[TutsNode.com] - Data Science Create Real World Projects

  9 - Introduction to Linear Regression

   49 - linear-regression-guide.zip

   45 - Learn about OLS [Ordinary Least Squares] algorithm English.vtt

   44 - Introduction to Linear Regression English.vtt

   48 - Introduction to R squared English.vtt

   49 - Implement Simple Linear Regression.mp4

   47 - Lecture Introduction to MSE, MAE, RMSE.mp4

  10 - Introduction to Logistic Regression

   53 - logistic.zip

   52 - Implement Logistic Regression part 1 English.vtt

   50 - Learn about Logistic Regression English.vtt

   52 - Implement Logistic Regression part 1.mp4

   50 - Learn about Logistic Regression.mp4

  6 - Introduction to Feature Transformation

   32 - min-max-scaler.ipynb

   33 - Standardization in practice English.vtt

   35 - one-hot-encoding.ipynb

   34 - Introduction to One Hot Encoding English.vtt

   31 - Data Standardization English.vtt

   30 - Data Normalization English.vtt

   32 - Normalization in practice.mp4

   34 - Introduction to One Hot Encoding.mp4

   31 - Data Standardization.mp4

  3 - Data Science Lifecycle Methodology

   5 - Data Science Methodologies English.vtt

   6 - CRISP English.vtt

   5 - Data Science Methodologies.mp4

   9 - Phases of CRISP-DM part 3.mp4

   8 - Phases of CRISP-DM part 2.mp4

  12 - Project 2 Natural Language Processing

   79 - Creating wordcloud English.vtt

   78 - Analyzing most commonly spoken words English.vtt

   73 - Loading the data to the project English.vtt

   85 - Topic Modeling Part Of Speech Tagging English.vtt

   75 - Storing data into the data frame English.vtt

   82 - Sentiment Label English.vtt

   86 - Text Generation English.vtt

   73 - Project-2.zip

   78 - Analyzing most commonly spoken words.mp4

   73 - Loading the data to the project.mp4

   87 - Text Generation Part 2.mp4

   75 - Storing data into the data frame.mp4

   83 - Plotting Polarity and Subjectivity.mp4

   81 - Sentimental Analysis.mp4

  5 - Cleaning data (Coding session) Feature Engineering

   28 - Sklearn-Feature-Importance.ipynb

   24 - Handle data type mismatch.mp4

   24 - Handle data type mismatch English.vtt

   27 - Feature Importance English.vtt

   26 - Handling missing data English.vtt

   27 - Feature Importance.mp4

   25 - Remove Duplicate data.mp4

  8 - Introduction to Decision Tree

   40 - Decision Tree part 1 English.vtt

   41 - Decision Tree part 2 English.vtt

   43 - Decision-Tree-Classifier.ipynb

   40 - Decision Tree part 1.mp4

   43 - Decision Tree GINI index.mp4

  7 - Introduction to Machine Learning

   39 - Train Test split Concept English.vtt

   37 - Structured format for datasets English.vtt

   39 - Train Test split Concept.mp4

   36 - Types of data in Machine Learning.mp4

  13 - Project 3 Artificial Intelligence Neural Network

   91 - Multi English.vtt

   93 - Multi English.vtt

   88 - Introduction to Neural Networks English.vtt

   90 - Learn to create MLP model.mp4

   93 - Multi-Layer Perception Algorithm part 4.mp4

   88 - Introduction to Neural Networks.mp4

  2 - Data Science Environment Setup

   3 - Set up environment and Download Machine Learning Libraries English.vtt

   4 - Introduction to Jupyter Notebook.mp4

   2 - Install anaconda on your machine.mp4

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

   69 - Mean Encoding for Categorical attributes English.vtt

   61 - Analysis 3 How does the price vary English.vtt

   70 - Preparing our data English.vtt

   63 - Analysis 4 Which months are busy months English.vtt

   60 - Analysis 2 How much do guests pay for room per night English.vtt

   59 - Analysis 1 Where do the guest come from English.vtt

   57 - Clean NA values English.vtt

   66 - Refine Numerical attributes English.vtt

   54 - Introduction to data and data dictionary English.vtt

   69 - Mean Encoding for Categorical attributes.mp4

   71 - Feature Importance.mp4

   64 - Analysis 5 How long do people stay at the hotels.mp4

   58 - Clean your data.mp4

   63 - Analysis 4 Which months are busy months.mp4

   67 - Refine Categorical attributes.mp4

   62 - Sorting.mp4

   66 - Refine Numerical attributes.mp4

   54 - Introduction to data and data dictionary.mp4

   55 - Setup project and import libraries.mp4

   55 - Project-1.zip

  4 - Introduction to Data Cleanup Munging

   19 - Cleaning the data English.vtt

   23 - Finalize Data Munging English.vtt

   22 - Introduction to Outliers English.vtt

   20 - Goal of data munging English.vtt

   17 - How to ensure data quality English.vtt

   10 - Why to clean the data English.vtt

   14 - Completeness of the data English.vtt

   20 - Goal of data munging.mp4

   21 - Understand your data.mp4

   11 - Data Quality.mp4

   17 - How to ensure data quality.mp4

   10 - Why to clean the data.mp4

   14 - Completeness of the data.mp4

   13 - Check if data is accurate or not.mp4

  1 - Welcome to the Course Start with Introduction

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