Building Machine Learning applications on Linux on Power

What is Machine Learning

 - It is a programming technology which enables building systems that can automatically learn and improve with experience
 - Learning means understanding the input data and making wise decisions based on the input data
 - Possible intelligent outcomes could be forecasting stock market trends , data mining , language processing

What are common Machine Learning

 **Supervised Learning** : 
       a) This includes such learning algorithms which analyze the training data and produces inferred functions . Some common examples : 
          -> e-mail classification as spam , 
          -> voice recognition , 
          -> naming webpages based on contents .
          -> Credit Card fraud detection
       b) Some common examples of supervised learning algorithms are : 
                    -> neural network 
                    -> support vector machine 
                    -> Naive Bayers Classifier
       c) "Naive Bayes classifiers" is most popular supervised learning algorithm
       d) It produces a decision model that marks unseen transactions as normal or suspicious

 **Unsupervised Learning** :
       a) This includes such learning algorithms which analyzes unlabeled data without having any predefined data for training the algorithm 
       b) Some common algorithms that come as part of this algorithm are : k-means , hierarchical clustering
       c) Example : Recommendation Engine 

 **Recommendations** :
       a) provides close recommendations based on user information such as previous purchases, clicks, and ratings

 **Classification** :
       a) uses known data to determine how the new data should be classified into a set of existing categories. Classification is a form of supervised learning

 **Clustering** :
       a) Clustering is used to form groups or clusters of similar data based on common characteristics. Clustering is a form of unsupervised learning    

What are steps involved in developing Machine Learning

      1) Data and problem definition 
      2) Data Collection
      3) Data Preprocessing
      4) Data analysis and modelling with ( Supervised or Unsupervised Machine Learning algorithms )
      5) Evaluation