Introduction to machine learning
0 likes•190 views
The document provides a comprehensive introduction to machine learning, outlining key concepts such as data analytics, types of data, and the differentiation between supervised and unsupervised learning. It also discusses challenges like overfitting and underfitting, and highlights the use of Scikit-learn for implementing various machine learning algorithms like support vector machines, naive bayes, and decision trees. Overall, it advocates the importance of data analysis in a data-driven world and demonstrates practical applications within machine learning frameworks.
Introduction to machine learning
- 1. Lesson One Introduction to Machine Learning - High Level Overview By: Oluwasgun Matthew & Abdulrazzaq Olajide Summary 1. Introduction to Concept of Data Analytics and Machine Learning a. Data Mining and Statistical Pattern Recognition b. Supervised and Unsupervised Classification/Learning 2. Types of Data - Continuous and Discrete Data 3. Insight on Data Overfitting and Underfitting a. Introducing Outliers 4. Scikit Learn usage in ML a. Support Vector Machine b. Gaussian Naive Bayes c. Decision Trees Let’s Dive In.. 1
- 2. Introduction - Concept of Data Analytics and Machine Learning In a world of data explosion, rate of data generation and consumption is on the increasing side, there comes the buzzword - Big Data. Big Data is the concept of fast moving, large volume data in varying dimensions (sources) and highly unpredicted sources. The 4Vs of Big Data ● Volume - Scale of Data ● Velocity - Analysis of Streaming Data ● Variety - Different forms of Data ● Veracity - Uncertainty of Data With increasing data availability, the new trend in the industry demands not just data collection, but making ample sense of acquired data - thereby, the concept of Data Analytics. Taking it a step further to further make futuristic prediction and realistic inferences - the concept of Machine Learning. A blend of both gives a robust analysis of data for the past, now and the future. There is a thin line between data analytics and Machine learning which becomes very obvious when you dig deep. Data Mining Data collection can be achieved either from static offline data generated from existing platforms or real-life data source in from of a stream. Pattern recognition in data is key to machine learning, finding relationship between features, labels and/or attributes of data set. For example, classification of animals into mammals and reptiles is solely dependent on physical attributes of animal set in consideration. Supervised and Unsupervised Learning Supervised learning is concerned with model or function generation from labeled data set. Making future inference based on existing predefined information about data attributes. 2
- 3. It’s a learning model where you have input variables (X) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output. The goal is to approximate the mapping function so well that when you have new input data (X) that you can predict the output variables (Y) for that data. Y = f(X) It’s is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process. We know the correct answers, the algorithm iteratively makes predictions on the training data and is corrected by the teacher. The Learning stops when the algorithm achieves an acceptable level of performance. A lot of machine learning project is centered around this as it’s easier than unsupervised, In this regard, there exist solutions like: ● Recommender Systems ● Prediction Engines ● Image Recognition from Tagged Attributes ● Time series prediction Supervised learning problems can be further grouped into regression and classification problems ● Classification: a classification problem is when the output variable is a category, such as “red” and “blue” or “disease” and “no disease” or “purchase” and “no purchase” ● Regression: a regression problem is when the output variable is real value, such as “weight”, “spend power”, “time of best billing” Some popular examples of supervised machine learning algorithms are: ● Linear regression for regression problems ● Random forest for classification and regression problems ● Support vector machines for classification problems Unsupervised learning tries to deduce inference from unlabeled data, i.e. no prior knowledge of attributes definition/classification. Unsupervised learning is where you only have input data (X) and no corresponding output variables. The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data. These are called unsupervised learning because unlike supervised learning above there is no correct answers and there is no teacher. Algorithms are left to their own devices to discover and present the interesting structure in the data. 3
- 4. The following solutions are classified under this category: ● Fraud Detection from weird transaction ● Clustering students into types based on learning styles Unsupervised learning problems can be further grouped into clustering and association problems. ● Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior ● Association: An association run learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y. Some popular examples of unsupervised learning algorithms are: ● K-means for clustering problems ● Apriori algorithm for association rule learning problems. Quiz Classify the following as either supervised or unsupervised learning: ● Spam detection in emails ● Fraud detection in transactions ● Customer segmentation ● Speech recognition ● Weather forecast ● House price prediction ● Astronomy prediction Types of Data - Continuous and Discrete Data There exist a wide range of data format that will be encountered during data collection, and sanitization from numerical, categorical, time series and text base data. Quiz What type of data type is: ● CPE508 Result ● List of courses offered in 500Level - Computer Science and Engineering ● Gender ● Frequency of Strike actions in O.A.U ● Lectures time table 4
- 5. Data Overfitting and Underfitting In machine learning we describe the learning of the target function from training data as inductive learning. Induction refers to learning general concepts from specific examples which is exactly the problem that supervised machine learning problems aim to solve. This is different from deduction that is the other way around and seeks to learn specific concepts from general rules. In statistics, a fit refers to how well you approximate a target function. This is good terminology to use in machine learning, because supervised machine learning algorithms seek to approximate the unknown underlying mapping function for the output variables given the input variables. Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance on the model on new data. This means that the noise or random fluctuations in the training data is picked up and learned as concepts by the model. Underfitting refers to a model that can neither model the training data not generalize to new data. An underfit machine learning model is not suitable model and will be obvious as it will have poor performance on the training data. Underfitting is often not discussed as it is easy to detect given a good performance metric. The remedy is to move on and try alternative machine learning algorithms. Nevertheless, it does provide good contrast to the problem of overfitting. Outlier is an observation that lies in an abnormal distance from other values in a random sample from a population. 5
- 6. NB: Clustering analysis is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters) Quiz Identify the outlier in the visualized data below; 1, 2 or 3: Enough of theoretical exposition, Let’s go practical… 6
- 7. Scikit Learn Usage in ML Scikit Learn (otherwise known as Sk-Learn) is an open source machine learning library for python developer. It encapsulate various classification, regression and clustering algorithms including support vector machines, random forest, gradient boosting, k-means and DBSCAN. It’s enhanced with data visualization tool which can be used with other separate python module like pandas. The focus of this section is to understand how the library works for classification problems with the following algorithms in mind: ● Support Vector Machines (for classification problems) - LinearSVC ● Gaussian Naive Bayes ● Decision Trees Support Vector Machines (SVM) SVMs contain a set of supervised learning methods used for classification, regression and outliers detection. The focus here is to use it strictly on classification problems. Advantages of SVMs are: - very effective in high dimensional spaced data set - uses a subset of training points in the decision function, so it’s memory efficient 7
- 8. Example of Linear SVC implementation: Learn more here: http://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC Gaussian Naive Bayes Naive Bayes methods basically applies Baye’s theorems with the “naive” assumption of independence between every pair of features. Advantages of Naive Bayes algorithm are: - worked well in real-world situations like spam filtering - requires a small amount of training data to estimate the necessary parameters Example of Gaussian Naive Bayes implementation: 8
- 9. Learn more here: http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bay es.GaussianNB Decision Trees Decision Trees (DTs) are a non-parametric supervised learning methods which creates a model that predicts the values of a target variable by learning simple decision rules inferred from the data features. Advantages of Decision Trees algorithm are: - simple to understand and interpret - Requires little data preparation Example of Decision Tree Classifier implementation: Learn more here: http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.Deci sionTreeClassifier 9
- 10. Next Plan Kindly create an account on Microsoft Azure ML Platform: https://studio.azureml.net/ 10