5. Introducing Machine Learning.ipynb

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    "### Supervised Machine Learning"
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    "* Supervised learning involves modeling the relationship between measured features of data (size, num_bedroom etc.) and some label(target or outcome) associated with the data (price of house)\n",
    "* Once this model is determined, it can be used to apply labels to new, unknown feature data. \n",
    "* This is further subdivided into classification tasks and regression tasks: in classification, the labels are discrete categories (spam or ham or fixed/predefined number of outcomes), \n",
    "* while in regression, the labels are continuous quantities. "
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    "### Unsupervised Machine Learning\n",
    "* Unsupervised learning involves modeling the features of a dataset without reference to any label, \n",
    "* and is often described as \"letting the dataset speak for itself.\" These models include tasks such as clustering and dimensionality reduction.\n",
    "* Clustering algorithms identify distinct groups of data, \n",
    "* dimensionality reduction algorithms search for more succinct representations of the data."
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    "##### Features \n",
    "Properties of Data\n",
    "\n",
    "##### Target/Label\n",
    "Outcome of data\n",
    "\n",
    "##### Qualitative/Categorical  Data\n",
    "Male - Female, Something which is not number but string. ML understands only  numbers & not strings. A very common operation is to convert string into numbers\n",
    "\n",
    "##### Quantitative/numerical Data\n",
    "Numberical information about the data\n",
    "\n",
    "##### Dimension of Data\n",
    "Number of features representing your data\n",
    "\n",
    "ML uses n-dim matrix to represent dataset\n",
    "\n"
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