| 1 - Welcome-to-the-Course.pptx | 287.4 KB | ||
| 1 - Welcome.mp4 | 21 MB | ||
| 10 - Pandas Introduction and Installation.mp4 | 24.8 MB | ||
| 11 - Pandas Series.mp4 | 17 MB | ||
| 11 - Pandas-Series.ipynb | 1.5 KB | ||
| 12 - Pandas DataFrame.mp4 | 25.2 MB | ||
| 12 - Pandas-DataFrame-Practical.ipynb | 3.2 KB | ||
| 13 - Pandas ReadCSV.mp4 | 17.9 MB | ||
| 13 - Read-CSV.ipynb | 17.3 KB | ||
| 14 - Analyzing-DataFrames.ipynb | 60.1 KB | ||
| 14 - Pandas Analyzing DataFrames.mp4 | 46 MB | ||
| 15 - Matplotlib Introduction.mp4 | 27.3 MB | ||
| 15 - Matplotlib-Intro-and-Getting-started.ipynb | 15.1 KB | ||
| 16 - Different types of plots in Matplotlib.mp4 | 43.7 MB | ||
| 16 - Different-types-of-plots-in-Matplotlib.ipynb | 29.3 KB | ||
| 17 - Seaborn.mp4 | 60.3 MB | ||
| 18 - Handling Missing Values.mp4 | 62 MB | ||
| 18 - Handling-Missing-Values-1.pptx | 620.9 KB | ||
| 18 - Missing-Values.ipynb | 14.9 KB | ||
| 19 - Feature Encoding.mp4 | 64.4 MB | ||
| 19 - Feature-Encoding.ipynb | 36.8 KB | ||
| 19 - Feature-Encoding.pptx | 492.3 KB | ||
| 2 - Course Overview.mp4 | 32.4 MB | ||
| 2 - Course-Overview.pptx | 291.9 KB | ||
| 20 - Feature Scaling.mp4 | 54.1 MB | ||
| 20 - Feature-Scaling.ipynb | 117.3 KB | ||
| 21 - Machine Learning Introduction.mp4 | 35.2 MB | ||
| 22 - Supervised Machine Learning.mp4 | 26.3 MB | ||
| 23 - Unsupervised Machine Learning.mp4 | 21.6 MB | ||
| 24 - Train Test Split.mp4 | 13.9 MB | ||
| 25 - Regression Analysis.mp4 | 53.5 MB | ||
| 26 - Linear Regression.mp4 | 49.6 MB | ||
| 26 - Linear-Regression.ipynb | 22 KB | ||
| 26 - Linear-Regression.pptx | 360.1 KB | ||
| 26 - Salary-Data.csv | 409.6 B | ||
| 27 - Logistic Regression.mp4 | 71.5 MB | ||
| 27 - Logistic-Regression-Practical.ipynb | 25.3 KB | ||
| 27 - Logistic-Regression.pptx | 405.2 KB | ||
| 28 - K-Nearest-Neighbors-KNN.pptx | 519.8 KB | ||
| 28 - KNN-Practical.ipynb | 60.8 KB | ||
| 28 - KNN.mp4 | 75.3 MB | ||
| 28 - User-Data.csv | 10.7 KB | ||
| 29 - SVM-Practical.ipynb | 8.7 KB | ||
| 29 - SVM.mp4 | 60.9 MB | ||
| 29 - Support-Vector-Machine-SVM.pptx | 554.8 KB | ||
| 29 - User-Data.csv | 10.7 KB | ||
| 3 - Numpy Introduction and Installation.mp4 | 33.7 MB | ||
| 30 - Decision Tree.mp4 | 70.8 MB | ||
| 30 - Decision-Tree-Algorithm.pptx | 463.4 KB | ||
| 30 - Decision-Tree-Practical.ipynb | 6.4 KB | ||
| 30 - User-Data.csv | 10.7 KB | ||
| 31 - Random Forest.mp4 | 46.4 MB | ||
| 31 - Random-Forest-Algorithm.pptx | 400.8 KB | ||
| 31 - Random-Forest-Practical.ipynb | 6.7 KB | ||
| 31 - User-Data.csv | 10.7 KB | ||
| 32 - K Means Clustering.mp4 | 63.1 MB | ||
| 32 - K-Means-Clustering-Algorithm.pptx | 588.5 KB | ||
| 32 - K-Means-Practical.ipynb | 60.7 KB | ||
| 32 - Mall-Customers.csv | 4.7 KB | ||
| 33 - GridSearch CV.mp4 | 70.4 MB | ||
| 33 - GridSearch-CV.ipynb | 5.6 KB | ||
| 33 - GridSearchCV.pptx | 487.2 KB | ||
| 34 - ML-Pipeline.ipynb | 10.5 KB | ||
| 34 - Machine Learning Pipeline.mp4 | 54.3 MB | ||
| 34 - Machine-learning-Pipeline.pptx | 415.5 KB | ||
| 35 - Diabetes Prediction.mp4 | 75.5 MB | ||
| 35 - Diabetes-Prediction-Project.ipynb | 27.6 KB | ||
| 35 - diabetes.csv | 23.3 KB | ||
| 36 - Insurance Cost Prediction.mp4 | 79.8 MB | ||
| 36 - Medical-Insurance-Cost-Prediction.ipynb | 79.9 KB | ||
| 36 - insurance.csv | 54.3 KB | ||
| 4 - Creating Arrays in Numpy.mp4 | 49.5 MB | ||
| 4 - Creating-Arrays-Numpy.ipynb | 4.5 KB | ||
| 5 - Array Shape and Reshape.mp4 | 47.1 MB | ||
| 5 - Shape-and-Reshape.ipynb | 5.2 KB | ||
| 6 - Array Indexing.mp4 | 33.5 MB | ||
| 6 - Array-Indexing.ipynb | 4 KB | ||
| 7 - Array Iterating.mp4 | 24.1 MB | ||
| 7 - Array-Iterating-Practical.ipynb | 2.1 KB | ||
| 8 - Array Slicing.mp4 | 40 MB | ||
| 8 - Array-Slicing.ipynb | 4.3 KB | ||
| 9 - Searching and Sorting.mp4 | 30.6 MB | ||
| 9 - Searching-and-Sorting-numpy-array-prac.ipynb | 3.9 KB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 85 total files | |||
Data Science And Machine Learning Basic To Advanced
https://DevCourseWeb.com
Published 10/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.59 GB | Duration: 5h 0m
Complete Introduction to Data Science and Machine Learning from Basic to Advanced.
What you'll learn
Students will have develop understanding of libraries used for Data Analysis like Pandas and Numpy.
Learn to create impactful visualizations using Matplotlib and Seaborn. By creating these visualizations you will be able to derive better conclusions from data.
After this course you will learn to build complete Data Science Pipeline from Data preparation to building the best Machine Learning Model.
The course contains practical section after every new concept discussed and the course also has two projects at the end.
Requirements
Basic understanding of Python Programming Language.
Description
Learn how to use Numpy and Pandas for Data Analysis. This will cover all basic concepts of Numpy and Pandas that are useful in data analysis.Learn to create impactful visualizations using Matplotlib and Seaborn. Creating impactful visualizations is a crucial step in developing a better understanding about your data.This course covers all Data Preprocessing steps like working with missing values, Feature Encoding and Feature Scaling.Learn about different Machine Learning Models like Random Forest, Decision Trees, KNN, SVM, Linear Regression, Logistic regression etc... All the video sessions will first discuss the basic theory concept behind these algorithms followed by the practical implementation.Learn to how to choose the best hyper parameters for your Machine Learning Model using GridSearch CV. Choosing the best hyper parameters is an important step in increasing the accuracy of your Machine Learning Model.You will learn to build a complete Machine Learning Pipeline from Data collection to Data Preprocessing to Model Building. ML Pipeline is an important concept that is extensively used while building large scale ML projects.This course has two projects at the end that will be built using all concepts taught in this course. The first project is about Diabetes Prediction using a classification machine learning algorithm and second is about prediciting the insurance premium using a regression machine learning algorithm.
Overview
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