Udemy - Forecast Future Demand of Phone Using Predictive Analytics

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Udemy - Forecast Future Demand of Phone Using Predictive Analytics (Size: 1.5 GB)
  1. Create project and application in Django.mp4 28.8 MB
  1. Create project and application in Django.srt 5.3 KB
  1. Feature selection.mp4 29 MB
  1. Feature selection.srt 4 KB
  1. Handling Missing Values.mp4 32.2 MB
  1. Handling Missing Values.srt 5.1 KB
  1. Installation of Python and Django.mp4 42.7 MB
  1. Installation of Python and Django.srt 5.1 KB
  1. Interface design.mp4 32.5 MB
  1. Interface design.srt 5.8 KB
  1. Introduction of course.mp4 34.4 MB
  1. Introduction of course.srt 3.7 KB
  1. Introduction of project.mp4 11.2 MB
  1. Introduction of project.srt 6.3 KB
  1. Introduction to Python.mp4 11.5 MB
  1. Introduction to Python.srt 2.5 KB
  1. Scikit learn.mp4 9.1 MB
  1. Scikit learn.srt 2.3 KB
  1. Summary of the course and Call to action.mp4 25.5 MB
  1. Summary of the course and Call to action.srt 3 KB
  2. Deploying model.mp4 175.5 MB
  2. Deploying model.srt 18.3 KB
  2. Handling Duplicated Vales.mp4 10.9 MB
  2. Handling Duplicated Vales.srt 5.9 KB
  2. Important configurations.mp4 35.4 MB
  2. Important configurations.srt 3.6 KB
  2. Installation of Anaconda Navigator.mp4 16.5 MB
  2. Installation of Anaconda Navigator.srt 6.8 KB
  2. Loading dataset.mp4 50.8 MB
  2. Loading dataset.srt 9 KB
  2. Meaning of predictive analytics.mp4 15.6 MB
  2. Meaning of predictive analytics.srt 3.1 KB
  2. Numpy.mp4 10.9 MB
  2. Numpy.srt 2.6 KB
  2. Train Test Split.mp4 19.1 MB
  2. Train Test Split.srt 3.2 KB
  2. Variable and data types.mp4 38.7 MB
  2. Variable and data types.srt 8.5 KB
  3. Data Conversion.mp4 26.5 MB
  3. Data Conversion.srt 4.2 KB
  3. Data exploration and Statistical analysis.mp4 37.1 MB
  3. Data exploration and Statistical analysis.srt 7.8 KB
  3. Fit the model in ml.mp4 57.4 MB
  3. Fit the model in ml.srt 7.3 KB
  3. Installation of Visual studio, PyCharm and Sublime Text Editor.mp4 76.2 MB
  3. Installation of Visual studio, PyCharm and Sublime Text Editor.srt 6 KB
  3. Meaning of machine learning.mp4 19.7 MB
  3. Meaning of machine learning.srt 2.8 KB
  3. Pandas.mp4 11.3 MB
  3. Pandas.srt 2.9 KB
  3. Relationship between Views, URL and template.mp4 70.4 MB
  3. Relationship between Views, URL and template.srt 8.8 KB
  3. Variable declaration and initialization.mp4 30.2 MB
  3. Variable declaration and initialization.srt 6.7 KB
  4. Data preprocessing.mp4 60.8 MB
  4. Data preprocessing.srt 7.6 KB
  4. Data visualization using pie chart.mp4 47.8 MB
  4. Data visualization using pie chart.srt 8.7 KB
  4. Machine Learning Vs Predictive analytics.mp4 26.4 MB
  4. Machine Learning Vs Predictive analytics.srt 4.2 KB
  4. Make prediction.mp4 93.9 MB
  4. Make prediction.srt 15.1 KB
  4. Matplolib.mp4 10.3 MB
  4. Matplolib.srt 2.4 KB
  4. String and Integer function.mp4 43.9 MB
  4. String and Integer function.srt 6.9 KB
  5. Accuracy measure.mp4 59.3 MB
  5. Accuracy measure.srt 7.5 KB
  5. Application of predictive analytics.mp4 25.9 MB
  5. Application of predictive analytics.srt 3.6 KB
  5. Array, list and dictionary.mp4 122.2 MB
  5. Array, list and dictionary.srt 19.9 KB
  5. Data visualization using bar graph.mp4 17 MB
  5. Data visualization using bar graph.srt 3.9 KB
  6. Dump and load the model using Joblib.mp4 28.4 MB
  6. Dump and load the model using Joblib.srt 5 KB
  6. If statement.mp4 47.2 MB
  6. If statement.srt 5.7 KB
  7. For loop.mp4 34.1 MB
  7. For loop.srt 5.2 KB
  Bonus Resources.txt 307.2 B
  Get Bonus Downloads Here.url 204.8 B
  ▲ 82 total files

Description


Forecast Future Demand of Phone Using Predictive Analytics
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 40 lectures (4h 2m) | Size: 1.32 GB
Python Machine Learning
What you'll learn:
40 videos to take you from beginner to machine learning engineer
Learn meaning of variable and data types in Python
Learn to declare and initialize variable in Python
Converting string to integer and integer to string in Python
Create list in Python and appending to a list
Create Numpy array, one dimensional Array and to convert one dimensional Array to two dimensional Array
Write condition statement in Python
Learn to use loop for performing iterating activity
Perform simple operations on dataset using Python
Understand meaning of Scikit learn, Pandas, Numpy and Matplotlib
Use Pandas to load dataset on Jupyter notebook
Visualizing data using Matplotlib
Analyze data using Pandas and Matplolib
Handling missing values using Pandas
Preprocessing of data using Pandas and scikit learn
Handling Duplicates using Pandas
Data conversion using Pandas
Converting dataframe to datetime
How to create a project and an application in Django
Important configuration for Django project
Learn the relationship between Views, URL and templates in Django
Understand meaning of machine learning and Predictive analytics
Feature selection in machine learning
Use train test split function to divide dataset into training and testing set
Train machine learning algorithm using training set
Use predictive analytics to discover the pattern and forecast the future
Measure accuracy of machine learning algorithm using cross validation
Make prediction using created model in machine learning
Create Django web application for deploying machine learning model
Learn to deploy machine learning model on Django web framework

Requirements
Python Basics
Machine Learning Basics
Visual Studio code, Sublime Text Editor And Pycharm
Computer with minimum of 4 RAM and 250 HDD.
Anaconda Navigator

Description
Become Artificial Intelligence Engineer.

This is step by step course on how to create predictive model using machine learning. It covers Numpy, Pandas, Matplotlib, Scikit learn and Django and at the end predictive model is deployed on Django. Most of things machine learning beginner do not know is how they can deploy a created model. How to put created model into application? Training model and getting 80%, 85% or 90% accuracy does not matter. As Artificial Intelligence Engineer you should be able to put created model into application.

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