorithms. Sometimes we use multiple models and compare their results and select the best model as per our requirements.
  5. Model building and Training: After selecting the algorithm, we have to build the model. 
    1. In the case of traditional machine learning building mode is easy it is just a few hyperparameter tunings. 
    2. In the case of deep learning, we have to define layer-wise architecture along with input and output size, number of nodes in each layer, loss function, gradient descent optimizer, etc.
    3. After that model is trained using the preprocessed dataset.
  6. Model Evaluation: Once the model is trained, it can be evaluated on the test dataset to determine its accuracy and performance using different techniques like classification report, F1 score, precision, recall, ROC Curve, Mean Square error, absolute error, etc. 
  7. Model Tuning: Based on the evaluation results, the model may need to be tuned or optimized to improve its performance. This involves tweaking the hyperparameters of the model. 
  8. Deployment: Once the model is trained and tuned, it can be deployed in a production environment to make predictions on new data. This step requires integrating the model into an existing software system or creating a new system for the model. 
  9. Monitoring and Maintenance: Finally, it is essential to monitor the model’s performance in the production environment and perform maintenance tasks as required. This involves monitoring for data drift, retraining the model as needed, and updating the model as new data becomes available

Types of Machine Learning

  1. Supervised Machine Learning
  2. Unsupervised Machine Learning
  3. Reinforcement Machine Learning

1. Supervised Machine Learning:

Supervised learning is a type of machine learning in which the algorithm is trained on the labeled dataset. It learns to map input features to targets based on labeled training data. In supervised learning, the algorithm is provided with input features and corresponding output labels, and it learns to generalize from this data to make predictions on new, unseen data.

There are two main types of supervised learning:

  • Regression: Regression is a type of supervised learning where the algorithm learns to predict continuous values based on input features. The output labels in regression are continuous values, such as stock prices, and housing prices. The different regression algorithms in machine learning are: Linear Regression, Polynomial Regression, Ridge Regression, Decision Tree Regression, Random Forest Regression, Support Vector Regression, etc
  • Classification: Classification is a type of supervised learning where the algorithm learns to assign input data to a specific category or class based on input features. The output labels in classification are discrete values. Classification algorithms can be binary, where the output is one of two possible classes, or multiclass, where the output can be one of several classes. The different Classification algorithms in machine learning are: Logistic Regression, Naive Bayes, Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), etc

2. Unsupervised Machine Learning:

Unsupervised learning is a type of machine learning where the algorithm learns to recognize patterns in data without being explicitly trained using labeled examples. The goal of unsupervised learning is to discover the underlying structure or distribution in the data.

There are two main types of unsupervised learning:

  • Clustering: Clustering algorithms group similar data points together based on their characteristics. The goal is to identify groups, or clusters, of data points that are similar to each other, while being distinct from other groups. Some popular clustering algorithms include K-means, Hierarchical clustering, and DBSCAN.
  • Dimensionality reduction: Dimensionality reduction algorithms reduce the number of input variables in a dataset while preserving as much of the original information as possible. This is useful for reducing the complexity of a dataset and making it easier to visualize and analyze. Some popular dimensionality reduction algorithms include Principal Component Analysis (PCA), t-SNE, and Autoencoders.

3. Reinforcement Machine Learning

Reinforcement learning is a type of machine learning where an agent learns to interact with an environment by performing actions and receiving rewards or penalties based on its actions. The goal of reinforcement learning is to learn a policy, which is a mapping from states to actions, that maximizes the expected cumulative reward over time.

There are two main types of reinforcement learning:

  • Model-based reinforcement learning: In model-based reinforcement learning, the agent learns a model of the environment, including the transition probabilities between states and the rewards associated with each state-action pair. The agent then uses this model to plan its actions in order to maximize its expected reward. Some popular model-based reinforcement learning algorithms include Value Iteration and Policy Iteration.
  • Model-free reinforcement learning: In model-free reinforcement learning, the agent learns a policy directly from experience without explicitly building a model of the environment. The agent interacts with the environment and updates its policy based on the rewards it receives. Some popular model-free reinforcement learning algorithms include Q-Learning, SARSA, and Deep Reinforcement Learning.

Need for machine learning:

Machine learning is important because it allows computers to learn from data and improve their performance on specific tasks without being explicitly programmed. This ability to learn from data and adapt to new situations makes machine learning particularly useful for tasks that involve large amounts of data, complex decision-making, and dynamic environments.

Here are some specific areas where machine learning is being used:

  • Predictive modeling: Machine learning can be used to build predictive models that can help businesses make better decisions. For example, machine learning can be used to predict which customers are most likely to buy a particular product, or which patients are most likely to develop a certain disease.
  • Natural language processing: Machine learning is used to build systems that can understand and interpret human language. This is important for applications such as voice recognition, chatbots, and language translation.
  • Computer vision: Machine learning is used to build systems that can recognize and interpret images and videos. This is important for applications such as self-driving cars, surveillance systems, and medical imaging.
  • Fraud detection: Machine learning can be used to detect fraudulent behavior in financial transactions, online advertising, and other areas.
  • Recommendation systems: Machine learning can be used to build recommendation systems that suggest products, services, or content to users based on their past behavior and preferences.

Overall, machine learning has become an essential tool for many businesses and industries, as it enables them to make better use of data, improve their decision-making processes, and deliver more personalized experiences to their customers.

Various Applications of Machine Learning

Now in this Machine learning tutorial, let’s learn the applications of Machine Learning:

  • Automation: Machine learning, which works entirely autonomously in any field without the need for any human intervention. For example, robots perform the essential process steps in manufacturing plants.
  • Finance Industry: Machine learning is growing in popularity in the finance industry. Banks are mainly using ML to find patterns inside the data but also to prevent fraud.
  • Government organization: The government makes use of ML to manage public safety and utilities. Take the example of China with its massive face recognition. The government uses Artificial intelligence to prevent jaywalking.
  • Healthcare industry: Healthcare was one of the first industries to use machine learning with image detection.
  • Marketing: Broad use of AI is done in marketing thanks to abundant access to data. Before the age of mass data, researchers develop advanced mathematical tools like Bayesian analysis to estimate the value of a customer. With the boom of data, the marketing department relies on AI to optimize customer relationships and marketing campaigns.
  • Retail industry: Machine learning is used in the retail industry to analyze customer behavior, predict demand, and manage inventory. It also helps retailers to personalize the shopping experience for each customer by recommending products based on their past purchases and preferences.
  • Transportation: Machine learning is used in the transportation industry to optimize routes, reduce fuel consumption, and improve the overall efficiency of transportation systems. It also plays a role in autonomous vehicles, where ML algorithms are used to make decisions about navigation and safety.

Challenges and Limitations of Machine Learning-

Limitations of Machine Learning:

  1. The primary challenge of machine learning is the lack of data or the diversity in the dataset.
  2.  A machine cannot learn if there is no data available. Besides, a dataset with a lack of diversity gives the machine a hard time. 
  3. A machine needs to have heterogeneity to learn meaningful insight. 
  4. It is rare that an algorithm can extract information when there are no or few variations.
  5.  It is recommended to have at least 20 observations per group to help the machine learn. This constraint leads to poor evaluation and prediction.