Machine Learning (ml)

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Machine learning is studying the computer algorithms which can improve through experimentation and using information. This has been used by artificial intelligence in many ways and can be considered an essential component. Machine learning is a tool for analyzing samples of data to predict or determine a decision without explicit programming of the algorithm. Machine learning has been applied to several different applications, ranging from the medical field to the veterinary profession in which conventional algorithms are not feasible.

Machine learning explained by another definition

Machine learning is the science of getting computers to act without being explicitly programmed. In machine learning, programming is replaced by teaching, in which a computer "studies" example data and uses the result to predict future data. Machine learning algorithms are used in many different fields, including search engines (to adjust rankings), spam filtering (to detect unwanted email), credit card fraud detection (to detect unauthorized charges), and even self-driving cars (which determine what actions to take based on observations). The quantity of training data available often plays a large role in determining how well an algorithm performs. As such, it is important to carefully select the input variables when using machine-learning algorithms for classification tasks.

Machine learning is a tool for analyzing samples of data to predict or determine a decision without explicit programming of the algorithm.

Machine learning has been applied to several different applications, ranging from the medical field to the veterinary profession in which conventional algorithms are not feasible. We’re here to help you get started with machine learning and artificial intelligence so that you can make informed decisions about your business and how it operates. Contact us today!

Machine learning explained by examples

The first step in the machine learning process is Data Acquisition, where large quantities of data are made available. The next step is Data Cleaning, which eliminates any noisy or invalid input data. In order to produce a quality model that balances complexity and accuracy, training datasets must be sufficiently large and diverse.

Data Preprocessing: Data preprocessing consists of three main activities: normalization, missing value imputation, and feature engineering

Normalization transforms the input vector into a new vector determined by an L2 norm (i.e., Euclidean distance) computed from the original vectors. This transformation helps features with extremely large values to have less influence on the optimization process during training. It also reduces computational cost because computing the L2 norm requires only simple arithmetic and takes less time than computing the dot product of two vectors.

Missing value imputation: This process replaces missing values in the training data with some other numerical value (e.g., mean or median) that might be more reasonable or expected based on what is known about each attribute's domain. For example, when there are too many missing values in a text field, machine learning algorithms treat these as unknowns and tend to avoid them because they impose noise on the training dataset. Missing value imputation helps balance the loss function and produces better prediction in machine learning models.

Feature engineering: Feature engineering allows you to expand upon simple summaries of data and implement new variables that might help predict an outcome more effectively. Before we try to predict something using machine learning, we must first build a dataset. To build a dataset, we must first decide what kind of data we have and how much of it we have. For example, if you wanted to predict the weather using machine learning, your data could be broken down into various attributes that might affect the future forecast. This includes location (i.e., latitude/longitude), time of year, temperature, and humidity just to name a few.

Machine learning algorithms can be categorized as being supervised or unsupervised learning algorithms. Supervised machine-learning algorithms require training datasets containing input vectors paired with an output or class vector so that they can adjust their weights accordingly during training. In contrast, unsupervised machine-learning algorithms require only input vectors to adjust their weights.

Classification: Classification is a supervised machine-learning task that assigns labels to input data based on which class it most closely resembles. The training dataset consists of pairs of examples in the form <input, output>, where each example contains an input vector and a corresponding output value. For example, for a spam filter application, we might have a labeled training set consisting of thousands or millions of email messages along with a label specifying whether or not each message is spam.

Regression: Regression is another supervised machine learning algorithm used when predicting continuous variables such as time series forecasting (e.g., stock prices over time), forecasting demand for products, and modeling real-valued functions such as LTV (long term value). The training dataset is composed of input vectors paired with corresponding output vectors, where each input vector contains an input attribute and the corresponding output vector contains the corresponding desired value.

Clustering: Clustering algorithms are unsupervised machine-learning tasks that sort objects into groups or clusters based on their characteristics. Unlike classification algorithms, clustering does not require known labels for data points to be assigned during training. For example, in a retail store scenario, you might want to group customers by similar shopping behaviors without requiring demographic information about them beforehand. Decision trees: Classification and regression trees (CART) are supervised learning models used for both classification and regression problems; however, they are especially useful when the outcome variable is categorical (i.e., the dependent variable has more than two possible outcomes).

Feature selection: Feature selection algorithms can be considered data mining algorithms because they are primarily used to discover previously unknown patterns in data (i.e., features) that can be useful for predictive or classification tasks (e.g., determining credit risk, fraud detection, etc.). There are several different feature selection techniques, but among them all there is a common goal of choosing the predictor variables that result in optimal performance when trained on random samples from the training dataset. Another way of thinking about it is that these techniques prune away attributes that do not produce an optimal reduction in predictive error when extracted from the training set. The most popular class of feature selection algorithms are filter methods. For example, the sequential minimal optimization (SMO) algorithm is a filter method that calculates the predictive error for each predictor variable based on a training dataset and then chooses those predictor variables that result in the lowest error.

Feature extraction: Feature extraction algorithms extract previously unknown patterns from data using a combination of techniques such as clustering or decision trees to first discover these patterns before using various feature selection algorithms to extract them from the data set. For example, if you wanted to explore customer shopping habits at a retail store, you might first want to use clustering to group customers according to their behavior and then use decision trees/rules to determine whether certain features such as "average dollar amount" can be used as another predictor for future purchasing.

Resampling: Resampling is a statistical technique used to estimate the performance of machine learning algorithms when applied to a particular dataset. Using resampling techniques, we can compute out-of-sample errors for various training/tuning parameters to optimize our models and select the best machine learning model development from a set of many potential models that could be used for an application. This is especially important in cases where there are too many algorithm options or limited data available when creating predictive models. The most popular methods for this task include:

Bootstrapping: Bootstrapping is another resampling method used primarily for hypothesis testing with statistical significance assessments. Each sample is randomly selected either with or without replacement (see below), thus providing multiple samples from the same original data set. The resulting bootstrap samples can be used to estimate the variance of a statistic (e.g., mean, regression coefficient) and make inferences about the accuracy of that statistic's population value (e.g., compare coefficients across different groups).

What is machine learning?

Machine learning is a method of data analysis that automates analytical model building. In other words, it creates models without requiring explicit instructions on how to do so. Instead of relying on step-by-step instructions to accomplish a task as a human would, machine learning algorithms use empirical data, such as observations or examples, to automatically discover patterns in data and then apply those patterns to find similar cases in new data. In general, there are three primary tasks for supervised machine learning problems:

Prediction: Based on the information available from historical cases predict whether an upcoming case will belong to one category or another (i.e., dependent variable). For example, based on past cases of cancer patients that provided their age at diagnosis, gender, race, and occupation, predict whether a new cancer patient had bladder cancer or not.

Classification: Based on the information available from historical cases classify an upcoming case into one of several categories (i.e., dependent variable). For example, based on past cases of cancer patients that have provided their age at diagnosis, gender, race, and occupation determine what other cancers each patient had been diagnosed with to create a more comprehensive picture of how different types of cancers are related.

Regression: Like classification problems above, regression problems involve modeling the relationship between variables so future values can be predicted based on changes in other variables. Unlike classification problems where the dependent variable can only assume categorical values that are mutually exclusive and exhaustive (i.e., classifying patients into either "bladder cancer" or "not bladder cancer"), regression problems refer to continuous dependent variables that can assume any value on a predefined interval. For example, if we are trying to model monthly sales of widgets at a particular store, the independent variable (predictor) could be hours worked by new hires and the number of customers who visited during each hour of business.

Feature selection: Feature selection algorithms automatically determine which subset of features users should use based on the analysis of the performance of several different models with different subsets of features. This is an important step because many machine learning tools require you to select your input variables before training; however, selecting irrelevant variables for inclusion in the model will decrease its accuracy. Additionally, overfitting training data by using too many irrelevant variables can lead to poor accuracy when used to make predictions about future cases.

What are the different types of machine learning?

Supervised learning: Supervised learning requires labeled input examples where each example has a specific correct answer to be predicted. The most common types of supervised learning include classification, regression, and binary logistic regression (classification for categorical dependent variables). Unsupervised learning: Unsupervised learning does not require labeled input examples because it is primarily concerned with discovering patterns in data. The most commonly used unsupervised algorithms are clustering algorithms which identify segments or "clusters" of similar values within the dataset. Reinforcement learning: Reinforcement learning is different from both supervised and unsupervised learning because it allows a model to learn by interacting with its environment. The goal is to maximize a real-valued reward function where each action changes the state of the environment and may lead to different rewards. Deep learning: Deep learning algorithms are supervised machine learning methods that use artificial neural networks or deep belief networks inspired by biological structures in our brains. Artificial neural networks consist of interconnected simple "neurons" that take input from other neurons, perform calculations on those inputs, and then generate an output based on these calculations.

How does a machine learning algorithm work?

Machine Learning Algorithms work very differently from human intelligence. We make decisions based on experience, but a computer makes models for specific purposes under supervision while trying many possible answers. A machine learning algorithm starts by trying to construct a model of how it is supposed to behave. Then, when given an example it uses the examples and tries many possible answers (models) until it finds the most likely answer; this is considered training.

Machine learning algorithms can be divided into three broad categories: Supervised Learning, Unsupervised Learning, and Reinforcement Learning.

Supervised Learning algorithms find patterns within data by comparing a labeled intermediate representation of input examples with the desired output label -- common supervised learning algorithms include Naïve Bayes Classification, K-Nearest Neighbor Classification, Linear Regression, Decision Tree Classifiers, Support Vector Machines, Artificial Neural Networks, Neonatal Network Classifiers, Gradient Boosting, and Ensembles.

Unsupervised Learning algorithms find structure within data without the use of labeled input examples -- common unsupervised learning algorithms include K-Means Clustering, Gaussian Mixture Models, Principal Component Analysis (PCA), Latent Dirichlet Allocation (LDA), Autoencoders, Non-Negative Matrix Factorization (NMF), Independent Component Analysis (ICA) and Hierarchical Clustering.

Reinforcement Learning algorithms learn from a series of actions with corresponding immediate rewards or punishments to maximize an expected cumulative reward -- reinforcement learning algorithms include Linear Regression with Extended Kalman Filter , Dynamic Programming Algorithm for Policy Evaluation , Q-Learning, Deep Q-Network, Generalized Policy Iteration, DQN, Actor-Critic RSG.

Machine learning algorithms are very complex and can be difficult to understand. There are many different types of machine learning algorithms including supervised learning, unsupervised learning, reinforced learning, semi-supervised learning, active learning, decision tree learners, etc...

Supervised Machine Learning is training a model with labeled input examples to create predictions about future cases. It requires labeled input examples where each example has a specific correct answer to be predicted such as labeling images on an image recognition site like Imagenet or labeling documents for sentiment analysis (positive or negative) Unsupervised Machine Learning tries to discover patterns within data without labeled input examples. The goal is to try and reduce the amount of data by finding patterns within the data (clusters). An example would be clustering customers with similar purchasing habits.

Reinforcement Machine Learning allows a model to learn how good action was given what has happened in the past or even predict future rewards based on present actions. A common reinforcement learning task is controlling an agent in a game like Pacman where the algorithm learns over time which moves lead to bigger scores.

There are many different types of machine learning algorithms including supervised learning, unsupervised learning, reinforcement learning, semi-supervised learning, active learning, decision tree learners, etc...

Tell me the most popular machine learning method?

Machine learning is a method of achieving artificial intelligence through the study and principles of pattern recognition and computational learning theory. It is used in computer science for creating statistical models from example data that are then used to perform predictive analytics such as spam filtering, facial recognition systems, language translation, etc... Supervised Learning is training a model with labeled input examples to create predictions about future cases. Unsupervised Learning tries to discover patterns within data without labeled input examples. Reinforcement Learning allows a model to learn how good action was given what has happened in the past or even predict future rewards based on present actions.

What types of machine learning algorithms are there?

(Multiple Choice)

There are three broad categories of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Supervised Machine Learning is training a model with labeled input examples to create predictions about future cases. It requires labeled input examples where each example has a specific correct answer to be predicted such as labeling images on an image recognition site like Imagenet or labeling documents for sentiment analysis (positive or negative). Unsupervised Machine Learning tries to discover patterns within data without labeled input examples. The goal is to try and reduce the amount of data by finding patterns within the data (clusters). An example would be clustering customers with similar purchasing habits. Reinforcement Machine Learning allows a model to learn how good action was given what has happened in the past or even predict future rewards based on present actions. A common reinforcement learning task is controlling an agent in a game like Pacman where the algorithm learns over time which moves lead to bigger scores.

How businesses are using machine learning?

Businesses are using machine learning to find insights within large datasets. Some examples include image recognition, language translation, spam filtering, sentiment analysis of customer text data or product reviews, etc... Businesses used to hire data scientists with PhDs in computer science and statistics but now they require a different profile that has business sense and the ability to speak the language of their specific business. They also want someone that can create quick prototypes and then hand them off to an engineering team.

What is supervised machine learning?

Supervised Machine Learning is training a model with labeled input examples to create predictions about future cases. It requires labeled input examples where each example has a specific correct answer to be predicted such as labeling images on an image recognition site like Imagenet or labeling documents for sentiment analysis (positive or negative).

What is unsupervised machine learning?

Unsupervised Machine Learning tries to discover patterns within data without labeled input examples. The goal is to try and reduce the amount of data by finding patterns within the data (clusters). An example would be clustering customers with similar purchasing habits.

What is reinforcement machine learning?

Reinforcement Machine Learning allows a model to learn how good action was given what has happened in the past or even predict future rewards based on present actions. A common reinforcement learning task is controlling an agent in a game like Pacman where the algorithm learns over time which moves lead to bigger scores.

What is supervised machine learning?

Supervised Machine Learning is training a model with labeled input examples to create predictions about future cases. It requires labeled input examples where each example has a specific correct answer to be predicted such as labeling images on an image recognition site like Imagenet or labeling documents for sentiment analysis (positive or negative).

What are machine learning algorithms?

There are three broad categories of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Supervised Machine Learning is training a model with labeled input examples to create predictions about future cases. It requires labeled input examples where each example has a specific correct answer to be predicted such as labeling images on an image recognition site like Imagenet or labeling documents for sentiment analysis (positive or negative). Unsupervised Machine Learning tries to discover patterns within data without labeled input examples. The goal is to try and reduce the amount of data by finding patterns within the data (clusters). An example would be clustering customers with similar purchasing habits. Reinforcement Machine Learning allows a model to learn how good action was given what has happened in the past or even predict future rewards based on present actions. A common reinforcement learning task is controlling an agent in a game like Pacman where the algorithm learns over time which moves lead to bigger scores.

What makes deep learning effective?

Deep learning has been very effective because it can learn from lots of data and even recognize patterns that humans might miss. Two main types of machine learning use deep learning, supervised, and reinforcement.

How businesses are using machine learning?

Businesses used to hire data scientists with PhDs in computer science and statistics but now they require a different profile that has business sense and the ability to speak the language of their specific business. They also want someone that can create quick prototypes and then hand them off to an engineering team.

How will machine learning evolve?

As more data is collected it will be easier for machines to learn from simple patterns within data especially thanks to unsupervised learning algorithms which discover these complex patterns. As more industries become digitalized, there will be larger opportunities for machine learning algorithms (i.e., driverless cars). There are still many things that humans can do better than machines like common sense or creative, but as computers become faster and more advanced it becomes harder for us to differentiate.

Why does it matter?

Machine learning can automate decisions and take humans out of the equation for better decision-making. However, there is a lot of hype surrounding machine learning which makes it easy to be misinformed and get the wrong idea about what it does. This means that machine learning needs to be communicated clearly so businesses understand how it works and its potential benefits.

I originally published this article on my blog. Please feel free to check out my entire blog where I often write about programming, startups, VC funding, entrepreneurship & more!

Evolution of machine learning

Machine learning has come a long way since it was first introduced back in the 1960s when there were only a few algorithms to choose from. Over time, these algorithms evolved into different sub-groups based on how they learn and their application areas.

Why does it matter?

Machine learning can automate decisions and take humans out of the equation for better decision-making. However, there is a lot of hype surrounding machine learning which makes it easy to be misinformed and get the wrong idea about what it does. This means that machine learning needs to be communicated clearly so businesses understand how it works and its potential benefits.

I originally published this article on my blog. Please feel free to check out my entire blog where I often write about programming, startups, VC funding, entrepreneurship & more!

Tell me the importance of machine learning?

Machine learning can automate decisions and take humans out of the equation for better decision-making. However, there is a lot of hype surrounding machine learning which makes it easy to be misinformed and get the wrong idea about what it does. This means that machine learning needs to be communicated clearly so businesses understand how it works and its potential benefits.

I originally published this article on my blog. Please feel free to check out my entire blog where I often write about programming, startups, VC funding, entrepreneurship & more!

How is machine learning used by organizations like Amazon?

The most common use of Machine Learning today is in web search engines like Google or Bing thanks to their large data sets containing thousands if not millions of pages. For example, as you type a sequence of words into Google, it tries to predict what you are searching for based on previous searches and the most common search results.

Machine learning is used regularly by these web search engines but also recently by organizations like Amazon use machine learning to recommend products that they believe will interest you based on your purchase history which can be very useful for online retailers. These types of algorithms can help with everything from selecting the right ad to appearing at the top of a page (i.e., Google Adwords) or recommending products (i.e., Amazon).

I originally published this article on my blog. Please feel free to check out my entire blog where I often write about programming, startups, VC funding, entrepreneurship & more!

Does it have real-world applications?

Machine learning is being used by companies the size of Amazon because it helps them to connect with their potential customers better and drive sales. For example, machine learning can be used in predictive analytics where you use data from previous customer transactions to predict what they might want to buy next which means that your website provides exactly what they are looking for when they are looking for it. This increases conversions as people are not presented with products or offers that they haven't expressed an interest in thus increasing ROI on marketing efforts.

Machine learning has come a long way since it was first introduced back in the 1960s when there were only a few algorithms to choose from. Over time, these algorithms have evolved and expanded into many different types such as supervised, unsupervised, and reinforcement learning.

I originally published this article on my blog. Please feel free to check out my entire blog where I often write about programming, startups, VC funding, entrepreneurship & more!

Today there are hundreds of algorithms that fall under the umbrella of machine learning and these can be applied to a large number of data sets: customer transactions, social media feeds (e.g., Twitter), financial market movement predictions, medical research studies, etc. The list is growing every day as machine learning is now being used in everything from self-driving cars to stock trading programs that operate without human input after they have been taught how to make decisions based on past data patterns.

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