Calculus – Math for AI Data Science & Machine Learning
Calculus – Math for AI Data Science & Machine Learning, available at $74.99, has an average rating of 4.15, with 114 lectures, based on 273 reviews, and has 3679 subscribers.
You will learn about Build Mathematical intuition especially Calculus required for Deep learning, Data Science and Machine Learning The Calculus intuition required to become a Data Scientist / Machine Learning / Deep learning Practitioner How to take their Data Science / Machine Learning / Deep learning career to the next level Hacks, tips & tricks for their Data Science / Machine Learning / Deep learning career Implement Machine Learning / Deep learning Algorithms better Learn core concept to Implement in Machine Learning / Deep learning This course is ideal for individuals who are Data Scientists who wish to improve their career in Data Science. or Deep learning / Machine learning practitioner who wants to take the career to next level or Any one who wants to understand the underpinnings of Maths in Data Science, Machine Learning , Deep Learning and Artificial intelligence or Any Data Science / Machine Learning / Deep learning enthusiast or Any student or professional who wants to start or transition to a career in Data Science / Machine Learning / Deep learning 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 / Deep learning or Any people who are not satisfied with their job and who want to become a Data Scientist / Deep learning / Machine learning practitioner It is particularly useful for Data Scientists who wish to improve their career in Data Science. or Deep learning / Machine learning practitioner who wants to take the career to next level or Any one who wants to understand the underpinnings of Maths in Data Science, Machine Learning , Deep Learning and Artificial intelligence or Any Data Science / Machine Learning / Deep learning enthusiast or Any student or professional who wants to start or transition to a career in Data Science / Machine Learning / Deep learning 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 / Deep learning or Any people who are not satisfied with their job and who want to become a Data Scientist / Deep learning / Machine learning practitioner.
Enroll now: Calculus – Math for AI Data Science & Machine Learning
Summary
Title: Calculus – Math for AI Data Science & Machine Learning
Price: $74.99
Average Rating: 4.15
Number of Lectures: 114
Number of Published Lectures: 113
Number of Curriculum Items: 122
Number of Published Curriculum Objects: 121
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will Learn
- Build Mathematical intuition especially Calculus required for Deep learning, Data Science and Machine Learning
- The Calculus intuition required to become a Data Scientist / Machine Learning / Deep learning Practitioner
- How to take their Data Science / Machine Learning / Deep learning career to the next level
- Hacks, tips & tricks for their Data Science / Machine Learning / Deep learning career
- Implement Machine Learning / Deep learning Algorithms better
- Learn core concept to Implement in Machine Learning / Deep learning
Who Should Attend
- Data Scientists who wish to improve their career in Data Science.
- Deep learning / Machine learning practitioner who wants to take the career to next level
- Any one who wants to understand the underpinnings of Maths in Data Science, Machine Learning , Deep Learning and Artificial intelligence
- Any Data Science / Machine Learning / Deep learning enthusiast
- Any student or professional who wants to start or transition to a career in Data Science / Machine Learning / Deep learning
- 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 / Deep learning
- Any people who are not satisfied with their job and who want to become a Data Scientist / Deep learning / Machine learning practitioner
Target Audiences
- Data Scientists who wish to improve their career in Data Science.
- Deep learning / Machine learning practitioner who wants to take the career to next level
- Any one who wants to understand the underpinnings of Maths in Data Science, Machine Learning , Deep Learning and Artificial intelligence
- Any Data Science / Machine Learning / Deep learning enthusiast
- Any student or professional who wants to start or transition to a career in Data Science / Machine Learning / Deep learning
- 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 / Deep learning
- Any people who are not satisfied with their job and who want to become a Data Scientist / Deep learning / Machine learning practitioner
Unlock the Power of Calculus in Machine Learning, Deep Learning, Data Science, and AI with Python: A Comprehensive Guide to Mastering Essential Mathematical Skills”
Are you striving to elevate your status as a proficient data scientist? Do you seek a distinctive edge in a competitive landscape? If you’re keen on enhancing your expertise in Machine Learning and Deep Learning by proficiently applying mathematical skills, this course is tailor-made for you.
Calculus for Deep Learning: Mastering Calculus for Machine Learning, Deep Learning, Data Science, Data Analysis, and AI using Python
Embark on a transformative learning journey that commences with the fundamentals, guiding you through the intricacies of functions and their applications in data fitting. Gain a comprehensive understanding of the core principles underpinning Machine Learning, Deep Learning, Artificial Intelligence, and Data Science applications.
Upon mastering the concepts presented in this course, you’ll gain invaluable intuition that demystifies the inner workings of algorithms. Whether you’re crafting self-driving cars, developing recommendation engines for platforms like Netflix, or fitting practice data to a function, the essence remains the same.
Key Learning Objectives:
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Function Fundamentals: Initiate your learning journey by grasping the fundamental definitions of functions, establishing a solid foundation for subsequent topics.
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Data Fitting Techniques: Progress through the course, delving into data fitting techniques essential for Machine Learning, Deep Learning, Artificial Intelligence, and Data Science applications.
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Approximation Concepts: Explore important concepts related to approximation, a cornerstone for developing robust models in Machine Learning, Deep Learning, Artificial Intelligence, and Data Science.
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Neural Network Training: Leverage your acquired knowledge in the final sections of the course to train Neural Networks, gaining hands-on experience with Linear Regression models by coding from scratch.
Why Enroll in This Course?
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Comprehensive Learning: From fundamental function understanding to advanced concepts of approximation, the course covers a spectrum of topics for a well-rounded understanding of Calculus in the context of Data Science.
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Practical Application: Translate theoretical knowledge into practical skills by coding Neural Networks and Linear Regression models using Python.
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Premium Learning Experience: Developed by experts with valuable feedback from students, this course ensures a premium learning experience that aligns with industry demands.
Join now to build confidence in the mathematical aspects of Machine Learning, Deep Learning, Artificial Intelligence, and Data Science, setting yourself on a trajectory of continuous career growth. See you in Lesson 1!
Course Curriculum
Chapter 1: Basics of Calculus
Lecture 1: Why Calculus ?
Lecture 2: Understanding the Function
Lecture 3: Calculus Basics
Lecture 4: Finding a Derivative
Lecture 5: Exercise 1 – Finding the Derivative
Lecture 6: Derivatives using Delta Method
Lecture 7: Exercise – 2
Lecture 8: Product Rule for Differentiation
Lecture 9: Exercise – 3
Lecture 10: Chain Rule
Lecture 11: Exercise – 4
Lecture 12: Applying all the basics
Lecture 13: End of Section 1
Chapter 2: Multi Variate Calculus
Lecture 1: Multi Variate Calculus
Lecture 2: Exercise – 5
Lecture 3: Differentiate With respect to anything
Lecture 4: Exercise – 6
Lecture 5: Jacobians
Lecture 6: Exercise – 7
Lecture 7: Hessian
Lecture 8: Exercise – 8
Chapter 3: Chain Rule on Multi-Variate Functions
Lecture 1: Chain Rule on Multi Variate
Lecture 2: Chain Rule on Multi Variate – more functions
Chapter 4: Taylor Series of Approximations
Lecture 1: Taylor Series of Approximation
Lecture 2: Concept of Approximation
Lecture 3: Taylor Series – Intuition
Lecture 4: Taylor Series Detailed
Lecture 5: Taylor Series Derivation
Lecture 6: Taylor Series Derivation Part 2
Lecture 7: Taylor Series – More
Chapter 5: Neural Networks
Lecture 1: Neural Networks – Intro
Lecture 2: Bias in Neural Networks
Lecture 3: Neural Networks Part 2
Lecture 4: Calculus in Action – Neural Networks
Lecture 5: Intuition of Sigmoid Function
Lecture 6: Manual Fitting of Data
Lecture 7: Loss Function
Lecture 8: How to Update Parameters
Lecture 9: Compute Partial Derivative
Lecture 10: Exercise to compute Partial derivative of parameter – bias
Lecture 11: Program overview
Lecture 12: Program in Python
Chapter 6: Optimization Methods – Newton Raphson & Gradient Descent
Lecture 1: Newton Raphson Method
Lecture 2: Newton Raphson Method in Python
Lecture 3: Gradient Descent
Chapter 7: Linear Regression
Lecture 1: Linear Regression
Lecture 2: Linear Regression in Python
Lecture 3: Evaluation of Model – RMSE and R2 Score
Lecture 4: Implementation using Scikit Library
Chapter 8: Calculus for Deep Learning
Lecture 1: Calculus in Deep Neural Networks
Lecture 2: Calculus Update – Sigmoid Neuron
Lecture 3: Fit & Accuracy
Lecture 4: Deep Neural Network Update Parameters
Lecture 5: Deep Neural Network
Lecture 6: Perform Fit Deep Neural Networks
Lecture 7: Jupyter Notebook of the Section
Chapter 9: Working with Tensorflow
Lecture 1: Install Tensorflow
Lecture 2: Tensor Object – Constant
Lecture 3: Tensor Object – Variables
Lecture 4: Tensor Object – Shape,Rank & Type Casting
Lecture 5: Mathematical Operation & Broadcasting
Lecture 6: Matmul – Transpose – Reshaping
Lecture 7: Concat – Stack – Slice – Reduce
Lecture 8: Jupyter Notebook of the Section
Chapter 10: Finding the Derivative using Tensorflow – AutoGrad
Lecture 1: Finding the Derivative Mathematically
Lecture 2: Intro to Gradients
Lecture 3: AutoGrad Part 1
Lecture 4: AutoGrad Part 2
Lecture 5: Download the Jupyter Notebook of the Section
Chapter 11: Linear Regression with Deep learning
Lecture 1: Linear Regression from Scratch
Lecture 2: Linear Regression Fit on Data
Lecture 3: Download the Jupyter Notebook of the Section
Chapter 12: Linear Regression using Keras
Lecture 1: Linear Regression Using Keras API
Lecture 2: Load Data in Batches
Lecture 3: Download the Jupyter Notebook of the Section
Chapter 13: Deep learning Tasks
Lecture 1: Multi Class Classification – Creating the Model
Lecture 2: Multi Class Classification – Perform Fit
Lecture 3: Regression
Lecture 4: Regression Part 2
Instructors
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Manifold AI Learning ®
Learn the Future – Data Science, Machine Learning & AI
Rating Distribution
- 1 stars: 10 votes
- 2 stars: 7 votes
- 3 stars: 33 votes
- 4 stars: 97 votes
- 5 stars: 126 votes
Frequently Asked Questions
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