| 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 | |||
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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