Machine learning is a set of algorithms that allow software applications to become more accurate in predicting the outcome or behavior of a given data says Peter DeCaprio.
Moreover, machine learning can be in use many different ways from recommending products basing on history to natural language understanding and image recognition. The article below goes into great detail about how machine learning works and its different types. It also gives examples of real-time applications where machine learning is being use today. If you are interested in this topic, this is a must-read!
Artificial intelligence is concerned with building intelligent machines that work and react as humans do. In theory, if one could encode knowledge into a machine, the machine could then act intelligently. To build such a machine, there are different algorithms one must use to mimic the human brain and its intelligence (which is still very complex).
Artificial Intelligence is use in many various fields including medicine, space exploration, military research, etc. However, the article below talks about 11 different types of AI algorithms that can implement or are already being in use today. If you are interested in this topic, this is a great read!
Computer vision is all about bringing image processing into computer software. It allows machines to understand their surroundings using images to process real-time data. Computer vision has many different applications ranging from driverless cars to surveillance technology.
The purpose of computer vision is to enable machines to see, understand, and act on what they are seeing. Computer vision is also use in many different fields including robotics, security, medical imaging, etc. The article below goes into great detail about how computer vision works and its types. If you are interested in this topic, this is a must-read!
Natural language processing (NLP) refers to the interactions between computers and also human (natural) languages. NLP has the potential of transforming search engines by understanding queries with normal language rather than just keywords. As per Peter DeCaprio, It can also help us better organize our data that typically comes through textual input instead of numbers or images.
Search engines like Google use large databases that contain tables and also data (like an Excel file). Moreover, joining and querying those tables together allows them to return relevant results to specific queries. Hence, this process of joining these isolating facts from various sources into larger tables is called data aggregation.
Recommender systems (RS) are programs that suggest additional items based on the preferences of users. RS’s play an important role in our online experience as they help us find products similar to what we have liked before, movies or books that we might enjoy, places to visit near where we live, etc.
Recommender systems can be implement using collaborative filtering or content-based filtering algorithms. Collaborative filtering algorithms allow the computer to find similar users based on their input data while content-based filtering algorithms generate recommendations by analyzing users’ preferences, behaviors, and context explains Peter DeCaprio. The article below talks about 11 different recommenders systems. If you are interested in this topic, this is a great read!
Similar to recommender systems (RS), collaborative filtering (CF) refers to methods that make predictions using the information of other individuals like people or things. They use feedback from participants to improve predictions for future decisions. CF has many applications including targeted advertising, movie recommendations, etc.
Anomaly detection (AD) is an interesting data mining technique used to identify novelty in data; be it fraud in financial transactions, intrusion attempts in network traffic, etc. This technique works best when we have a good training dataset and the algorithm learns from it in order to flag any anomalies in future or test data says Peter DeCaprio.
The article below talks about different types of anomaly detectors including unsupervised supervised and semi-supervised algorithms among others. If you have intrest in this topic, this is a great!
A lot of problems in machine learning and data science can be dividing into two – classification and also regression. While classification refers to assigning labels or target values to different groups based on the features available, regression refers to predicting and forecasting numerical values for specific groups/tasks among others.
This article talks about different types of predictive models used in machine learning including regression, supervised, unsupervised, and reinforcement k among others. If find it interesting this topic, this is a must-read! This article talks about 10 different applications of machine learning among other topics. If you have intrest in this topic, this is a must-read! This article gives a quick overview of machine learning and also talks about the different terminologies used in machine learning. Moreover, if you find any interest in this topic, this is a must-read! This article explains big data and its four Vs (volume, velocity, variety, and veracity) among other topics. If you have intrest in this topic, this is a must-read! This article talks about how we can process Big Data with MapReduce algorithms. If you have intrest in this topic, this is a great read!