Data Science: Create Real World Projects | Udemy

Data Science: Create Real World Projects | Udemy
English | Size: 8.26 GB
Genre: eLearning

What you’ll learn
Learn to create real world Data science and Machine learning projects
Learn about different Machine learning models and algorithms
Learn about Data Science life cycle and apply methodologies for creating projects
Learn about different domains of Data Science: Feature engineering, Feature transformation, and model Melection
Learn about Natural Language Processing
Learn about Artificial Intelligence and how to use it to solve the Data Science problems

FAQ about Data Science:

What is Data Science?

Data science encapsulates the interdisciplinary activities required to create data-centric artifacts and applications that address specific scientific, socio-political, business, or other questions.

Let’s look at the constituent parts of this statement:

1. Data: Measurable units of information gathered or captured from activity of people, places and things.

2. Specific Questions: Seeking to understand a phenomenon, natural, social or other, can we formulate specific questions for which an answer posed in terms of patterns observed, tested and or modeled in data is appropriate.

3. Interdisciplinary Activities: Formulating a question, assessing the appropriateness of the data and findings used to find an answer require understanding of the specific subject area. Deciding on the appropriateness of models and inferences made from models based on the data at hand requires understanding of statistical and computational methods

Why Data Science?

The granularity, size and accessibility data, comprising both physical, social, commercial and political spheres has exploded in the last decade or more.

According to Hal Varian, Chief Economist at Google and I quote:

“I keep saying that the sexy job in the next 10 years will be statisticians and Data Scientist”

“The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades, not only at the professional level but even at the educational level for elementary school kids, for high school kids, for college kids.”

************ ************Course Organization **************************

Section 1: Setting up Anaconda and Editor/Libraries

Section 2: Learning about Data Science Lifecycle and Methodologies

Section 3: Learning about Data preprocessing: Cleaning, normalization, transformation of data

Section 4: Some machine learning models: Linear/Logistic Regression

Section 5: Project 1: Hotel Booking Prediction System

Section 6: Project 2: Natural Language Processing

Section 7: Project 3: Artificial Intelligence

Section 8: Farewell

Who this course is for:
This course is dedicated to those people who has some knowledge of programming and wants to learn about how to solve data science and machine learning problems
This course is for them who wants to built career in the field of Data science and Machine Learning
This course is for them who wants to learn data science in perfect way: by learning about feature engineering: data cleaning, transforming and using it to algorithms
This course is for them who wants to learn Machine Learning and Artificial Intelligence by creating fun projects




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