AI vs ML vs DL
What is AI?
The word artificial intelligence comprises two words “artificial” and “intelligence”
Artificial refers to something which is non-natural. Intelligence means the ability to
understand and think.
Artificial Intelligence is a technique that enables the machine to mimic human behavior.
Artificial Intelligence is the branch of computer science by which we can create intelligent
machines which can think like human and behave like human.
Machine Learning: Machine Learning is the subset of artificial intelligence. Machine
Learning is one of the approaches of AI that provides the system the ability to automatically
learn from past experience.
Deep Learning: Deep Learning is the subset of machine learning. Deep learning tries to
mimic the human brain to achieve the goals of AI. Deep Learning creates artificial neural
networks similar to brain simulations. The basic building block of artificial neural networks is
the neuron.
Introduction to Data Science
What is Data Science?
Data Science is a multi-disciplinary field that uses scientific methods, processes, algorithms
and systems to extract knowledge and insights from a tremendous amount of data.
Theories and techniques from many fields and disciplines are used to investigate and
analyze a large amount of data to help decision-makers in many industries such as science,
engineering, economics, politics, finance, and education
Data Science Essentials
Data Science Life Cycle
Business Understanding
Every domain and business works with a set of rules and goals. In order to acquire the
correct data, we should be able to understand the business.
Data Collection
The source of data could be logs from web servers, data from online repositories, data from
databases, social media data, and data in excel sheets, so in short data can come from any
source.
Data Preparation
To analyze the data, data needs to be in a certain format. Data might have missing values
which will cause obstruction in analysis and model building.
Data is to be cleaned before processing any further. Thus, this step is also known as Data
Cleaning or Data Wrangling.
Exploratory Data Analysis (EDA) plays an important role at this stage as summarization of
clean data helps in identifying the structure, outliers, anomalies, and patterns in the data.
Data Modelling
Based on the business problem models could be selected. It is essential to identify what is
the task, is it a classification problem, regression or prediction problem, time series
forecasting or a clustering problem. Once the problem type is sorted out model could be
implemented.
After the modeling process, model performance measurement is required. For this
precision, recall, F1 score for classification problems could be used. For regression problem
R2, MAPE (Moving Average Percentage Error) or RMSE (Root Mean Square Error) could be
used. The model should be a robust one and not an overfitted model. If it is overfitted
model then predictions for future data will not come our accurately.
Interpreting Data
This is the last step of any Data Science project and also the most important step. The
execution of this step should be as good as a layman should be able to understand the
outcome of the project. The predictive power of the model lies in its ability to generalize.
Actionable insights from the model show how Data Science has the power of doing
predictive analytics and prescriptive analytics. This gives us the power to learn how to
repeat positive results, or how to prevent the negative result.
Last but not the least, visualization of findings should be done. It should be in line with
business questions.
Data Science Applications
Fraud Detection.
Healthcare.
Internet Search.
Targeted Advertising.
Website Recommendations.
Advanced Image Recognition.
Speech Recognition.
