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:

1. Function Fundamentals: Initiate your learning journey by grasping the fundamental definitions of functions, establishing a solid foundation for subsequent topics.

2. Data Fitting Techniques: Progress through the course, delving into data fitting techniques essential for Machine Learning, Deep Learning, Artificial Intelligence, and Data Science applications.

3. Approximation Concepts: Explore important concepts related to approximation, a cornerstone for developing robust models in Machine Learning, Deep Learning, Artificial Intelligence, and Data Science.

4. 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?

1. 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.

2. Practical Application: Translate theoretical knowledge into practical skills by coding Neural Networks and Linear Regression models using Python.

3. 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

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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