Data Science: Create Real World Projects

  • Category Other
  • Type Tutorials
  • Language English
  • Total size 8.3 GB
  • Uploaded By tutsnode
  • Downloads 225
  • Last checked 4 days ago
  • Date uploaded 3 years ago
  • Seeders 13
  • Leechers 15

Infohash : 681150D62E719B00923739295D69256CF29F0B15




Description

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

Requirements

Basic knowledge of Python programming is essential
You should know topics of programming like functions, data structures and object oriented programming

Last Updated 4/2022

Files:

Data Science Create Real World Projects [TutsNode.com] - Data Science Create Real World Projects 9 - Introduction to Linear Regression
  • 45 - Learn about OLS [Ordinary Least Squares] algorithm.mp4 (246.8 MB)
  • 49 - linear-regression-guide.zip (86.7 KB)
  • 46 - Introduction to working of Linear Regression English.vtt (40.6 KB)
  • 45 - Learn about OLS [Ordinary Least Squares] algorithm English.vtt (34.1 KB)
  • 49 - Implement Simple Linear Regression English.vtt (28.7 KB)
  • 44 - Introduction to Linear Regression English.vtt (18.5 KB)
  • 47 - Lecture Introduction to MSE, MAE, RMSE English.vtt (14.8 KB)
  • 48 - Introduction to R squared English.vtt (12.8 KB)
  • 46 - Introduction to working of Linear Regression.mp4 (194.6 MB)
  • 49 - Implement Simple Linear Regression.mp4 (168.9 MB)
  • 44 - Introduction to Linear Regression.mp4 (156.4 MB)
  • 47 - Lecture Introduction to MSE, MAE, RMSE.mp4 (57.0 MB)
  • 48 - Introduction to R squared.mp4 (49.4 MB)
10 - Introduction to Logistic Regression
  • 52 - logistic.zip (143.3 KB)
  • 53 - logistic.zip (143.3 KB)
  • 53 - Implement Logistic Regression part 2 English.vtt (38.8 KB)
  • 52 - Implement Logistic Regression part 1 English.vtt (25.3 KB)
  • 51 - Learn about Gradient Descent English.vtt (7.5 KB)
  • 50 - Learn about Logistic Regression English.vtt (7.3 KB)
  • 53 - Implement Logistic Regression part 2.mp4 (200.7 MB)
  • 52 - Implement Logistic Regression part 1.mp4 (145.3 MB)
  • 51 - Learn about Gradient Descent.mp4 (45.3 MB)
  • 50 - Learn about Logistic Regression.mp4 (38.3 MB)
6 - Introduction to Feature Transformation
  • 30 - min-max-scaler.ipynb (48.5 KB)
  • 32 - min-max-scaler.ipynb (48.5 KB)
  • 33 - standard-scaler.ipynb (38.1 KB)
  • 33 - Standardization in practice English.vtt (16.3 KB)
  • 34 - one-hot-encoding.ipynb (16.2 KB)
  • 35 - one-hot-encoding.ipynb (16.2 KB)
  • 35 - One Hot Encoding in practice English.vtt (15.2 KB)
  • 34 - Introduction to One Hot Encoding English.vtt (12.5 KB)
  • 32 - Normalization in practice English.vtt (12.1 KB)
  • 31 - Data Standardization English.vtt (10.8 KB)
  • 29 - Introduction to Feature Importance English.vtt (10.7 KB)
  • 30 - Data Normalization English.vtt (4.2 KB)
  • 33 - Standardization in practice.mp4 (138.1 MB)
  • 32 - Normalization in practice.mp4 (125.2 MB)
  • 35 - One Hot Encoding in practice.mp4 (116.9 MB)
  • 34 - Introduction to One Hot Encoding.mp4 (55.8 MB)
  • 29 - Introduction to Feature Importance.mp4 (55.7 MB)
  • 31 - Data Standardization.mp4 (48.3 MB)
  • 30 - Data Normalization.mp4 (23.0 MB)
3 - Data Science Lifecycle Methodology
  • 7 - Phases of CRISP English.vtt (4.4 KB)
  • 5 - Data Science Methodologies English.vtt (9.3 KB)
  • 9 - Phases of CRISP English.vtt (7.6 KB)
  • 6 - CRISP English.vtt (6.9 KB)
  • 8 - Phases of CRISP English.vtt (3.3 KB)
  • 5 - Data Science Methodologies.mp4 (51.2 MB)
  • 6 - CRISP-DM model.mp4 (42.9 MB)
  • 9 - Phases of CRISP-DM part 3.mp4 (39.7 MB)
  • 7 - Phases of CRISP-DM.mp4 (26.6 MB)
  • 8 - Phases of CRISP-DM part 2.mp4 (17.4 MB)
12 - Project 2 Natural Language Processing
  • 76 - Cleaning the data.mp4 (245.4 MB)
  • 79 - Creating wordcloud English.vtt (23.0 KB)
  • 79 - Creating wordcloud.mp4 (207.3 MB)
  • 78 - Analyzing most commonly spoken words English.vtt (15.1 KB)
  • 84 - Topic Modeling English.vtt (14.5 KB)
  • 73 - Loading the data to the project English.vtt (12.8 KB)
  • 80 - Profanity English.vtt (12.8 KB)
  • 85 - Topic Modeling Part Of Speech Tagging English.vtt (12.7 KB)
  • 77 - Creating Document Term Matrix English.vtt (8.9 KB)
  • 75 - Storing data into the data frame English.vtt (8.5 KB)
  • 83 - Plotting Polarity and Subjectivity English.vtt (8.3 KB)
  • 82 - Sentiment Label English.vtt (8.1 KB)
  • 74 - Introduction to Corpus and Term Document Matrix English.vtt (7.7 KB)
  • 86 - Text Generation English.vtt (6.0 KB)
  • 81 - Sentimental Analysis English.vtt (6.0 KB)
  • 73 - Project-2.zip (853.8 KB)
  • 74 - Project-2.zip (853.8 KB)
  • 78 - Analyzing most commonly spoken words.mp4 (141.9 MB)
  • 84 - Topic Modeling.mp4 (128.1 MB)
  • 73 - Loading the data to the project.mp4 (117.0 MB)
  • 85 - Topic Modeling Part Of Speech Tagging.mp4 (104.4 MB)
  • 87 - Text Generation Part 2.mp4 (86.1 MB)
  • 80 - Profanity.mp4 (81.5 MB)
  • 75 - Storing data into the data frame.mp4 (78.2 MB)
  • 82 - Sentiment Label.mp4 (69.3 MB)
  • 83 - Plotting Polarity and Subjectivity.mp4 (53.5 MB)
  • 74 - Introduction to Corpus and Term Document Matrix.mp4 (44.3 MB)
  • 81 - Sentimental Analysis.mp4 (37.8 MB)
  • 86 - Text Generation.mp4 (36.1 MB)
5 - Cleaning data (Coding session) Feature Engineering
  • 27 - Sklearn-Feature-Importance.ipynb (113.2 KB)
  • 28 - Sklearn-Feature-Importance.ipynb (113.2 KB)
  • 24 - pandas-data-type-mismatch.ipynb (44.8 KB)
  • 24 - Handle data type mismatch.mp4 (242.8 MB)
  • 26 - pandas-missing-data.ipynb (22.4 KB)
  • 24 - Handle data type mismatch English.vtt (27.3 KB)
  • 25 - pandas-duplicate-data.ipynb (22.2 KB)
  • 27 - Feature Importance English.vtt (15.5 KB)
  • 25 - Remove Duplicate data English.vtt (12.3 KB)
  • 26 - Handling missing data English.vtt (11.9 KB)
  • 28 - Plot feature importance plot English.vtt (6.4 KB)
  • 27 - Feature Importance.mp4 (146.3 MB)
  • 26 - Handling missing data.mp4 (95.5 MB)
  • 25 - Remove Duplicate data.mp4 (89.9 MB)
  • 28 - Plot feature importance plot.mp4 (60.6 MB)
8 - Introduction to Decision Tree
  • 42 - Code Decision Tree classifier English.vtt (34.7 KB)
  • 40 - Decision Tree part 1 English.vtt (37.2 KB)
  • 42 - Code Decision Tree classifier.mp4 (235.5 MB)
  • 41 - Decision Tree part 2 English.vtt (30.6 KB)
  • 42 - Decision-Tree-Classifier.ipynb (28.6 KB)
  • 43 - Decision-Tree-Classifier.ipynb (28.6 KB)
  • 43 - Decision Tree GINI index English.vtt (12.8 KB)
  • 40 - Decision Tree part 1.mp4 (140.1 MB)
  • 41 - Decision Tree part 2.mp4 (116.8 MB)
  • 43 - Decision Tree GINI index.mp4 (95.2 MB)
7 - Introduction to Machine Learning
  • 38 - Introduction to pandas library English.vtt (32.6 KB)
  • 39 - Train Test split Concept English.vtt (18.9 KB)
  • 36 - Types of data in Machine Learning English.vtt (10.7 KB)
  • 37 - Structured format for datasets English.vtt (10.0 KB)
  • 38 - Introduction to pandas library.mp4 (164.1 MB)
  • 39 - Train Test split Concept.mp4 (89.5 MB)
  • 37 - Structured format for datasets.mp4 (69.6 MB)
  • 36 - Types of data in Machine Learning.mp4 (31.6 MB)
13 - Project 3 Artificial Intelligence Neural Network
  • 89 - Neuron English.vtt (29.4 KB)
  • 91 - Multi English.vtt (24.4 KB)
  • 90 - Learn to create MLP model English.vtt (20.1 KB)
  • 93 - Multi English.vtt (13.3 KB)
  • 92 - Multi English.vtt (8.8 KB)
  • 88 - Introduction to Neural Networks English.vtt (3.2 KB)
  • 91 - Multi-Layer Perception Algorithm part 2.mp4 (166.1 MB)
  • 90 - Learn to create MLP model.mp4 (156.5 MB)
  • 89 - Neuron.mp4 (116.4 MB)
  • 93 - Multi-Layer Perception Algorithm part 4.mp4 (86.0 MB)
  • 92 - Multi-Layer Perception Algorithm part 3.mp4 (61.4 MB)
  • 88 - Introduction to Neural Networks.mp4 (24.2 MB)
2 - Data Science Environment Setup
  • 4 - Introduction to Jupyter Notebook English.vtt (27.0 KB)
  • 3 - Set up environment and Download Machine Learning Libraries English.vtt (16.7 KB)
  • 2 - Install anaconda on your machine English.vtt (12.3 KB)
  • 4 - Introduction to Jupyter Notebook.mp4 (111.4 MB)
  • 3 - Set up environment and Download Machine Learning Libraries.mp4 (80.5 MB)
  • 2 - Install anaconda on your machine.mp4 (69.1 MB)
11 - Project 1 Hotel Booking Prediction System (Learn Classification problem)
  • 72 - Splitting data and Building models English.vtt (27.0 KB)
  • 69 - Mean Encoding for Categorical attributes English.vtt (26.4 KB)
  • 64 - Analysis 5 How long do people stay at the hotels English.vtt (19.4 KB)
  • 61 - Analysis 3 How does the price vary English.vtt (19.0 KB)
  • 71 - Feature Importance English.vtt (16.4 KB)
  • 70 - Preparing our data English.vtt (16.1 KB)
  • 65 - Feature selection using coorelation English.vtt (16.0 KB)
  • 63 - Analysis 4 Which months are busy months English.vtt (15.9 KB)
  • 62 - Sorting English.vtt (14.9 KB)
  • 60 - Analysis 2 How much do guests pay for room per night English.vtt (14.8 KB)
  • 58 - Clean your data English.vtt (14.5 KB)
  • 59 - Analysis 1 Where do the guest come from English.vtt (14.1 KB)
  • 67 - Refine Categorical attributes English.vtt (10.8 KB)
  • 57 - Clean NA values English.vtt (10.6 KB)
  • 68 - Augment the data English.vtt (8.9 KB)
  • 66 - Refine Numerical attributes English.vtt (8.7 KB)
  • 55 - Setup project and import libraries English.vtt (3.2 KB)
  • 54 - Introduction to data and data dictionary English.vtt (2.8 KB)
  • 56 - Import data to the project English.vtt (2.5 KB)
  • 69 - Mean Encoding for Categorical attributes.mp4 (206.8 MB)
  • 72 - Splitting data and Building models.mp4 (187.6 MB)
  • 71 - Feature Importance.mp4 (137.3 MB)
  • 61 - Analysis 3 How does the price vary.mp4 (134.3 MB)
  • 64 - Analysis 5 How long do people stay at the hotels.mp4 (132.2 MB)
  • 65 - Feature selection using coorelation.mp4 (127.2 MB)
  • 58 - Clean your data.mp4 (116.3 MB)
  • 60 - Analysis 2 How much do guests pay for room per night.mp4 (112.1 MB)
  • 63 - Analysis 4 Which months are busy months.mp4 (108.1 MB)
  • 70 - Preparing our data.mp4 (102.7 MB)
  • 67 - Refine Categorical attributes.mp4 (99.0 MB)
  • 57 - Clean NA values.mp4 (98.6 MB)
  • 62 - Sorting.mp4 (97.9 MB)
  • 59 - Analysis 1 Where do the guest come from.mp4 (95.2 MB)
  • 66 - Refine Numerical attributes.mp4 (68.3 MB)
  • 68 - Augment the data.mp4 (63.9 MB)
  • 54 - Introduction to data and data dictionary.mp4 (31.2 MB)
  • 56 - Import data to the project.mp4 (17.6 MB)
  • 55 - Setup project and import libraries.mp4 (14.5 MB)
  • 54 - Project-1.zip (2.3 MB)
  • 55 - Project-1.zip (2.3 MB)
4 - Introduction to Data Cleanup Munging
  • 18 - Inspect the data English.vtt (2.4 KB)
  • 19 - Cleaning the data English.vtt (14.3 KB)
  • 12 - Check if data is valid or not English.vtt (13.5 KB)
  • 23 - Finalize Data Munging English.vtt (13.0 KB)
  • 11 - Data Quality English.vtt (9.2 KB)
  • 22 - Introduction to Outliers English.vtt (8.6 KB)
  • 21 - Understand your data English.vtt (8.5 KB)
  • 20 - Goal of data munging English.vtt (7.9 KB)
  • 16 - Uniformity of the data English.vtt (6.7 KB)
  • 17 - How to ensure data quality English.vtt (6.0 KB)
  • 15 - Consistency of the data English.vtt (4.1 KB)
  • 10 - Why to clean the data English.vtt (3.5 KB)
  • 13 - Check if data is accurate or not English.vtt (3.3 KB)
  • 14 - Completeness of the data English.vtt (3.1 KB)
  • 23 - Finalize Data Munging.mp4 (88.5 MB)
  • 20 - Goal of data munging.mp4 (77.5 MB)
  • 19 - Cleaning the data.mp4 (67.5 MB)
  • 21 - Understand your data.mp4 (60.9 MB)
  • 12 - Check if data is valid or not.mp4 (51.9 MB)
  • 11 - Data Quality.mp4 (51.4 MB)
  • 22 - Introduction to Outliers.mp4 (45.1 MB)
  • 17 - How to ensure data quality.mp4 (26.6 MB)
  • 16 - Uniformity of the data.mp4 (25.2 MB)
  • 10 - Why to clean the data.mp4 (20.0 MB)
  • 15 - Consistency of the data.mp4 (13.5 MB)
  • 14 - Completeness of the data.mp4 (11.2 MB)
  • 18 - Inspect the data.mp4 (11.2 MB)
  • 13 - Check if data is accurate or not.mp4 (10.8 MB)
1 - Welcome to the Course Start with Introduction
  • 1 - Introduction.mp4 (18.7 MB)
  • TutsNode.com.txt (0.1 KB)
  • [TGx]Downloaded from torrentgalaxy.to .txt (0.6 KB)
    • .pad
    • 0 (0.1 KB)
    • 1 (1.7 KB)
    • 2 (0.2 KB)
    • 3 (0.1 KB)
    • 4 (68.1 KB)
    • 5 (227.1 KB)
    • 6 (313.6 KB)
    • 7 (447.3 KB)
    • 8 (391.6 KB)
    • 9 (75.6 KB)
    • 10 (105.6 KB)
    • 11 (55.3 KB)
    • 12 (484.9 KB)
    • 13 (646.4 KB)
    • 14 (731.0 KB)
    • 15 (734.8 KB)
    • 16 (54.5 KB)
    • 17 (901.8 KB)
    • 18 (925.9 KB)
    • 19 (745.4 KB)
    • 20 (672.3 KB)
    • 21 (826.7 KB)
    • 22 (891.0 KB)
    • 23 (774.6 KB)
    • 24 (853.8 KB)
    • 25 (1,007.8 KB)
    • 26 (101.0 KB)
    • 27 (200.5 KB)
    • 28 (655.9 KB)
    • 29 (696.5 KB)
    • 30 (897.9 KB)
    • 31 (652.2 KB)
    • 32 (888.1 KB)
    • 33 (617.5 KB)
    • 34 (272.5 KB)
    • 35 (990.5 KB)
    • 36 (458.3 KB)
    • 37 (120.5 KB)
    • 38 (500.0 KB)
    • 39 (798.2 KB)
    • 40 (817.5 KB)
    • 41 (63.6 KB)
    • 42 (549.3 KB)
    • 43 (550.4 KB)
    • 44 (890.0 KB)
    • 45 (32.4 KB)
    • 46 (509.3 KB)
    • 47 (501.5 KB)
    • 48 (831.4 KB)
    • 49 (519.4 KB)
    • 50 (371.3 KB)
    • 51 (730.7 KB)
    • 52 (967.8 KB)
    • 53 (765.6 KB)
    • 54 (531.6 KB)
    • 55 (102.0 KB)
    • 56 (570.0 KB)
    • 57 (115.9 KB)
    • 58 (401.4 KB)
    • 59 (39.4 KB)
    • 60 (221.0 KB)
    • 61 (318.0 KB)
    • 62 (510.8 KB)
    • 63 (95.4 KB)
    • 64 (564.9 KB)
    • 65 (805.0 KB)
    • 66 (586.2 KB)
    • 67 (721.8 KB)
    • 68 (691.6 KB)
    • 69 (917.7 KB)
    • 70 (725.5 KB)
    • 71 (145.1 KB)
    • 72 (281.7 KB)
    • 73 (685.3 KB)
    • 74 (169.8 KB)
    • 75 (926.8 KB)
    • 76 (415.9 KB)
    • 77 (786.4 KB)
    • 78 (411.3 KB)
    • 79 (425.3 KB)
    • 80 (791.6 KB)
    • 81 (869.3 KB)
    • 82 (1,002.4 KB)
    • 83 (1,005.2 KB)
    • 84 (356.2 KB)
    • 85 (455.3 KB)
    • 86 (567.4 KB)
    • 87 (562.7 KB)
    • 88 (555.1 KB)
    • 89 (771.3 KB)
    • 90 (798.7 KB)
    • 91 (187.7 KB)
    • 92 (753.2 KB)

There are currently no comments. Feel free to leave one :)

Code:

  • udp://open.stealth.si:80/announce
  • udp://tracker.tiny-vps.com:6969/announce
  • udp://fasttracker.foreverpirates.co:6969/announce
  • udp://tracker.opentrackr.org:1337/announce
  • udp://explodie.org:6969/announce
  • udp://tracker.cyberia.is:6969/announce
  • udp://ipv4.tracker.harry.lu:80/announce
  • udp://tracker.uw0.xyz:6969/announce
  • udp://opentracker.i2p.rocks:6969/announce
  • udp://tracker.birkenwald.de:6969/announce
  • udp://tracker.torrent.eu.org:451/announce
  • udp://tracker.moeking.me:6969/announce
  • udp://tracker.dler.org:6969/announce
  • udp://9.rarbg.me:2970/announce
REVERSE-PROXY 🔄 RP (success) | 891ms | Saved: 1Opha4HJ... 📄 torrent 🕐 18 Jan 2026, 03:30:51 pm IST ⏰ 12 Feb 2026, 03:30:51 pm IST ✅ Valid for 24d 23h 🔄 Wait 10m