Linear Algebra Math for AI – Artificial Intelligence
Linear Algebra Math for AI – Artificial Intelligence, available at $94.99, has an average rating of 4.1, with 198 lectures, 1 quizzes, based on 371 reviews, and has 5052 subscribers.
You will learn about Build Mathematical intuition required for Data Science and Machine Learning The linear algebra intuition required to become a Data Scientist How to take their Data Science career to the next level Hacks, tips & tricks for their Data Science career Implement Machine Learning Algorithms better Apply Linear Algebra in Data Analysis Learn core concept to Implement in Machine Learning This course is ideal for individuals who are Data Scientists who wish to improve their career in Data Science. or Machine Learning Practitioners or Any one who wants to understand the underpinnings of Maths in Data Science, Machine Learning and Artificial intelligence or Any Data Science enthusiast or Any student or professional who wants to start or transition to a career in Data Science. or Students who want to refresh and learn important maths concepts required for Machine Learning , Deep Learning & Data Science. or Any data analysts who want to level up in Machine Learning. or Any people who are not satisfied with their job and who want to become a Data Scientist. It is particularly useful for Data Scientists who wish to improve their career in Data Science. or Machine Learning Practitioners or Any one who wants to understand the underpinnings of Maths in Data Science, Machine Learning and Artificial intelligence or Any Data Science enthusiast or Any student or professional who wants to start or transition to a career in Data Science. or Students who want to refresh and learn important maths concepts required for Machine Learning , Deep Learning & Data Science. or Any data analysts who want to level up in Machine Learning. or Any people who are not satisfied with their job and who want to become a Data Scientist.
Enroll now: Linear Algebra Math for AI – Artificial Intelligence
Summary
Title: Linear Algebra Math for AI – Artificial Intelligence
Price: $94.99
Average Rating: 4.1
Number of Lectures: 198
Number of Quizzes: 1
Number of Published Lectures: 198
Number of Published Quizzes: 1
Number of Curriculum Items: 199
Number of Published Curriculum Objects: 199
Number of Practice Tests: 1
Number of Published Practice Tests: 1
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will Learn
- Build Mathematical intuition required for Data Science and Machine Learning
- The linear algebra intuition required to become a Data Scientist
- How to take their Data Science career to the next level
- Hacks, tips & tricks for their Data Science career
- Implement Machine Learning Algorithms better
- Apply Linear Algebra in Data Analysis
- Learn core concept to Implement in Machine Learning
Who Should Attend
- Data Scientists who wish to improve their career in Data Science.
- Machine Learning Practitioners
- Any one who wants to understand the underpinnings of Maths in Data Science, Machine Learning and Artificial intelligence
- Any Data Science enthusiast
- Any student or professional who wants to start or transition to a career in Data Science.
- Students who want to refresh and learn important maths concepts required for Machine Learning , Deep Learning & Data Science.
- Any data analysts who want to level up in Machine Learning.
- Any people who are not satisfied with their job and who want to become a Data Scientist.
Target Audiences
- Data Scientists who wish to improve their career in Data Science.
- Machine Learning Practitioners
- Any one who wants to understand the underpinnings of Maths in Data Science, Machine Learning and Artificial intelligence
- Any Data Science enthusiast
- Any student or professional who wants to start or transition to a career in Data Science.
- Students who want to refresh and learn important maths concepts required for Machine Learning , Deep Learning & Data Science.
- Any data analysts who want to level up in Machine Learning.
- Any people who are not satisfied with their job and who want to become a Data Scientist.
Master Linear Algebra for Data Science, Machine Learning, and Deep Learning – Unleash the Power of Mathematics in AI Applications
Are you eager to enhance your skills in Machine Learning, Deep Learning, and Data Science by mastering the crucial foundation of Linear Algebra? Look no further – this comprehensive course is designed just for you.
With the increasing demand for expertise in Machine Learning and Deep Learning, it’s crucial to avoid the common mistake of relying solely on tools without a deep understanding of their underlying mathematical principles. This course is your key to developing a solid foundation in mathematics, providing you with a profound intuition of how algorithms work, their limitations, and the assumptions they rely on.
Why is a strong mathematical foundation important? Understanding the machinery under the hood is the key to becoming a confident practitioner in the fields of Machine Learning, Data Science, and Deep Learning. Linear Algebra is universally acknowledged as a fundamental starting point in the learning journey of these domains.
The basic elements of Linear Algebra – Vectors and Matrices – serve as the backbone for storing and processing data in various applications of Machine Learning, Data Science, and Artificial Intelligence. From basic operations to complex tasks involving massive datasets, Linear Algebra plays a pivotal role.
Even in advanced technologies like Deep Learning and Neural Networks, Matrices are employed to store inputs such as images and text, providing state-of-the-art solutions to complex problems.
Recognizing the paramount importance of Linear Algebra in a Data Science career, we have crafted a curriculum that ensures you build a strong intuition for the concepts without getting lost in complex mathematics.
By the end of this course, you will not only grasp the analytical aspects of Linear Algebra but also witness its practical implementation through Python. Additionally, you will gain insights into the functioning of the renowned Google PageRank Algorithm, utilizing the concepts learned throughout the course.
Here’s what the course covers:
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Vectors Basics
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Vector Projections
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Basis of Vectors
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Matrices Basics
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Matrix Transformations
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Gaussian Elimination
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Einstein Summation Convention
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Eigen Problems
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Google Page Rank Algorithm
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SVD – Singular Value Decomposition
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Pseudo Inverse
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Matrix Decomposition
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Solve Linear Regression using Matrix Methods
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Linear Regression from Scratch
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Linear Algebra in Natural Language Processing
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Linear Algebra for Deep Learning
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Linear Regression using PyTorch
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Bonus: Python Basics & Python for Data Science
This hands-on course takes you on a step-by-step journey, providing the essential Linear Algebra skills required for Data Science, Machine Learning, Natural Language Processing, and Deep Learning. Enroll now and embark on your journey to master the mathematical foundations powering AI applications. Click the ‘Enroll’ button to start your learning experience – I look forward to seeing you in Lecture 1!
Course Curriculum
Chapter 1: Vectors Basics
Lecture 1: Vectors – Basics
Lecture 2: Operation on Vectors
Lecture 3: Co- Ordination System
Lecture 4: Ready for Next Section
Chapter 2: Vector Projections
Lecture 1: Vector Magnitude and Direction
Lecture 2: Cosine Rule Triangle of Vectors
Lecture 3: Projection of Vector
Lecture 4: Operation of Vectors in Python
Lecture 5: Vector Norm / Magnitude
Chapter 3: Basis of Vectors
Lecture 1: Changing Basis of Vectors
Lecture 2: Understanding the Basis, Linear combination and Span
Chapter 4: Matrix Basics from High school
Lecture 1: Matrices Introduction
Lecture 2: Understanding the concept of Matrices
Lecture 3: Types of Matrices
Lecture 4: Operations on Matrices
Lecture 5: Matrix Multiplication
Lecture 6: Transpose of a Matrix
Lecture 7: Elementary Row Operations on Matrix
Lecture 8: Matrix Hands-on – Arithmetic Operations
Lecture 9: Types of Matrices
Lecture 10: Operations on Matrices
Chapter 5: Matrices – Setting up the stage – Transformations
Lecture 1: Setting the stage for Matrices
Lecture 2: how Matrices Transform
Lecture 3: Types of Matrix Transformation
Lecture 4: Combination or Composition
Chapter 6: Gaussian Elimination
Lecture 1: Gaussian Elimination – Solve Equations
Lecture 2: Vector Matrices relation
Lecture 3: Determinants
Chapter 7: Einstein Summation convention – Non Orthogonal basis – Gram Schmidt Process
Lecture 1: Einstein Summation convention
Lecture 2: Transforming non orthogonal basis
Lecture 3: Transforming to new Basis
Lecture 4: Orthogonal Matrices
Lecture 5: Gram-Schmidt Process
Lecture 6: Gram-Schmidt in Python
Chapter 8: Eigen Problems
Lecture 1: Vector Field
Lecture 2: Eigen Values
Lecture 3: Eigen – Mathematical sense
Lecture 4: Changing to Eigen Basis
Lecture 5: Changing Eigen Basis – Example
Lecture 6: Eigen Decomposition using Python
Lecture 7: Eigen Values Calculation – Recap
Lecture 8: Eigen values in Python
Chapter 9: Principal Component Analysis – Application of Eigen Values and Eigen Vectors
Lecture 1: PCA with Eigen Background
Lecture 2: LDA on Dimensionality Reduction
Chapter 10: Google Pagerank Algorithm
Lecture 1: Google Page Rank Algorithm
Lecture 2: Page Rank Algorithm – Python
Lecture 3: Damping
Chapter 11: SVD – Singular Value Decomposition
Lecture 1: SVD – Singular Value Decomposition
Lecture 2: SVD – Concept of Singular
Lecture 3: SVD – Building up for Reduced SVD
Lecture 4: Full SVD
Lecture 5: Compute terms of SVD
Lecture 6: Preparation for SVD in Python
Lecture 7: SVD
Lecture 8: SVD in Image Compression
Chapter 12: Pseudo Inverse
Lecture 1: Moore Penrose Pseudo inverse
Lecture 2: Pseudo Inverse – Python
Chapter 13: Matrix Decompositions
Lecture 1: Matrix Decompositions
Chapter 14: Solving the Linear Regression using Matrix Decomposition methods
Lecture 1: Solve Linear Equations Part 1
Lecture 2: Solve Linear Equations Part 2 – Normal Equation
Lecture 3: Solve Linear Equation Part 3 – Inverse QR
Chapter 15: Linear Regression from Scratch
Lecture 1: Loading the data
Lecture 2: Initialize & Predict
Lecture 3: Finding the cost
Lecture 4: Updation of Parameters – Finding the Partial Derivatives
Lecture 5: Gradient Descent with Python
Lecture 6: Linear Regression Run
Chapter 16: Linear Algebra in Natural Language Processing
Lecture 1: NLP Intro
Lecture 2: NLP – Data Exploration
Lecture 3: NLP – Count Vectorizer
Lecture 4: NLP – Apply Count Vectorizer on Text Data
Lecture 5: Understanding the Density of words
Lecture 6: Limit the number of Features
Lecture 7: Removal of Stop Words
Lecture 8: Porter Stemmer on plurals
Lecture 9: Stemming on a Sentence
Lecture 10: Stemming on Entire Text data
Lecture 11: Lemmatization on the text
Lecture 12: Applying Machine Learning Model on Text data
Lecture 13: One Hot Encoding on Text Data
Lecture 14: TFidf Vectorizer on Text Data
Chapter 17: Linear Algebra for Deep Learning – Getting started with Pytorch
Lecture 1: 1. Deep Learning Algebra Intro Installation
Instructors
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Manifold AI Learning ®
Learn the Future – Data Science, Machine Learning & AI
Rating Distribution
- 1 stars: 12 votes
- 2 stars: 12 votes
- 3 stars: 53 votes
- 4 stars: 114 votes
- 5 stars: 180 votes
Frequently Asked Questions
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