Udemy - Feature Engineering and Dimensionality Reduction with Python

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Feature Engineering and Dimensionality Reduction with Python [TutsNode.com] - Feature Engineering and Dimensionality Reduction with Python 03 Feature Selection
  • 024 Activity- Feature Selection in Python.mp4 (260.3 MB)
  • 024 Activity- Feature Selection in Python.en.srt (72.3 KB)
  • 020 Statistical Based Methods.en.srt (25.3 KB)
  • 018 Search Strategy.en.srt (19.1 KB)
  • 023 Similarity Based Methods Criteria.en.srt (14.2 KB)
  • 021 Information Theoratic Methods.en.srt (13.4 KB)
  • 016 Wrapper Methods.en.srt (12.7 KB)
  • 017 Embedded Methods.en.srt (12.5 KB)
  • 019 Search Strategy Activity.en.srt (2.1 KB)
  • 022 Similarity Based Methods Introduction.en.srt (11.4 KB)
  • 013 Why Feature Selection.en.srt (10.2 KB)
  • 025 Activity- Feature Selection.en.srt (1.1 KB)
  • 015 Filter Methods.en.srt (6.7 KB)
  • 014 Feature Selection Methods.en.srt (3.6 KB)
  • 020 Statistical Based Methods.mp4 (104.4 MB)
  • 023 Similarity Based Methods Criteria.mp4 (71.0 MB)
  • 018 Search Strategy.mp4 (62.4 MB)
  • 021 Information Theoratic Methods.mp4 (59.9 MB)
  • 022 Similarity Based Methods Introduction.mp4 (57.9 MB)
  • 016 Wrapper Methods.mp4 (51.7 MB)
  • 017 Embedded Methods.mp4 (42.9 MB)
  • 013 Why Feature Selection.mp4 (38.3 MB)
  • 015 Filter Methods.mp4 (24.8 MB)
  • 014 Feature Selection Methods.mp4 (15.9 MB)
  • 019 Search Strategy Activity.mp4 (9.7 MB)
  • 025 Activity- Feature Selection.mp4 (5.4 MB)
05 Feature Extraction
  • 060 Dimensionality Reduction Pipelines Python Project.en.srt (39.3 KB)
  • 051 PCA Implementation.en.srt (37.1 KB)
  • 056 Kernel PCA vs The Rest.en.srt (21.1 KB)
  • 057 Encoder Decoder Networks For Dimensionality Reduction vs kernel PCA.en.srt (20.0 KB)
  • 058 Supervised PCA and Fishers Linear Discriminant Analysis.en.srt (19.0 KB)
  • 055 Kernel PCA vs ISOMAP.en.srt (17.8 KB)
  • 050 PCA Derivation.en.srt (16.6 KB)
  • 049 PCA Max Variance Formulation.en.srt (15.1 KB)
  • 053 PCA vs SVD.en.srt (13.0 KB)
  • 054 Kernel PCA.en.srt (12.8 KB)
  • 052 PCA For Small Sample Size Problems(DualPCA).en.srt (12.5 KB)
  • 047 PCA Criteria.en.srt (12.0 KB)
  • 060 Dimensionality Reduction Pipelines Python Project.mp4 (147.1 MB)
  • 048 PCA Properties.en.srt (8.6 KB)
  • 046 PCA Introduction.en.srt (7.8 KB)
  • 045 Feature Extraction Introduction.en.srt (7.1 KB)
  • 059 Supervised PCA and Fishers Linear Discriminant Analysis Activity.en.srt (2.3 KB)
  • 051 PCA Implementation.mp4 (146.1 MB)
  • 055 Kernel PCA vs ISOMAP.mp4 (123.0 MB)
  • 056 Kernel PCA vs The Rest.mp4 (119.2 MB)
  • 058 Supervised PCA and Fishers Linear Discriminant Analysis.mp4 (96.9 MB)
  • 057 Encoder Decoder Networks For Dimensionality Reduction vs kernel PCA.mp4 (72.7 MB)
  • 047 PCA Criteria.mp4 (70.2 MB)
  • 053 PCA vs SVD.mp4 (64.7 MB)
  • 052 PCA For Small Sample Size Problems(DualPCA).mp4 (63.5 MB)
  • 050 PCA Derivation.mp4 (61.6 MB)
  • 046 PCA Introduction.mp4 (59.4 MB)
  • 054 Kernel PCA.mp4 (59.3 MB)
  • 049 PCA Max Variance Formulation.mp4 (41.9 MB)
  • 048 PCA Properties.mp4 (27.6 MB)
  • 045 Feature Extraction Introduction.mp4 (20.2 MB)
  • 059 Supervised PCA and Fishers Linear Discriminant Analysis Activity.mp4 (7.8 MB)
01 Introduction
  • 003 Request for Your Honest Review.en.srt (2.5 KB)
  • 004 Link to Github to get the Python Notebooks.html (2.1 KB)
  • 001 Introduction To Instructor.en.srt (15.0 KB)
  • 002 Focus of the Course.en.srt (12.5 KB)
  • 001 Introduction To Instructor.mp4 (53.3 MB)
  • 002 Focus of the Course.mp4 (37.9 MB)
  • 003 Request for Your Honest Review.mp4 (28.8 MB)
04 Mathematical Foundation
  • 043 Linear Algebra Module Python.en.srt (19.9 KB)
  • 032 Coordinates vs Dimensions.en.srt (18.9 KB)
  • 033 SubSpace.en.srt (16.0 KB)
  • 030 Vector Space.en.srt (15.7 KB)
  • 027 Closure Of A Set.en.srt (14.9 KB)
  • 035 Matrix Product.en.srt (14.8 KB)
  • 040 Singular Value Decomposition SVD.en.srt (14.7 KB)
  • 028 Linear Combinations.en.srt (14.3 KB)
  • 031 Basis and Dimensions.en.srt (13.2 KB)
  • 039 Positive Semi Definite Matrix.en.srt (12.5 KB)
  • 038 Eigen Space.en.srt (11.5 KB)
  • 037 Rank.en.srt (9.4 KB)
  • 034 Orthonormal Basis.en.srt (9.4 KB)
  • 036 Least Squares.en.srt (8.8 KB)
  • 041 Lagrange Multipliers.en.srt (7.6 KB)
  • 029 Linear Independence.en.srt (7.5 KB)
  • 042 Vector Derivatives.en.srt (7.4 KB)
  • 026 Introduction to Mathematical Foundation of Feature Selection.en.srt (4.8 KB)
  • 044 Activity-Linear Algebra Module Python.en.srt (3.3 KB)
  • 032 Coordinates vs Dimensions.mp4 (89.6 MB)
  • 033 SubSpace.mp4 (72.4 MB)
  • 030 Vector Space.mp4 (68.5 MB)
  • 043 Linear Algebra Module Python.mp4 (67.2 MB)
  • 040 Singular Value Decomposition SVD.mp4 (65.8 MB)
  • 031 Basis and Dimensions.mp4 (59.5 MB)
  • 035 Matrix Product.mp4 (57.5 MB)
  • 039 Positive Semi Definite Matrix.mp4 (55.5 MB)
  • 028 Linear Combinations.mp4 (50.5 MB)
  • 027 Closure Of A Set.mp4 (39.5 MB)
  • 038 Eigen Space.mp4 (38.1 MB)
  • 036 Least Squares.mp4 (36.0 MB)
  • 037 Rank.mp4 (34.8 MB)
  • 042 Vector Derivatives.mp4 (33.7 MB)
  • 034 Orthonormal Basis.mp4 (31.4 MB)
  • 041 Lagrange Multipliers.mp4 (27.9 MB)
  • 029 Linear Independence.mp4 (27.4 MB)
  • 026 Introduction to Mathematical Foundation of Feature Selection.mp4 (13.7 MB)
  • 044 Activity-Linear Algebra Module Python.mp4 (13.3 MB)
02 Features in Data Science
  • 010 Why Dimensionality Reduction.en.srt (19.9 KB)
  • 011 Activity-Dimensionality Reduction.en.srt (1.3 KB)
  • 012 Feature Dimensionality Reduction Methods.en.srt (12.8 KB)
  • 007 Feature Space.en.srt (12.2 KB)
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Description


Description

Artificial Intelligence (AI) is indispensable these days. From preventing white-collar fraud, real-time aberration detection to forecasting customer churn, businesses are finding new ways to apply machine learning (ML). But how does this technology make accurate predictions? What is the secret behind the fail-proof AI magic? Let us start at the beginning.

The focus of the data science community is usually on algorithm selection and model training. While these elements are important, the most vital element in the AI/ML workflow isn’t how you choose or tune algorithms but what you input to AI/ML. This is where Feature Engineering plays a crucial role. Feature Engineering is essentially the process in which you apply domain knowledge and draw out analytical representations from raw data, preparing it for machine learning. Evidently, the holy grail of data science is Feature Engineering.

So, understanding the concepts of Feature Engineering and Dimensionality Reduction are the basic requirements for optimizing the performance of most of the machine learning models. Sophisticated and flexible models are sometimes useless if applied to data with irrelevant features.

The course Feature Engineering and Dimensionality Reduction, Theory and Practice in Python has been crafted to reflect the in-demand skills today, helping you to understand the concepts and methodology with respect to Python. The course is:

· Easy to understand.

· Imaginative and descriptive.

· Exhaustive.

· Practical with live coding.

· Establishes links between Feature Engineering and performance of Data Science models.

How is this course different?

This course is created for beginners, but we will go into great detail gradually.

This course is essentially a compilation of all the basics, thus encouraging you to move forward and experience much more than what you have learned. You are assigned activities/tasks in every module. The aim is to assess/(further build) your learning and update your knowledge based on the concepts and methods you have previously learned. Hence, your learning is step-by-step and totally related.

Data Science is, without a doubt, a rewarding career. You solve some of the most interesting problems, and in the bargain, you are rewarded with a handsome salary package. A clear understanding of Feature Engineering and Dimensionality Reduction will help you find new business solutions and ensure upward career growth.

Unlike other expensive courses, this in-depth course has been priced low and is easily affordable. You can master the concepts and methodologies of Feature Engineering and Dimensionality Reduction at a fraction of the cost of comparable courses. Our tutorials are grouped into a series of short HD videos along with code notebooks.

So, without any further delay, start this course. Embrace yourself with the latest AI knowledge.

Teaching is our passion:

We strive to create online tutorials with subject-matter experts who can help you in understanding the concepts very clearly. We aim to ensure that you have a strong basic understanding before you move onward to the advanced version. Our learning resources include high-quality video content, questions that assess what you have learned, relevant course material, course notes, and handouts. In case you have any doubts, you can approach our friendly team.

REMEMBER, the course comes with a 30-day money-back guarantee, so you can sign up today with no risk. So what are you waiting for? Enrol today, embrace the power of feature engineering and build better machine learning models.
Who this course is for:

People who want to get their data speak.
People who want to learn Feature Engineering and Dimensionality Reduction with real datasets in Data Science.
Individuals who are passionate about numbers and programming.
People who want to learn Feature Engineering and Dimensionality Reduction along with its implementation in realistic projects.
Data Scientists.
Business Analysts.

Requirements

No prior knowledge needed. We will start from the basics and gradually build up your knowledge in the subject.
A willingness to learn and practice.
A knowledge Python will be a plus.

Last Updated 3/2021



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Udemy - Feature Engineering and Dimensionality Reduction with Python

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3.6 GB
seeders:22
leechers:21
Udemy - Feature Engineering and Dimensionality Reduction with Python

Torrent hash: 900D74F52F4F0585DD495EFBA57381795A6F1F3E