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What Machine Learning Is
Machine learning is basically any system that you can train with known data where you already know the answers of what you’re trying to predict, and it builds up this model internally over time, that can then take new data and make predictions based on that model.
And that would include things like neural networks or deep learning networks or other techniques as well they’re all basically under the umbrella of machine learning algorithms, so generally this term covers the entire field of deep learning in Artificial Intelligence. One of the hardest part in a data scientist (or analyst) job, is preparing the actual training data, and detect the so called “outliers” down the read.
But it’s not limited to those either. Very generally speaking it’s any system where you can train it by feeding what we call feature data with known labels. In layman’s terms, is a set of algorithms that can learn from observational data, and make predictions based on that data.
Machine Learning Predictive Models
Predictive modeling is a technique using statistics for making future predictions. It’s a process of data mining and probability, to predict future outcomes. Models are using one of more than one classifiers, in an attempt to calculate the probability of a data set which is part of an other. For instance a typical model used, is to define if an email is spam or not.
Below are the types of model types predictive analysis:
- Linear Regression.
- Logistic Regression.
- Polynomial Regression.
- Multiple Regression.
Data Analysis Techniques
Lets break this down
- Supervised & Unsupervised Learning.
- Bayesian Method.
- K-Means Clustering.
- Decision Trees.
- Ensemble Learning.
- Support Vector Machines.
- K-Nearest-Neighbors.
- Dimensionality Reduction.
- Principal Component Analysis.
- Reinforcement Learning.
- Sparse dictionary learning.
- Anomaly detection.
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