Udemy - Data Science Mastery - 10-in-1 Data Interview Projects showoff

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Udemy - Data Science Mastery - 10-in-1 Data Interview Projects showoff (Size: 2.3 GB)
  1. 1. How to download Kaggle data in Google Collab!.mp4 27.6 MB
  1. 1. Introducing Fraud Detection and Conducting Exploratory Data Analysis..mp4 35.1 MB
  1. 1. Introduction to Big Data Analytics and Apache Spark..mp4 32.7 MB
  1. 1. Introduction to Customer Churn Prediction..mp4 29.5 MB
  1. 1. Introduction to House Prices Prediction..mp4 104.1 MB
  1. 1. Introduction to Predictive Modeling and Machine Learning..mp4 29.1 MB
  1. 1. Introduction to Sentiment Analysis & NLP..mp4 80.6 MB
  1. 1. Introduction..mp4 33.7 MB
  1. 1. Reading and Preprocessing Data..mp4 37.6 MB
  1. 1. Visual Exploring of Google App Store Data..mp4 60.9 MB
  1. Introduction.mp4 74.2 MB
  2. 2. Big Data Data Exploration and Preprocessing..mp4 29.9 MB
  2. 2. Creating Directories & The images data..mp4 27.8 MB
  2. 2. Data Cleaning and Preprocessing of Google App Store Data..mp4 46.1 MB
  2. 2. Data Exploration and Preprocessing of the Titanic Dataset..mp4 42.4 MB
  2. 2. Data Preprocessing and Cleaning..mp4 23.4 MB
  2. 2. Data Transformation and Visualization..mp4 29.7 MB
  2. 2. Feature Selection and Model Building..mp4 39.1 MB
  2. 2. Housing Data Processing & Cleaning For ML Model..mp4 123.8 MB
  2. 2. Model Building for Fraud Detection..mp4 42.7 MB
  2. 2. Text Preprocessing for Sentiment Analysis..mp4 108.9 MB
  3. 3. Advanced Techniques for Churn Prediction..mp4 51 MB
  3. 3. Advanced Techniques for Fraud Detection..mp4 103 MB
  3. 3. Big Data Transformation and Feature Engineering..mp4 27.6 MB
  3. 3. Data Visualization Techniques..mp4 97 MB
  3. 3. Doing EDA (Exploratory Data Analysis) Using Data Visualization..mp4 33.4 MB
  3. 3. Feature Extraction for Sentiment Analysis..mp4 78.3 MB
  3. 3. Image data preprocessing and visualization with Python..mp4 29 MB
  3. 3. Model Selection and Evaluation of The Titanic Dataset..mp4 31.3 MB
  3. 3. Train-Test Split and Model Selection..mp4 41.6 MB
  3. 3. Visualizing Time Series Data..mp4 30.2 MB
  4. 4. Big Data Visualization and Analysis..mp4 27.3 MB
  4. 4. Building Model for the Housing Data..mp4 19.8 MB
  4. 4. Building Sentiment Analysis Models..mp4 29.8 MB
  4. 4. Building and Evaluating Forecasting Models..mp4 48.4 MB
  4. 4. Creating and Validating Model using CNN..mp4 46.2 MB
  4. 4. Ensemble Methods and Model Evaluation..mp4 30.3 MB
  4. 4. Model Evaluation and Interpretability..mp4 33.8 MB
  4. 4. Model Training and Hyperparameter Tuning of The Titanic Dataset..mp4 59.5 MB
  4. 4. Model Training with XGBoost..mp4 30.7 MB
  4. 4. Statistical Analysis and Hypothesis Testing..mp4 49.8 MB
  5. 5. Conclusion and Next Steps..mp4 37.7 MB
  5. 5. Data Storytelling..mp4 51.7 MB
  5. 5. Deployment of The Predictive Models of The Titanic Dataset..mp4 61.5 MB
  5. 5. Evaluation of Sentiment Analysis Models..mp4 38.9 MB
  5. 5. Making Predictions and Submission..mp4 19.3 MB
  5. 5. Model Deployment..mp4 32.6 MB
  5. 5. Model Interpretation, Deployment, and Next steps..mp4 27 MB
  5. 5. Predicting Future Bitcoin Prices..mp4 53.4 MB
  5. 5. Validating Our Model..mp4 29 MB
  6. 6. Conclusion..mp4 66.6 MB
  Bonus Resources.txt 409.6 B
  Get Bonus Downloads Here.url 204.8 B
  ▲ 53 total files

Description


Data Science Mastery:10-in-1 Data Interview Projects showoff

https://FreeCourseWeb.com

Published 2/2024
Created by Tamer Ahmed
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 51 Lectures ( 5h 20m ) | Size: 2.31 GB

Comprehensive Machine Learning and Data Science Projects to Boost Your Career.

What you'll learn:
Students will learn how to preprocess, visualize, and extract meaningful insights from complex datasets, enhancing their data analysis skills.
Students will gain the ability to train machine learning models, evaluate their performance, and use them for future predictions, thereby mastering predictive m
Through sentiment analysis, students will master natural language processing techniques to classify text as positive, negative, or neutral.
Students will learn how to preprocess and visualize time series data and build robust forecasting models, gaining proficiency in time series analysis.
Students will scale up their data science skills with big data analytics, learning how to process large datasets using Apache Spark in a distributed computing.
Students will apply ML to real-world problems, such as customer churn prediction, image classification, fraud detection, and housing price prediction.
By working on ten hands-on projects, students will build a portfolio that showcases their skills and experience, making them industry-ready.
With the practical experience gained from this course, students will be well-prepared to transform their careers in the field of data science and ML.

Requirements:
Basic Understanding of Mathematics: Familiarity with basic mathematical concepts such as statistics and algebra is beneficial for understanding machine learning algorithms.
Some experience with programming, preferably in Python, is required as the course involves coding in Python for implementing machine learning models.
A basic understanding of machine learning concepts would be helpful but not mandatory. The course starts from the basics and gradually moves to advanced topics.
You should have a computer with internet access and the ability to install Python and related libraries for data analysis and machine learning. Instructions for setup will be provided in the course.
Most importantly, a sense of curiosity and enthusiasm for learning new concepts and techniques is essential!

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