Deep Learning Complete Guide for Calculus - Machine Learning
4.4 (52 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
671 students enrolled

Deep Learning Complete Guide for Calculus - Machine Learning

Mastering Calculus for Deep learning / Machine learning / Data Science / Data Analysis / AI using Python
4.4 (52 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
671 students enrolled
Last updated 2/2020
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Current price: $38.99 Original price: $59.99 Discount: 35% off
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This course includes
  • 6.5 hours on-demand video
  • 12 articles
  • 25 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Assignments
  • Certificate of Completion
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What you'll 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
Course content
Expand all 78 lectures 06:32:14
+ Introduction
12 lectures 42:39
Finding a Derivative
07:16
Exercise 1 - Finding the Derivative
00:03
Confirm whether you have completed the exercise 1 by typing as - YES and Submit
Exercise 1 - Completion confirmation
1 question
Derivatives using Delta Method
11:13
Exercise - 2
00:03
Confirm whether you have completed the Exercise 2 by typing as - YES and Submit
Exercise - 2 - Completion confirmation
1 question
Product Rule for Differentiation
08:28
Exercise - 3
00:03
Confirm whether you have completed the Exercise 3 by typing as - YES and Submit
Exercise - 3 - Completion confirmation
1 question
Chain Rule
03:38
Exercise - 4
00:03
Confirm whether you have completed the exercise 4 by typing as - YES and Submit
Exercise - 4 - Completion confirmation
1 question
Applying all the basics
03:27
End of Section 1
00:37
+ Multi Variate Calculus
8 lectures 15:57
Multi Variate Calculus
03:59
Exercise - 5
00:03
Confirm whether you have completed the exercise 5 by typing as - YES and Submit
Exercise - 5 - Completion confirmation
1 question
Differentiate With respect to anything
05:02
Exercise - 6
00:03
Confirm whether you have completed the exercise 6 by typing as - YES and Submit
Exercise - 6 - Completion confirmation
1 question
Jacobians
04:09
Exercise - 7
00:03
Confirm whether you have completed the exercise 7 by typing as - YES and Submit
Exercise - 7 - Completion confirmation
1 question
Hessian
02:34
Exercise - 8
00:03
Confirm whether you have completed the exercise 8 by typing as - YES and Submit
Exercise - 8 - Completion confirmation
1 question
+ Chain Rule on Multi-Variate Functions
2 lectures 09:12
Chain Rule on Multi Variate
04:07
Chain Rule on Multi Variate - more functions
05:05
+ Taylor Series of Approximations
7 lectures 47:48
Taylor Series of Approximation
00:37
Concept of Approximation
04:38
Taylor Series - Intuition
04:09
Taylor Series Detailed
10:36
Taylor Series Derivation
10:43
Taylor Series Derivation Part 2
06:40
Taylor Series - More
10:25
+ Neural Networks
12 lectures 01:19:40
Bias in Neural Networks
02:58
Neural Networks Part 2
04:49
Calculus in Action - Neural Networks
08:10
Intuition of Sigmoid Function
05:48
Manual Fitting of Data
08:15
Loss Function
04:25
How to Update Parameters
09:20
Compute Partial Derivative
08:07
Exercise to compute Partial derivative of parameter - bias
02:54
Program overview
03:30
Program in Python
12:09
+ Optimization Methods - Newton Raphson & Gradient Descent
3 lectures 20:45
Newton Raphson Method
10:32
Newton Raphson Method in Python
04:23
Gradient Descent
05:50
+ Linear Regression
4 lectures 25:50
Linear Regression
07:07
Linear Regression in Python
10:52
Evaluation of Model - RMSE and R2 Score
04:26
Implementation using Scikit Library
03:25
+ Solution for Exercise
8 lectures 21:10
Exercise 1 - Solution
02:51
Exercise 2 - Solution
04:55
Exercise 3 - Solution
01:59
Exercise 4 - Solution
04:21
Exercise 5 - Solution
01:06
Exercise 6 - Solution
03:11
Exercise 7 - Solution
01:46
Exercise 8 - Solution
01:01
+ Python for Data Science - Refresh the Basics
8 lectures 50:34
Source code download
00:01
Installing & Using Jupyter Notebook
08:09
Google Colab
01:09
Basic Data Types
12:12
Python Basics - Containers in Python
06:40
Control Statements Python if..else
04:49
Control Statements While & For
05:11
Functions & Classes in Python
12:23
+ Python for Data Science
9 lectures 01:11:31
Python Numpy Basics
08:24
Python Numpy Basics Contd
12:24
Python Numpy
07:16
Pandas in Python - Pandas Series
07:25
Pandas DataFrame
08:35
Pandas - Dealing with Missing Values
12:40
Matplotlib
10:22
Matplotlib - Density and Contour Plot
04:25
Source code download
00:00
Requirements
  • Pen and a paper to workout maths problem
  • Computer with Python to execute the code
  • Some programming experience
Description

Do you want to be better data Scientist ?


Are you looking for way to stand out in the crowd?


Interested in increasing your Machine Learning, Deep Learning expertise by effectively applying the mathematical skills ?


If the Answer is Yes.

Then, this course is for you.

Calculus for Deep learning

"Mastering Calculus for Deep learning / Machine learning / Data Science / Data Analysis / AI using Python "


With this course,

You start by learning the definition of function and move your way up for fitting the data to the function which is the core for any Machine learning,  Deep Learning , Artificial intelligence, Data Science Application.


Once you have mastered the concepts of this course, you will never be blind while applying the algorithm to your data, instead you have the intuition as how each code is working in background.


Whether you are building Self driving cars, or building the recommendation engine for Netflix, or trying to fit the practice data for a function


Your data,

Will have some type of labelled input and , some type of labelled output.

A typical goal would always be fit these data to the function by adjusting the parameters.



Hence in our course,

We start from understanding the basics of functions which you might have touched upon in highschool.

And then,

In further sections, we move along and apply the basics and learn some of the important concepts related to approximation which is the core for any Machine learning,  Deep Learning , Artificial intelligence, Data Science model


And, in the last two sections of this course,

We make use of all our learning from previous sections, and train our Neural networks and understand how we apply in Linear Regression models by writing the code from scratch.


We are sure that you will be amazed how well you can perform in your work once you have the intuition of calculus.

This course is carefully designed by experts with student’s feedback so that you can have the premium learning experience.


Join now to build confidence in Mathematics part of Machine learning,  Deep Learning , Artificial intelligence, Data Science and stay ahead in your career.


See you in the Lesson 1.


Who this course is for:
  • 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