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Master Class Machine Learning Using Google Cloud (2021) [CoursesGhar]

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English

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1.52 GB

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File	Size
!! IMPORTANT Note !!.txt	287.00 B
!!! Please Support !!! [CoursesGhar.Com].txt	197.00 B
00. Websites You May Like/CoursesGhar.Com.url	114.00 B
00. Websites You May Like/New Internet Shortcut.url	114.00 B
1. Introduction [CoursesGhar.Com]/1. Introduction.mp4	17.40 MB
1. Introduction [CoursesGhar.Com]/2. Introduction to Colab Google Cloud Development Environment.mp4	28.87 MB
10. How to go about an ML project [CoursesGhar.Com]/1. How to go about an ML project.mp4	19.86 MB
12. Exploratory Data Analysis (EDA) for Machine Learning [CoursesGhar.Com]/1. What is EDA.mp4	19.34 MB
12. Exploratory Data Analysis (EDA) for Machine Learning [CoursesGhar.Com]/2. Understanding the data.mp4	27.56 MB
12. Exploratory Data Analysis (EDA) for Machine Learning [CoursesGhar.Com]/3. Univariate Analysis.mp4	49.90 MB
12. Exploratory Data Analysis (EDA) for Machine Learning [CoursesGhar.Com]/4. Bivariate Analysis and Multi Collinearity.mp4	138.39 MB
12. Exploratory Data Analysis (EDA) for Machine Learning [CoursesGhar.Com]/5. Missing Values.mp4	21.49 MB
12. Exploratory Data Analysis (EDA) for Machine Learning [CoursesGhar.Com]/6. Outliers.mp4	69.61 MB
12. Exploratory Data Analysis (EDA) for Machine Learning [CoursesGhar.Com]/7. Variable Transformation.mp4	17.66 MB
13. Python Programming [CoursesGhar.Com]/1. Getting Started with Python.mp4	13.66 MB
13. Python Programming [CoursesGhar.Com]/10. Set.mp4	19.64 MB
13. Python Programming [CoursesGhar.Com]/11. Dictionary.mp4	12.98 MB
13. Python Programming [CoursesGhar.Com]/12. Getting Started with NumPy.mp4	28.48 MB
13. Python Programming [CoursesGhar.Com]/13. NumPy Shape in Arrays.mp4	12.65 MB
13. Python Programming [CoursesGhar.Com]/14. NumPy Iterating Arrays.mp4	5.71 MB
13. Python Programming [CoursesGhar.Com]/15. NumPy Joining Arrays.mp4	13.42 MB
13. Python Programming [CoursesGhar.Com]/16. NumPy Splitting Arrays.mp4	6.09 MB
13. Python Programming [CoursesGhar.Com]/17. NumPy Searching and Sorting Arrays.mp4	10.29 MB
13. Python Programming [CoursesGhar.Com]/18. Getting Started with Pandas.mp4	34.20 MB
13. Python Programming [CoursesGhar.Com]/19. Pandas Dataframe.mp4	28.08 MB
13. Python Programming [CoursesGhar.Com]/2. Variables.mp4	24.56 MB
13. Python Programming [CoursesGhar.Com]/20. Pandas Descriptive Statistics.mp4	10.83 MB
13. Python Programming [CoursesGhar.Com]/21. Pandas Sorting, Slicing, Flipping, Grouping Data.mp4	22.71 MB
13. Python Programming [CoursesGhar.Com]/22. Data Visualization using Matplotlib.mp4	33.03 MB
13. Python Programming [CoursesGhar.Com]/3. Operators.mp4	18.79 MB
13. Python Programming [CoursesGhar.Com]/4. Conditions.mp4	18.31 MB
13. Python Programming [CoursesGhar.Com]/5. Loops.mp4	19.88 MB
13. Python Programming [CoursesGhar.Com]/6. Functions.mp4	15.26 MB
13. Python Programming [CoursesGhar.Com]/7. Arrays.mp4	20.14 MB
13. Python Programming [CoursesGhar.Com]/8. List.mp4	15.41 MB
13. Python Programming [CoursesGhar.Com]/9. Tuple.mp4	19.82 MB
2. ML Concepts [CoursesGhar.Com]/1. ML Concepts.mp4	149.00 MB
3. Introduction to Colab Google Cloud Development Environment [CoursesGhar.Com]/1. Introduction to Colab Google Cloud Development Environment.mp4	25.92 MB
4. Simple Linear Regression 1 [CoursesGhar.Com]/1. Simple Linear Regression 1.mp4	70.80 MB
5. Simple Linear Regression 2 [CoursesGhar.Com]/1. Simple Linear Regression 2.mp4	40.90 MB
6. Multiple Linear Regression [CoursesGhar.Com]/1. Multiple Linear Regression.mp4	62.15 MB
7. Logistic Regression [CoursesGhar.Com]/1. Logistic Regression.mp4	187.49 MB
8. Decision Tree, Random Forest and XGBoost [CoursesGhar.Com]/1. Decision Tree, Random Forest and XGBoost.mp4	95.56 MB
9. Unsupervised Learning [CoursesGhar.Com]/1. Unsupervised Learning.mp4	105.96 MB
More !!! Courses.url	114.00 B
telegram @coursesghargate.url	128.00 B
Uploaded by [Coursesghar.com].txt	1.10 kB

Description

Master Class Machine Learning Using Google Cloud (2021) [CoursesGhar]
MP4 | Video: h264, 1280x720 | Audio: AAC, 44100 Hz | Language: English | Size: 1.51 GB | Duration: 5h 27m
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What you'll learn
Machine learning concepts
How to code and access data stored in a cloud environment
Simple and Multiple Linear Regression
Logistics Regression
Decision Tree
Random Forest
XG Boost
Unsupervised Algorithms - Centroid (kNN based) and Hierarchical
How to go about a ML project
Python programming
Exploratory Data Analysis (EDA)

Requirements
None. (Python is covered extensively in the course)

Description
Machine learning is a subset of artificial intelligence that is at the forefront of digital transformation in the world. Thanks to machine learning, it is now possible to detect diseases, know the defaulters of a loan and know the future sales of a product. All these information can be had proactively and not as an after the fact scenario. Machine learning and artificial intelligence-based roles are in great demand in the job market and such roles offer a higher salary than traditional programming roles.

This course covers the concepts of machine learning as well as the application of these concepts using case studies and examples, along with a walk through of the python codes. Python programming is also covered for the benefit of those who are new to python and those who want to refresh some of the topics in python.

The following algorithms are covered in detail:

Simple and multiple linear regression

Logistic regression

Decision tree, Random forest and XG boost

Unsupervised algorithms - Cluster (kNN based) and Hierarchical.

Learners will also understand how to develop the above machine learning in a cloud environment. They will learn not just to code in cloud but also to access the data stored in cloud. This will be particularly helpful to learners since many organizations are adopting cloud at a fast pace.

A key aspect of the course is the coverage of Exploratory Data Analysis (EDA). EDA covers the set of activities that you do before you start the ML project.

Lastly, how to pursue a machine learning project has been covered.

This course is taught by an industry veteran, who brings his vast experiences and practical perspectives into the program.

Who this course is for:
Professionals wanting to shift to ML roles
Students
ML professionals who are looking for a refresher