🔥 Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training
This Edureka Machine Learning Full Course video will help you understand and learn Machine Learning Algorithms in detail. This Machine Learning Tutorial is ideal for both beginners as well as professionals who want to master Machine Learning Algorithms. Below are the topics covered in this Machine Learning Tutorial for Beginners video:
00:00 Introduction
2:47 What is Machine Learning?
4:08 AI vs ML vs Deep Learning
5:43 How does Machine Learning works?
6:18 Types of Machine Learning
6:43 Supervised Learning
8:38 Supervised Learning Examples
11:49 Unsupervised Learning
13:54 Unsupervised Learning Examples
16:09 Reinforcement Learning
18:39 Reinforcement Learning Examples
19:34 AI vs Machine Learning vs Deep Learning
22:09 Examples of AI
23:39 Examples of Machine Learning
25:04 What is Deep Learning?
25:54 Example of Deep Learning
27:29 Machine Learning vs Deep Learning
33:49 Jupyter Notebook Tutorial
34:49 Installation
50:24 Machine Learning Tutorial
51:04 Classification Algorithm
51:39 Anomaly Detection Algorithm
52:14 Clustering Algorithm
53:34 Regression Algorithm
54:14 Demo: Iris Dataset
1:12:11 Stats & Probability for Machine Learning
1:16:16 Categories of Data
1:16:36 Qualitative Data
1:17:51 Quantitative Data
1:20:55 What is Statistics?
1:23:25 Statistics Terminologies
1:24:30 Sampling Techniques
1:27:15 Random Sampling
1:28:05 Systematic Sampling
1:28:35 Stratified Sampling
1:29:35 Types of Statistics
1:32:21 Descriptive Statistics
1:37:36 Measures of Spread
1:44:01 Information Gain & Entropy
1:56:08 Confusion Matrix
2:00:53 Probability
2:03:19 Probability Terminologies
2:04:55 Types of Events
2:05:35 Probability of Distribution
2:10:45 Types of Probability
2:11:10 Marginal Probability
2:11:40 Joint Probability
2:12:35 Conditional Probability
2:13:30 Use-Case
2:17:25 Bayes Theorem
2:23:40 Inferential Statistics
2:24:00 Point Estimation
2:26:50 Interval Estimate
2:30:10 Margin of Error
2:34:20 Hypothesis Testing
2:41:25 Supervised Learning Algorithms
2:42:40 Regression
2:44:05 Linear vs Logistic Regression
2:49:55 Understanding Linear Regression Algorithm
3:11:10 Logistic Regression Curve
3:18:34 Titanic Data Analysis
3:58:39 Decision Tree
3:58:59 what is Classification?
4:01:24 Types of Classification
4:08:35 Decision Tree
4:14:20 Decision Tree Terminologies
4:18:05 Entropy
4:44:05 Credit Risk Detection Use-case
4:51:45 Random Forest
5:00:40 Random Forest Use-Cases
5:04:29 Random Forest Algorithm
5:16:44 KNN Algorithm
5:20:09 KNN Algorithm Working
5:27:24 KNN Demo
5:35:05 Naive Bayes
5:40:55 Naive Bayes Working
5:44:25Industrial Use of Naive Bayes
5:50:25 Types of Naive Bayes
5:51:25 Steps involved in Naive Bayes
5:52:05 PIMA Diabetic Test Use Case
6:04:55 Support Vector Machine
6:10:20 Non-Linear SVM
6:12:05 SVM Use-case
6:13:30 k Means Clustering & Association Rule Mining
6:16:33 Types of Clustering
6:17:34 K-Means Clustering
6:17:59 K-Means Working
6:21:54 Pros & Cons of K-Means Clustering
6:23:44 K-Means Demo
6:28:44 Hierarchical Clustering
6:31:14 Association Rule Mining
6:34:04 Apriori Algorithm
6:39:19 Apriori Algorithm Demo
6:43:29 Reinforcement Learning
6:46:39 Reinforcement Learning: Counter-Strike Example
6:53:59 Markov’s Decision Process
6:58:04 Q-Learning
7:02:39 The Bellman Equation
7:12:14 Transitioning to Q-Learning
7:17:29 Implementing Q-Learning
7:23:33 Machine Learning Projects
7:38:53 Who is a ML Engineer?
7:39:28 ML Engineer Job Trends
7:40:43 ML Engineer Salary Trends
7:42:33 ML Engineer Skills
7:44:08 ML Engineer Job Description
7:45:53 ML Engineer Resume
7:54:48 Machine Learning Interview Questions

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Post Graduate Certification in Data Science with IIT Guwahati – https://www.edureka.co/post-graduate/data-science-program
(450+ Hrs || 9 Months || 20+ Projects & 100+ Case studies)

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Dr. Stuart Russell is a Professor of Computer Science at the University of California, Berkeley and Adjunct Professor of Neurological Surgery at the University of California, San Francisco.

Dr. Russell’s research spans many areas of artificial intelligence, including machine learning, probabilistic reasoning, and philosophical foundations. Recently, his work has focused on ensuring that advanced AI is developed safely.

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With more board configurations than there are atoms in the universe, the ancient Chinese game of Go has long been considered a grand challenge for artificial intelligence. On March 9, 2016, the worlds of Go and artificial intelligence collided in South Korea for an extraordinary best-of-five-game competition, coined The DeepMind Challenge Match. Hundreds of millions of people around the world watched as a legendary Go master took on an unproven AI challenger for the first time in history.

Directed by Greg Kohs with an original score by Academy Award nominee, Hauschka, AlphaGo chronicles a journey from the halls of Oxford, through the backstreets of Bordeaux, past the coding terminals of DeepMind in London, and ultimately, to the seven-day tournament in Seoul. As the drama unfolds, more questions emerge: What can artificial intelligence reveal about a 3000-year-old game? What can it teach us about humanity?

** Edureka RPA Training (Use Code: YOUTUBE20) : https://www.edureka.co/robotic-process-automation-certification-courses **
This Edureka Robotic Process Automation Full Course video will help you understand and learn RPA in detail. This RPA Tutorial is ideal for both beginners as well as professionals who want to master RPA tools such as UiPath & Automation Anywhere. Below are the topics covered in this RPA tutorial video:
1:56 Introduction to RPA
2:26 Why RPA?
8:46 What is RPA?
10:16 RPA Tools
10:26 RPA Lifecycle
11:01 Discovery Phase
14:36 Solution Design Phase
17:35 Development Phase
18:16 UAT
18:51 Deployment Phase
19:21 Execute Bots
22:31 Introduction to UiPath
23:01 UiPath Studio
23:11 UiPath Installation
25:36 UiPath Studio Projects
29:46 UiPath Studio Ribbon Components
40:01 UiPath Studio Activity Pane Components
42:41 UIPath Studio Properties Pane
43:16 UiPath Studio Output Pane
49:46 UiPath RPA Architecture
50:11 UiPath Platform Components
50:21 UiPath Studio
52:21 UiPath Orchestrator
55:21 UiPath Architecture
58:11 Variables, Data Types & Activities in UiPath
1:03:11 Types of Variables
1:08:56 Data Types
1:10:16 Activities
1:12:16 Message Box
1:15:51 Write CSV Activity
1:18:56 If Activity
1:21:51 For Each Activity
1:24:41 While Activity
1:27:31 Do While Activity
1:29:06 Switch Activity
1:31:26 Automations in UiPath
1:31:31 Why Excel Automation?
1:32:21 Installing Excel Activities in UiPath Studio
1:33:46 Demo; Automating the Filling of a Form
1:48:06 UiPath Selectors
1:48:51 What are Selectors?
1:51:16 Why do we need Selectors in UiPAth?
1:53:26 Demo: Selectors in UiPath
2:21:51 UiPath Web Automation
2:24:01 Hands-on: Web Scraping of Google Contacts
2:24:01 Hands-on: Extracting Data From E-Commerce Website
2:46:26 UiPath PDF Automation
2:48:01 Types of PDF Activities
2:49:43 Demo: Extracting Large Texts
3:03:08 Demo: Extracting Specific Element
3:11:58 UiPath Email Automation
3:15:03 Demo: UiPath Email Automation
3:45:53 UiPath Citrix Automation
3:46:18 Automating Virtual Machine
3:48:53 Why Citrix Automation?
3:49:53 Hands-on: Simple Desktop Application
3:59:18 Debugging & Error Handling in UiPath
3:59:43 Debugging in UiPath
4:17:33 Exception Handling in UiPath
4:22:08 UiPath Tips & Tricks
4:29:28 Orchestrator in UiPath
4:33:38 UiPath Orchestrator Community Edition
5:06:23 UiPath ReFramework
5:06:38 Why Re-Framework?
5:08:48 What is Re-Framework?
5:12:38 How to use Re-Framework?
5:14:43 Re-Framework Architecture
5:18:11 INIT State
5:32:01 Get Transaction Data State
5:39:13 Process Transaction State
5:54:28 End Process State
5:58:18 RPA & UiPath Interview Questions
7:11:50 Introduction to Automation Anywhere
7:11:55 What is Automation Anywhere?
7:12:55 Automation Anywhere Architecture
7:15:30 Products of Automation Anywhere
7:23:50 Industries Using Automation Anywhere
7:28:05 Automation Anywhere Installation
7:44:50 Install & Setup Client
7:49:00 Control Room & Bots
7:49:35 Automation Anywhere Architecture
7:52:40 Control Room Components
8:02:10 Hands-on
8:06:30 Automation Anywhere Bots
8:08:00 Types of Automation Anywhere Bots
8:08:20 Task Bots
8:10:50 Meta Bots
8:25:20 IQ Bots
8:27:08 Products of Automation Anywhere
8:28:13 Automation Anywhere Examples
8:28:53 Windows Action
8:33:08 Mouse Clicks
8:35:58 String Operations
8:41:23 Files & Folders
8:44:38 Web Recorders
8:49:34 OCR
8:51:49 Key Strokes
8:53:14 REST Web Services
8:55:39 Excel Automation
9:04:14 PDF Automation
9:15:24 Automation Anywhere Interview Questions
9:17:04 Basic Questions
9:33:19 Tool-based Questions
9:57:04 Scenario-based Questions

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Matt Zeiler is the CEO of Clarifai, a company that uses artificial intelligence and machine learning to recognize and identify different videos and images.

While there are a lot of uses for image recognition — you’ve probably most recently interacted with facial recognition, for instance, on Facebook — Zeiler said online retail stands to benefit significantly from using AI.

“You have to be thinking about it in your business,” Zeiler said at Recode’s Code Commerce event in New York. “This is going to change every interaction with your customers.”

Watch his full presentation.

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As one of the world leaders in cyber tech, Israel hosted 2019 Cybertech in Tel Aviv, with Israeli Prime Minister Netanyahu making an appearance to speak about the industry developments and how they are being used against the country’s enemies.

Story:

Israel’s Prime Minister Benjamin Netanyahu that his country foils Iranian-directed cyber attacks against it ‘on a daily basis’ as he touted Israel’s cyber prowess at the CyberTech conference in Tel Aviv on Tuesday.

‘Iran attacks Israel on a daily basis. We monitor these attacks, we see these attacks, and we foil these attacks all the time,’ Netanyahu said.

‘Iran threatens us in many other ways. They issued in the last 24 hours threats that say they’ll destroy us, they’ll target our cities with missiles. We’re not oblivious to these threats. They don’t impress us. Because we know what out power is both in defense and in offense,’ he added.

‘But the important thing is that any country can be attacked today with cyber attacks and every country needs the combination of a national cyber defense effort and a robust cybersecurity industry. And I think Israel has that in ways that are in many ways unmatched.’

Netanyahu was speaking at the annual CyberTech conference in Tel Aviv, which drew thousands of cyber experts and firms from more than 80 countries for discussions on the latest trends in cyber and innovation.

Netanyahu touted Israel’s influence in the industry relative to its population, attributing it to investment in research and development and its leveraging of its national cyber defense efforts to boost private industry.

Israel is considered a world leader in cyber technology and innovation, exporting cybersecurity worth $3.8 billion in 2017 and is ranking among the top 10 percent in cyber academic research.

In the span of just a decade, Netanyahu said, the world’s top ten largest companies have all opened ‘major’ research and development centers in Israel, among them Apple, Google, Microsoft, Amazon, and Facebook.

‘Israel leads the world in investment in R&D as a percentage of [gross domestic product]…and has by far the highest percentage of R&D personnel,’ Netanyahu explained.

‘Cybersecurity is not merely here to stay, it’s going to grow exponentially,’ Netanyahu said.

‘I decided several years ago to turn Israel into one of the five cyber powers of the world and that required allowing this combination of military intelligence, academia, and industry to converge in one place. And where we’re doing that is in Beersheba in the south,’ Netanyahu said.

Beersheba, a city in the vast Negev desert of southern Israel, has experienced a rapid gentrification since the start of the decade fueled by the city’s ambition to become Israel’s cyber capital, especially since the creation of its cyber industrial park CyberSpark.

The two ultra-modern complexes house a dozen Israeli companies, start-ups, venture capital funds and foreign groups — such as Lockheed Martin, Deutsche Telekom, Oracle and IBM — which employ thousands technicians, engineers and researchers many of whom have studied at the computer sciences department of the local Ben Gurion University — part of a planned symbiosis between the university and the company.

Last year, the Israel Innovation Authority, the Ministry of Economy and Industry, and the National Cyber Directorate announced a dedicated program to strengthen the country’s cyber industry with an investment of NIS 90 million over the next three years.

A significant portion of the investment has been earmarked for further enhancing Beersheba’s CyberSpark hub.

A documentary exploring how artificial intelligence is changing life as we know it — from jobs to privacy to a growing rivalry between the U.S. and China.

FRONTLINE investigates the promise and perils of AI and automation, tracing a new industrial revolution that will reshape and disrupt our world, and allow the emergence of a surveillance society.

This journalism is made possible by viewers like you. Support your local PBS station here: http://www.pbs.org/donate

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FRONTLINE is streaming more than 200 documentaries online, for free, here: http://to.pbs.org/hxRvQP

Funding for FRONTLINE is provided through the support of PBS viewers and by the Corporation for Public Broadcasting. Major funding for FRONTLINE is provided by the John D. and Catherine T. MacArthur Foundation and the Ford Foundation. Additional funding is provided by the Abrams Foundation, the Park Foundation, The John and Helen Glessner Family Trust, and the FRONTLINE Journalism Fund with major support from Jon and Jo Ann Hagler on behalf of the Jon L. Hagler Foundation.

Filmmaker Robin Hauser is a proven storyteller of complex topics. In her award-winning documentary, “Code—Debugging the Gender Gap” she examined the dearth of women in computer coding.

Now, in her latest film, “Bias”, Robin posits compelling questions: how have primal human survival instincts made racial and gender bias an innate part of ourselves; and with the rise of machine learning, with increasing reliance on AI, can we protect Artificial Intelligence from our inherent biases? Her film is an engrossing exploration and clarion call that will frighten and also enlighten.

Today’s Guest: Robin Hauser
@biasfilm
https://www.biasfilm.com/

Interviewer: Jim Kamp
http://polychromemedia.com/jameskamp/
@kampjames

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🔥 Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training
This Edureka video on “Artificial Intelligence” will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples.

Following topics are covered in this video:
02:27 History Of AI
06:45 Demand For AI
08:46 What Is Artificial Intelligence?
09:50 AI Applications
16:49 Types Of AI
20:24 Programming Languages For AI
27:12 Introduction To Machine Learning
28:08 Need For Machine Learning
31:48 What Is Machine Learning?
34:13 Machine Learning Definitions
37:26 Machine Learning Process
49:13 Types Of Machine Learning
49:21 Supervised Learning
52:00 Unsupervised Learning
53:44 Reinforcement Learning
55:29 Supervised vs Unsupervised vs Reinforcement Learning
58:23 Types Of Problems Solved Using Machine Learning
1:04:49 Supervised Learning Algorithms
1:05:17 Linear Regression
1:11:20 Linear Regression Demo
1:26:36 Logistic Regression
1:35:36 Decision Tree
1:55:18 Random Forest
2:07:31 Naive Bayes
2:14:37 K Nearest Neighbour (KNN)
2:20:31 Support Vector Machine (SVM)
2:26:40 Demo (Classification Algorithms)
2:42:36 Unsupervised Learning Algorithms
2:42:45 K-means Clustering
2:50:49 Demo (Unsupervised Learning)
2:56:40 Reinforcement Learning
3:24:36 Demo (Reinforcement Learning)
3:31:41 AI vs Machine Learning vs Deep Learning
3:33:08 Limitations Of Machine Learning
3:36:32 Introduction To Deep Learning
3:38:36 How Deep Learning Works?
3:40:48 What Is Deep Learning?
3:41:50 Deep Learning Use Case
3:43:14 Single Layer Perceptron
3:50:56 Multi Layer Perceptron (ANN)
3:52:55 Backpropagation
3:54:39 Training A Neural Network
4:01:02 Limitations Of Feed Forward Network
4:03:18 Recurrent Neural Networks
4:05:36 Convolutional Neural Networks
4:09:00 Demo (Deep Learning)
4:29:02 Natural Language Processing
4:30:53 What Is Text Mining?
4:32:43 What Is NLP?
4:33:26 Applications Of NLP
4:35:53 Terminologies In NLP
4:41:19 NLP Demo
4:47:21 Machine Learning Masters Program

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About the Masters Program

Edureka’s Machine Learning Engineer Masters Program makes you proficient in techniques like Supervised Learning, Unsupervised Learning and Natural Language Processing. It includes training on the latest advancements and technical approaches in Artificial Intelligence & Machine Learning such as Deep Learning, Graphical Models and Reinforcement Learning.

The Master’s Program Covers Topics LIke:
Python Programming
PySpark
HDFS
Spark SQL
Machine Learning Techniques and Artificial Intelligence Types
Tokenization
Named Entity Recognition
Lemmatization
Supervised Algorithms
Unsupervised Algorithms
Tensor Flow
Deep learning
Keras
Neural Networks
Bayesian and Markov’s Models
Inference
Decision Making
Bandit Algorithms
Bellman Equation
Policy Gradient Methods.

———————-

Prerequisites

There are no prerequisites for enrolment to the Masters Program. However, as a goodwill gesture, Edureka offers a complimentary self-paced course in your LMS on SQL Essentials to brush up on your SQL Skills. This program is designed and developed for an aspirant planning to build a career in Machine Learning or an experienced professional working in the IT industry.

————————————–

Please write back to us at sales@edureka.in or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information

Exclusive interview with Stuart Russell. He discusses the importance of achieving friendly AI – Strong AI that is provably (probably approximately) beneficial.

Points of discussion:
A clash of intuitions about the beneficiality of Strong Artificial Intelligence
– A clash of intuitions: Alan Turing raised the concern that if we were to build an AI smarter than we are, we might not be happy about the results. While there is a general notion amoungst AI developers etc that building smarter than human AI would be good.
– But it’s not clear why the objectives of Superintelligent AI will be inimicable to our values – so we need to solve what some poeple call the value alignment problem.
– we as humans learn values in conjunction with learning about the world

The Value Alignment problem

Basic AI Drives: Any objective generates sub-goals

– Designing an AI not want to disable it’s off switch
– 2 principles
– 1) its only objective is to maximise your reward function (this is not an objective programmed into the machine but is a kind of (non-observed) latent variable
– 2) the machine has to be explicitly uncertain about what that objective is
– if the robot thinks it knows what your objective functions are, then it won’t believe that it will make you unhappy and therefore has an incentive to disable the off switch
– the robot will only want to be switched off if thinks it will makes you unhappy

– How will the machines do what humans want if they can’t see their objective functions?
– one answer is to allow the machines to observe human behaviour, and interpret that behaviour as providing evidence of an underlying preference structure – inverse reinforcement learning

Aggregated Volition: How does an AI optimise for many peoples values?
– Has the benefit of symmetry
– difficulties in commensurbaility of different human preferences
– Problem: If someone feels more strongly about a value X should they get more of a share of value X?

How to deal with people who’s preferences include the suffering of others?

Should a robot be more obligated to its owner than to the rest of the world?
– should this have something to do with how much you pay for the robot?

Moral philosophy will be a key industry sector

Issues of near term Narrow AI vs future Strong AI
– Very easy to confuse the near term killer robot question with the existential risk question

Differences in the issues with the risk of the misuse of Narrow AI and the risk of Strong AI
– Weaponised Narrow AI

Should we replace the gainful employment of humans with AI?

A future where humans lose a sense of meaning & dignity

Hostility to the idea of Superintelligence and AI Friendline
– there seems to be something else going on for AI experts to make rational arguments as simple minded as ‘If the AI goes bad, just turn the AI off’
– beating alphago is no problem – we just need to play better moves
– it’s theoretically possibe that AI could pose existential risk – but it’s also possible that a black hole could appear in near earth orbit – we don’t spend any time worrying about that so why should we spend time worrying about the existential risk of AI?

Defensive psychological reactions to feeling one’s research is under attack
– People proposing AI safety are not anti AI any more than people wanting to contain a nuclear reaction are anti physics

Provably beneficial AI
– where the AI systems responsibility is to figure out what you want
– though the data to train the AI may be sometimes unrepresentative – leading to a small prossibility of deviation from true beneficiality – probably approximately beneficial AI

Convincing the AI community that AI friendliness is important

Will there be a hard takeoff to superintelligence?

What are the benefits of building String AI?

Center for Human-Compatible AI – UC Berkley
http://humancompatible.ai/

Stuart Jonathan Russell is a computer scientist known for his contributions to artificial intelligence. He is a Professor of Computer Science at the University of California, Berkeley and Adjunct Professor of Neurological Surgery at the University of California, San Francisco.
https://en.wikipedia.org/wiki/Stuart_J._Russell

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Adam Ford
– Science, Technology & the Future

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TABLE OF CONTENT

00:00:00 Introduction
00:01:49 Installing Python 3
00:06:10 Your First Python Program
00:08:11 How Python Code Gets Executed
00:11:24 How Long It Takes To Learn Python
00:13:03 Variables
00:18:21 Receiving Input
00:22:16 Python Cheat Sheet
00:22:46 Type Conversion
00:29:31 Strings
00:37:36 Formatted Strings
00:40:50 String Methods
00:48:33 Arithmetic Operations
00:51:33 Operator Precedence
00:55:04 Math Functions
00:58:17 If Statements
01:06:32 Logical Operators
01:11:25 Comparison Operators
01:16:17 Weight Converter Program
01:20:43 While Loops
01:24:07 Building a Guessing Game
01:30:51 Building the Car Game
01:41:48 For Loops
01:47:46 Nested Loops
01:55:50 Lists
02:01:45 2D Lists
02:05:11 My Complete Python Course
02:06:00 List Methods
02:13:25 Tuples
02:15:34 Unpacking
02:18:21 Dictionaries
02:26:21 Emoji Converter
02:30:31 Functions
02:35:21 Parameters
02:39:24 Keyword Arguments
02:44:45 Return Statement
02:48:55 Creating a Reusable Function
02:53:42 Exceptions
02:59:14 Comments
03:01:46 Classes
03:07:46 Constructors
03:14:41 Inheritance
03:19:33 Modules
03:30:12 Packages
03:36:22 Generating Random Values
03:44:37 Working with Directories
03:50:47 Pypi and Pip
03:55:34 Project 1: Automation with Python
04:10:22 Project 2: Machine Learning with Python
04:58:37 Project 3: Building a Website with Django

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This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient of a function. We then start to build up a set of tools for making calculus easier and faster. Next, we learn how to calculate vectors that point up hill on multidimensional surfaces and even put this into action using an interactive game. We take a look at how we can use calculus to build approximations to functions, as well as helping us to quantify how accurate we should expect those approximations to be. We also spend some time talking about where calculus comes up in the training of neural networks, before finally showing you how it is applied in linear regression models. This course is intended to offer an intuitive understanding of calculus, as well as the language necessary to look concepts up yourselves when you get stuck. Hopefully, without going into too much detail, you’ll still come away with the confidence to dive into some more focused machine learning courses in future.
Who is this class for: This class is for people who would like to learn more about machine learning techniques, but don’t currently have the fundamental mathematics in place to go into much detail. This course will include some exercises that require you to work with code. If you’ve not had much experience with code before DON’T PANIC, we will give you lots of guidance as you go.
Topic Covered:
Fuctions
Definition of a derivative
Differentiation examples & special cases
differentiate some functions
Time saving rules
Product rule
Chain rule
Variables, constants & context
Differentiate with respect to anything
Jacobians – vectors of derivatives
Jacobian applied
The Sandpit
The Sandpit -2
The Hessian
Multivariate chain rule
Neural Networks
Simple neural networks
Power series
Visualising Taylor Series
Power series derivation
Power series details
Multivariable Taylor Series
Linearisation
Multivariate Taylor
Gradient Descent
Gradient descent in a sandpit
Lagrange multipliers
Constrained optimisation
Simple linear regression
Non-linear regression
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This course is created by Imperial College London
If you like this video and course explanation feel free to take the
complete course and get certificate from: https://www.coursera.org/specializations/mathematics-machine-learning

This video is provided here for research and educational purposes in the field of Mathematics. No copyright infringement intended. If you are content owner would like to remove this video from YouTube, Please contact me through email: ict_hanif@yahoo.com
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