| 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 | |||
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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| 2.9 GB | freecoursewb | 1 week | 15 | 5 | |
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Udemy - Revit Ideate - BIM Automation, Data Management and Documents Posted by
freecoursewb in Other
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2.4 GB | freecoursewb | 1 week | 11 | 10 |
| 952.8 MB | freecoursewb | 2 weeks | 45 | 16 |
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