Machine Learning Lec 2 | Waheed Anwer

Download PDf directly

Or Read Directly

Computer Science Machine Learning Dr. Waheed Anwar Computer Science Machine learning Life cycle • Machine learning life cycle is a cyclic process to build an efficient machine learning project. The main purpose of the life cycle is to find a solution to the problem or project. • Machine learning life cycle involves seven major steps, which are given below: 1. Gathering Data 2. Data preparation 3. Data Wrangling 4. Analyse Data 5. Train the model 6. Test the model 7. Deployment Computer Science • The most important thing in the complete process is to understand the problem and to know the purpose of the problem. Therefore, before starting the life cycle, we need to understand the problem because the good result depends on the better understanding of the problem Machine learning Life cycle Computer Science • 1. Data Gathering • Data Gathering is the first step of the machine learning life cycle. The goal of this step is to identify and obtain all data-related problems. • data can be collected from various sources such as files, database, internet, or mobile devices. ➢Identify various data sources ➢Collect data ➢Integrate the data obtained from different sources • By performing the above task, we get a coherent set of data, also called as a dataset. It will be used in further steps. • The quantity and quality of the collected data will determine the efficiency of the output. • The more will be the data, the more accurate will be the prediction. Machine learning Life cycle Computer Science 2. Data preparation • Data preparation is a step where we put our data into a suitable place and prepare it to use in our machine learning training. • In this step, first, we put all data together, and then randomize the ordering of data. • This step can be further divided into two processes: • Data exploration: It is used to understand the nature of data that we have to work with. We need to understand the characteristics, format, and quality of data. A better understanding of data leads to an effective outcome. In this, we find Correlations, general trends, and outliers. • Data pre-processing: Now the next step is preprocessing of data for its analysis. Machine learning Life cycle Computer Science • 3. Data Wrangling • Data wrangling is the process of cleaning and converting raw data into a useable format. It is the process of cleaning the data, selecting the variable to use, and transforming the data in a proper format to make it more suitable for analysis in the next step. It is one of the most important steps of the complete process. Cleaning of data is required to address the quality issues. • It is not necessary that data we have collected is always of our use as some of the data may not be useful. In real-world applications, collected data may have various issues, including: • Missing Values • Duplicate data • Invalid data • Noise • So, we use various filtering techniques to clean the data. • It is mandatory to detect and remove the above issues because it can negatively affect the quality of the outcome. Machine learning Life cycle Computer Science • 4. Data Analysis • Now the cleaned and prepared data is passed on to the analysis step. This step involves: 1. Selection of analytical techniques 2. Building models 3. Review the result • The aim of this step is to build a machine learning model to analyze the data using various analytical techniques and review the outcome. It starts with the determination of the type of the problems, where we select the machine learning techniques such as Classification, Regression, Cluster analysis, Association, etc. then build the model using prepared data, and evaluate the model. • Hence, in this step, we take the data and use machine learning algorithms to build the model. Machine learning Life cycle Computer Science • 5. Train Model • in this step we train our model to improve its performance for better outcome of the problem. • We use datasets to train the model using various machine learning algorithms. Training a model is required so that it can understand the various patterns, rules, and, features. Machine learning Life cycle Computer Science • 6. Test Model • In this step, we check for the accuracy of our model by providing a test dataset to it. • Testing the model determines the percentage accuracy of the model as per the requirement of project or problem. Machine learning Life cycle Computer Science • 7. Deployment • The last step of machine learning life cycle is deployment, where we deploy the model in the real-world system. • If the above-prepared model is producing an accurate result as per our requirement with acceptable speed, then we deploy the model in the real system. But before deploying the project, we will check whether it is improving its performance using available data or not. The deployment phase is similar to making the final report for a project. Machine learning Life cycle Computer Science • A dataset is a collection of data in which data is arranged in some order. A dataset can contain any data from a series of an array to a database table. • Types of data in datasets • Numerical data: Such as house price, temperature, etc. • Categorical data: Such as Yes/No, True/False, Blue/green, etc. • Ordinal data: These data are similar to categorical data but can be measured on the basis of comparison. • Note: A real-world dataset is of huge size, which is difficult to manage and process at the initial level. Therefore, to practice machine learning algorithms, we can use any dummy dataset. Dataset for Machine Learning Computer Science • The key to success in the field of machine learning or to become a great data scientist is to practice with different types of datasets. But discovering a suitable dataset for each kind of machine learning project is a difficult task. So, in this class, I shall provide the detail of the sources from where you can easily get the dataset according to your project. Datasets for Machine Learning Computer Science • During the development of the ML project, the developers completely rely on the datasets. In building ML applications, datasets are divided into two parts: • Training dataset: • Test Dataset Datasets for Machine Learning Computer Science Popular sources for Machine Learning datasets 1. UCI Machine Learning Repository UCI Machine learning repository is one of the great sources of machine learning datasets. This repository contains databases, domain theories, and data generators that are widely used by the machine learning community for the analysis of ML algorithms. The link for the UCI machine learning repository is https://archive.ics.uci.edu/ml/index.php Computer Science Popular sources for Machine Learning datasets 2. Google's Dataset Search Engine Google dataset search engine is a search engine launched by Google on September 5, 2018. This source helps researchers to get online datasets that are freely available for use. The link for the Google dataset search engine is https://toolbox.google.com/datasetsearch Computer Science Popular sources for Machine Learning datasets 3. Microsoft Datasets The Microsoft has launched the "Microsoft Research Open data" repository with the collection of free datasets in various areas such as natural language processing, computer vision, and domain-specific sciences. The link to download or use the dataset from this resource is https://msropendata.com/. Computer Science Popular sources for Machine Learning datasets 4. Scikit-learn dataset Scikit-learn is a great source for machine learning enthusiasts. This source provides both toy and real-world datasets. These datasets can be obtained from sklearn.datasets package and using general dataset API. The link to download datasets from this source is https://scikit-learn.org/stable/datasets/index.html. Computer Science Popular sources for Machine Learning datasets 5. Kaggle Datasets Kaggle provides a high-quality dataset in different formats that we can easily find and download. The link for the Kaggle dataset is https://www.kaggle.com/datasets. Computer Science Popular sources for Machine Learning datasets 6. Computer Vision Datasets Visual data provides multiple numbers of the great dataset that are specific to computer visions such as Image Classification, Video classification, Image Segmentation, etc. The link for downloading the dataset from this source is https://www.visualdata.io/. Computer Science Popular sources for Machine Learning datasets 7. Awesome Public Dataset Collection The link to download the dataset from Awesome public dataset collection is https://github.com/awesomedata/awesome-public-datasets.