Data Science- Create Real World Projects

Data Science: Create Real World Projects, available at $59.99, has an average rating of 4.8, with 93 lectures, based on 13 reviews, and has 175 subscribers.

You will learn about 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 This course is ideal for individuals who are 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 or This course is for them who wants to built career in the field of Data science and Machine Learning or 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 or This course is for them who wants to learn Machine Learning and Artificial Intelligence by creating fun projects It is particularly useful 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 or This course is for them who wants to built career in the field of Data science and Machine Learning or 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 or This course is for them who wants to learn Machine Learning and Artificial Intelligence by creating fun projects.

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Summary

Title: Data Science: Create Real World Projects

Price: $59.99

Average Rating: 4.8

Number of Lectures: 93

Number of Published Lectures: 93

Number of Curriculum Items: 93

Number of Published Curriculum Objects: 93

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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

Course Curriculum

Chapter 1: Welcome to the Course: Start with Introduction

Lecture 1: Introduction

Chapter 2: Data Science Environment Setup

Lecture 1: Install anaconda on your machine

Lecture 2: Set up environment and Download Machine Learning Libraries

Lecture 3: Introduction to Jupyter Notebook

Chapter 3: Data Science Lifecycle/Methodology

Lecture 1: Data Science Methodologies

Lecture 2: CRISP-DM model

Lecture 3: Phases of CRISP-DM

Lecture 4: Phases of CRISP-DM part 2

Lecture 5: Phases of CRISP-DM part 3

Chapter 4: Introduction to Data Cleanup/Munging

Lecture 1: Why to clean the data?

Lecture 2: Data Quality

Lecture 3: Check if data is valid or not?

Lecture 4: Check if data is accurate or not?

Lecture 5: Completeness of the data

Lecture 6: Consistency of the data

Lecture 7: Uniformity of the data

Lecture 8: How to ensure data quality

Lecture 9: Inspect the data

Lecture 10: Cleaning the data

Lecture 11: Goal of data munging

Lecture 12: Understand your data

Lecture 13: Introduction to Outliers

Lecture 14: Finalize Data Munging

Chapter 5: Cleaning data (Coding session) : Feature Engineering

Lecture 1: Handle data type mismatch

Lecture 2: Remove Duplicate data

Lecture 3: Handling missing data

Lecture 4: Feature Importance

Lecture 5: Plot feature importance plot

Chapter 6: Introduction to Feature Transformation

Lecture 1: Introduction to Feature Importance

Lecture 2: Data Normalization

Lecture 3: Data Standardization

Lecture 4: Normalization in practice

Lecture 5: Standardization in practice

Lecture 6: Introduction to One Hot Encoding

Lecture 7: One Hot Encoding in practice

Chapter 7: Introduction to Machine Learning

Lecture 1: Types of data in Machine Learning

Lecture 2: Structured format for datasets

Lecture 3: Introduction to pandas library

Lecture 4: Train Test split Concept

Chapter 8: Introduction to Decision Tree

Lecture 1: Decision Tree part 1

Lecture 2: Decision Tree part 2

Lecture 3: Code: Decision Tree classifier

Lecture 4: Decision Tree: GINI index

Chapter 9: Introduction to Linear Regression

Lecture 1: Introduction to Linear Regression

Lecture 2: Learn about OLS [Ordinary Least Squares] algorithm

Lecture 3: Introduction to working of Linear Regression

Lecture 4: Lecture: Introduction to MSE, MAE, RMSE

Lecture 5: Introduction to R squared

Lecture 6: Implement Simple Linear Regression

Chapter 10: Introduction to Logistic Regression

Lecture 1: Learn about Logistic Regression

Lecture 2: Learn about Gradient Descent

Lecture 3: Implement Logistic Regression part 1

Lecture 4: Implement Logistic Regression part 2

Chapter 11: Project 1: Hotel Booking Prediction System (Learn Classification problem)

Lecture 1: Introduction to data and data dictionary

Lecture 2: Setup project and import libraries

Lecture 3: Import data to the project

Lecture 4: Clean NA values

Lecture 5: Clean your data

Lecture 6: Analysis 1: Where do the guest come from?

Lecture 7: Analysis 2: How much do guests pay for room per night?

Lecture 8: Analysis 3: How does the price vary?

Lecture 9: Sorting

Lecture 10: Analysis 4: Which months are busy months?

Lecture 11: Analysis 5: How long do people stay at the hotels?

Lecture 12: Feature selection using coorelation

Lecture 13: Refine Numerical attributes

Lecture 14: Refine Categorical attributes

Lecture 15: Augment the data

Lecture 16: Mean Encoding for Categorical attributes

Lecture 17: Preparing our data

Lecture 18: Feature Importance

Lecture 19: Splitting data and Building models

Chapter 12: Project 2: Natural Language Processing

Lecture 1: Loading the data to the project

Lecture 2: Introduction to Corpus and Term Document Matrix

Lecture 3: Storing data into the data frame

Lecture 4: Cleaning the data

Lecture 5: Creating Document Term Matrix

Lecture 6: Analyzing most commonly spoken words

Lecture 7: Creating wordcloud

Lecture 8: Profanity

Lecture 9: Sentimental Analysis

Lecture 10: Sentiment Label

Lecture 11: Plotting Polarity and Subjectivity

Lecture 12: Topic Modeling

Lecture 13: Topic Modeling: Part Of Speech Tagging

Lecture 14: Text Generation

Lecture 15: Text Generation Part 2

Chapter 13: Project 3: Artificial Intelligence: Neural Network

Instructors

Sachin Kafle
Founder of CSAMIN & Bit4Stack Tech Inc. [[Author, Teacher]]

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