We are in the middle of a major shift in computing that’s transitioning us from a mobile-first world into one that’s AI-first. AI will touch every industry and transform the products and services we use daily. Breakthroughs in machine learning have enabled dramatic improvements in the quality of Google Translate, made your photos easier to organize with Google Photos, and enabled improvements in Search, Maps, YouTube, and more. We’re also sharing the underlying technology with developers and researchers via open-source software such as TensorFlow, academic publications, and a full suite of Cloud machine learning services. Join this session to hear some of Alphabet’s top machine learning experts discuss their cutting-edge research and the opportunities they see ahead.

See all the talks from Google I/O ’17 here: https://goo.gl/D0D4VE
Watch more Android talks at I/O ’17 here: https://goo.gl/c0LWYl
Watch more Chrome talks at I/O ’17 here: https://goo.gl/Q1bFGY
Watch more Firebase talks at I/O ’17 here: https://goo.gl/pmO4Dr

Subscribe to the Google Developers channel: http://goo.gl/mQyv5L

#io17 #GoogleIO #GoogleIO2017

ML Systems Workshop @ NIPS 2017

Contributed Talk 3: NSML: A Machine Learning Platform That Enables You to Focus on Your Models by Nako Sung. This Video is by Jung-Woo Ha.

The accelerated pace of success in machine learning applications reflects significant improvements in specialized hardware, algorithms, and access to data. Corporations now know that they need to deploy machine learning rapidly, but they are unsure about best practices. Public sentiment has shifted from skepticism to fears of runaway AI, as well as employment, privacy, and ethical issues. The future success of machine learning depends on our ability to capture competitive advantages and manage downside risks effectively.

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About Singularity University:
Singularity University is a benefit corporation headquartered at NASA’s research campus in Silicon Valley. We provide educational programs, innovative partnerships and a startup accelerator to help individuals, businesses, institutions, investors, NGOs and governments understand cutting-edge technologies, and how to utilize these technologies to positively impact billions of people.

Singularity University

In this episode of Eyes on Enterprise, Stephanie Wong invites Yufueng Guo, Developer Advocate for machine learning, to talk about how enterprises can incorporate ML into their environments, build workflows, and apply them to real world scenarios. Specifically, they discuss how to frame ML as descriptive, predictive, or prescriptive problems, and when to use tooling like Tensorflow, Keras, Scikit Learn, and Pytorch.

Follow Stephanie Wong on Twitter → @swongful

Time Markers:
0:00 Intro
0:34 Trends in ML
1:32 AI vs. ML
2:57 Building an ML workflow
10:31 ML tools and developer experience
11:33 Approaching ML by problem type
13:45 Processing in the cloud vs. on-prem
17:51 Google Cloud tools
20:12 Real world use cases
21:57 Takeaways

The 7 Steps of Machine Learning → https://goo.gle/3aUPQd5
AI Platform → https://goo.gle/2UyIPb1
The fight against illegal deforestation with TensorFlow → https://goo.gle/38I0gLk
AI Experiments → https://goo.gle/3aBsY1Y

For more content like this, subscribe to the GCP Channel → https://goo.gle/34tknuO
Watch more episodes of Eyes on Enterprise → https://goo.gle/2Uipf3X

Product: Cloud Machine Learning Engine; fullname: Stephanie Wong, Yufeng Guo;


#Freelancing #FreelanceDataScience #Upwork

Want to get started with freelancing in machine learning but don’t know where to start?

In my new series I’ll break it all down for you. What platform to use, how to write your profile, how to screen clients, how to apply to jobs, and how to deliver.

Stay tuned for more episodes.

Learn how to turn deep reinforcement learning papers into code:

Deep Q Learning:

Actor Critic Methods:

Reinforcement Learning Fundamentals

Come hang out on Discord here:

Website: https://www.neuralnet.ai
Github: https://github.com/philtabor
Twitter: https://twitter.com/MLWithPhil

Using past outcomes as predictions from our pattern recognition.

Welcome to the Machine Learning for Forex and Stock analysis and algorithmic trading tutorial series. In this series, you will be taught how to apply machine learning and pattern recognition principles to the field of stocks and forex.

This is especially useful for people interested in quantitative analysis and algo or high frequency trading. Even if you are not, the series will still be of great use to anyone interested in learning about machine learning and automatic pattern recognition, through a hands-on tutorial series.


Using previous pattern outcomes to help us begin to predict future outcomes.

Welcome to the Machine Learning for Forex and Stock analysis and automated trading tutorial series. In this series, you will be taught how to apply machine learning and pattern recognition principles to the field of stocks and forex.

This is especially useful for people interested in quantitative analysis and algo or high frequency trading. Even if you are not, the series will still be of great use to anyone interested in learning about machine learning and automatic pattern recognition, through a hands-on tutorial series.


The battle for the 2020 US Presidential election has begun. The Republican nominee Donald Trump and the Democratic nominee Joe Biden are fighting for a place in the White House. In this video, we will analyze the US elections using Twitter Sentiment Analysis in Python. You will understand how to extract tweets, store it in a CSV file, examine the tweets’ polarity, and visualize the results. Finally, you will build a word cloud based on the tweets for Trump and Biden.

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⏩ Check out the Machine Learning tutorial videos: https://bit.ly/3fFR4f4

#USElectionPrediction2020 #USElectionPredictionLatest #ElectionPredictionUsingMachineLearning #MachineLearningCourse #ElectionPredictionUsingDataScience #Simplilearn

About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.

What skills will you learn from this Machine Learning course?

By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems

👉Learn more at: https://bit.ly/3fouyY0

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🔥NIT Warangal Post Graduate Program in AI & Machine Learning with Edureka: https://www.edureka.co/nitw-ai-ml-pgp
This Edureka “Stock Prediction using Machine Learning” takes you through the basic process of predicting the trends of stock prices using machine learning architecture of LSTM while also making use of prominent Python Libraries such as Tensorflow, Keras, etc. Topics covered in the tutorial are as follow:
01:30 Introduction to Stock Prediction
03:00 LSTM Architecture
05:35 Stock Prediction Model
26:35 Conclusion

🔸Dataset & code: https://bit.ly/2x6BPe0

🔹Check out our machine learning Playlist: https://bit.ly/3byztDp
🔹And our Blog series: https://bit.ly/34YXH7n
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#edureka #machinelearningEdureka #StockPredictionUsingMachineLearning #machinelearningalgorithms #pythonMachineLearning #machineLearningTraining
Why Machine Learning & AI?

Because of the increasing need for intelligent and accurate decision making, there is an exponential growth in the adoption of AI and ML technologies. Hence these are poised to remain the most important technologies in the years to come.
PG Program in Machine Learning & AI

1. Ranked 4th among NITs by NIRF
2. Ranked among Top 50 Institutes in India
3. Designated as Institute of National Importance
Program Features

1. Mentorship from NITW faculty
2. Placement Assistance
3. Alumni Status
4. Industry Networking
Industry Projects

1. Building a Conversational ChatBot
2. Predictive Model for Auto Insurance
3. E-commerce Website – Sales Prediction
Mentors & Instructors

Dr. RBV Subramaanyam
Professor NITW

Dr. DVLN Somayajulu
Professor NITW

Dr. P. Radha Krishna
Professor NITW

Dr. V. Ravindranath
Professor JNTU Kakinada
Is this program for me?

If you’re passionate about AI & ML and want to pursue a career in this field, this program is for you. Whether you’re a fresher or a professional, this program is designed to equip you with the skills you need to rise to the top in a career in AI & ML.

Is there any eligibility criteria for this program?

A potential candidate must have one of the following prerequisites: Degrees like BCA, MCA, and B.Tech or Programming experience Should have studied PCM in 10+2

Will I get any certificate at the end of the course?

Yes, you will receive a Post-Graduate industry-recognized certificate from E & ICT Academy, NIT Warangal upon successful completion of the course.
For more information, Please write back to us at sales@edureka.in or call us at IND: +91-9606058418 / US: 18338555775 (toll-free).

A rundown, based on my book AI in Practice, on the best examples of how trailblazing companies are using AI in practice.

If you would like more information on this topic, please feel free to visit my website and sign up for content updates! I write articles every week on various different topics such as Big Data, Artificial Intelligence & Machine Learning.

Visit the Artificial Intelligence & Machine Learning topic page here: https://bernardmarr.com/default.asp?contentID=1314

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Large scale machine learning is playing an increasingly important role in improving the quality and monetization of Internet properties. A small number of techniques, such as regression, have proven to be widely applicable across Internet properties and applications. Sibyl is a research project that implements these primitives at scale and is widely used within Google. In this talk I will outline Sibyl and the requirements that it places on Google’s computing infrastructure.

Tushar Chandra is a Principal Engineer at Google Research and a co-lead for the Sibyl project. He received his Ph.D. in Computer Science from Cornell University in 1993, worked at IBM Research thereafter until he joined Google in 2004. He has worked on a number of distributed systems projects: Reliable Scalable Cluster Technology, Gryphon, and Oceano at IBM and Bigtable and a Paxos-based platform for fault-tolerance at Google. He was a joint winner of the 2010 Edsger W. Dijkstra Prize in Distributed Computing.

Introduced by Georgia Tech professor and DSN 2014 General Chair, Dr. Doug Blough, this keynote was presented at the IEEE DSN (Dependable Systems and Networks) conference at the Georgia Tech Hotel & Conference Center in Atlanta, GA on Wednesday, June 25th 2014.

See: http://2014.dsn.org/keynote.shtml

(Recorded and edited by: Conner Reinhardt)

This post originally appeared on SIG’s SIGnals blog.

What is machine learning and why is it being talked about now more than ever? We asked these questions and more to our intelligent systems expert, Jeff Erhardt.




Visit GE Digital’s Website: https://www.ge.com/digital/
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Find GE Digital on LinkedIn: https://www.linkedin.com/company/2681277

How can we use machine learning and predictive analytics in the classroom?

Simon Harper from Killara High School, NSW shares the journey his school has undertaken in AI to set students up for success in the future.

Komatsu intelligent Machine Control technology provides solutions to help customers become more productive. Its a sure formula for an unmatched efficiency improvement. More Productive Today and in the Future.

Intelligent Farming Agriculture Technology,Smart Farming – Amazing Homemade Invention Machine


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Thanks for Watching

Incredibly intelligent machine. – This factory machine will blow your mind.

Our J.P. Morgan office is a place where you can solve real-world problems using state of the art machine learning methods and cutting-edge AI research. Learn more about opportunities to develop your career and advance tech at: https://www.jpmorgan.com/technology.
SUBSCRIBE: http://jpm.com/x/i/NFPWfK0

About J.P. Morgan:
J.P. Morgan is a leader in financial services, offering solutions to clients in more than 100 countries with one of the most comprehensive global product platforms available. We have been helping our clients to do business and manage their wealth for more than 200 years. Our business has been built upon our core principle of putting our clients’ interests first.

Connect with J.P. Morgan Online:
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#jpmorgan #AIResearch #MachineLearning
Life as an AI Researcher & Machine Learning Engineer | Technology | J.P. Morgan

Komatsu demonstrating their intelligent machine control system, two Komatsu PC210LCi-10 excavators, side by side working with and with out machine control, at the Bauma 2016 expo in Germany.

Come and follow us on Facebook…

Extreme Fast Homemade Firewood Machinery Wood Cutting And Splitting Firewood intelligent machine engine
#woodcuttingmachine #wood #intelligenttechnology

This video shows how we can use Break and Continue statements in a loop to effectively handle exceptions.

*About us*

Applied AI course (AAIC Technologies Pvt. Ltd.) is an Ed-Tech company based out in Hyderabad offering on-line training in Machine Learning and Artificial intelligence. Applied AI course through its unparalleled curriculum aims to bridge the gap between industry requirements and skill set of aspiring candidates by churning out highly skilled machine learning professionals who are well prepared to tackle real world business problems.

*Key highlights of Applied AI course*

1. Job guarantee or money back guarantee
2. Query resolution inside 24 hours
3. Personalized learning path for every course participant
4. 30 Practical Assignments
5. 15 end-to-end case studies based on real world problems across various industries
6. Mentor-ship for portfolio development, resume and interview preparation, and career counseling for every course participant

For More information Please visit https://www.appliedaicourse.com/

For any queries you can either drop a mail to team@appliedaicourse.com or call us at +91 8106-920-029 or +91 6301-939-583

Facebook: https://www.facebook.com/appliedaicou…
Soundcloud: https://soundcloud.com/applied-ai-course
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kiloskar 5 Hp pumping Machine #Machine #Pumps #engine #machines Field marshal 5 HP New Generator Machines Starting without handle Testing. pumps machines

Gary Marcus was a speaker at EmTech Digital 2016 in San Francisco.

To find out more about MIT Technology Review’s events, please visit: http://events.technologyreview.com/

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The first edition of Microsoft India’s OpenHack was organized by CSE India in Bengaluru from 10th – 12th April in partnership with the global CSE team, G&E group, IDC team, and India GSMO team.
100+ Data Scientists and Developers from 45+ enterprise customers, partners, and startups participated in this 3-day coding event. All participants hacked alongside their respective teams to solve real world Machine Learning problems designed in the form of progressive challenges. The challenges were crafted methodically to enable customers to learn basics of Machine Learning and Image Processing using Deep Learning. 38 Microsoft engineers from over 7 countries coached their respective teams on their journey to Deep Learning

The #1 question I get is how to get started with Machine Learning, so join me today as we talk about this! 😀

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Favorite Resources
Activation Functions – https://en.wikipedia.org/wiki/Activation_function
Luis Serrano’s Neural Network Series (REALLY GOOD) – https://www.youtube.com/watch?v=UNmqTiOnRfg&list=PLs8w1Cdi-zvavXlPXEAsWIh4Cgh83pZPO
Giant_Neural_Network’s Neural Network Series (REALLY GOOD) – https://www.youtube.com/watch?v=ZzWaow1Rvho&list=PLxt59R_fWVzT9bDxA76AHm3ig0Gg9S3So
Macheads101’s Neural Network series – https://www.youtube.com/watch?v=OypPjvm4kiA&list=PL3XtGMELeTxytyFKrUu87EudAJiO4XK0u
3Blue1Brown’s Neural Network Series – https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi
Hugo’s Neural Network Series – https://www.youtube.com/watch?v=SGZ6BttHMPw&list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH
James Mccaffrey Talk (A BIT OUTDATED INFORMATION BUT STILL A FUN ONE)- https://www.youtube.com/watch?v=-zT1Zi_ukSk

Important Maths
Vectors (explained with Linear Algebra): https://www.khanacademy.org/math/linear-algebra/vectors-and-spaces
Vectors (explained with PreCal): https://www.khanacademy.org/math/precalculus/vectors-precalc
Matrices: https://www.khanacademy.org/math/precalculus/precalc-matrices
Sequences: https://www.khanacademy.org/math/precalculus/seq-induction
Derivative Rules: https://www.khanacademy.org/math/ap-calculus-bc/bc-derivative-rules

Maths that will help
Algebra Functions: https://www.khanacademy.org/math/algebra/algebra-functions
Quadratics: https://www.khanacademy.org/math/algebra/quadratics
Irrational: https://www.khanacademy.org/math/algebra/rational-and-irrational-numbers
Analyzing Categorical Data: https://www.khanacademy.org/math/statistics-probability/analyzing-categorical-data


#AI #MachineLearning #softwareengineering

We’ve developed a new framework for reinforcement learning, a subset of machine learning. This video shows the framework applied to an autonomous RC car that learns to drift around a truck.

Because of the Youtube Live Streaming platform outage on Wednesday, this speaker was interrupted during the streaming session. The missing portion appears in this video.

(approx. 32 min. of additional content)

The ACM Recommender System conference (RecSys) is the premier international forum for the presentation of new research results, systems and techniques in the broad field of recommender systems. Recommendation is a particular form of information filtering, that exploits past behaviors and user similarities to generate a list of information items that is personally tailored to an end-user’s preferences. As RecSys brings together the main international research groups working on recommender systems, along with many of the world’s leading e-commerce companies, it has become the most important annual conference for the presentation and discussion of recommender system research.

RecSys 2014, the eighth conference in this series, was held in Silicon Valley. It brought together researchers and practitioners from academia and industry to present their latest results and identify new trends and challenges in providing recommendation components in a range of innovative application contexts. In addition to the main technical track, RecSys 2014 program featured keynote and invited talks, tutorials covering state-of-the-art in this domain, a workshop program, an industrial track and a doctoral symposium.

Published papers went through a rigorous full peer review process. The conference proceedings, which are available both on a USB drive and via the ACM Digital Library, are expected to be widely read and cited. ACM RecSys 2014 took place at the Crowne Plaza hotel in Foster City, Silicon Valley, California from Oct 6-10, 2014.

To accommodate the numerous researchers who could not register for this year’s technical conference and to possibly mitigate local traffic, the Tuesday, Wednesday and Thursday program was streamed real-time on the official ACM RecSys YouTube Channel.

Video services by: Rosa Media Productions http://rosamediaproductions.com/

Manish speaks on the delicate balance needed for the relationship between Man Vs AI Mr. Manish Goyal: Head, Strategy and Sales, Xtage Labs, New Delhi. He is a certified digital transformation consultant and a trained AI expert from MIT, Boston with 15 years of experience in leading and developing businesses and advising enterprises. As Head of Strategy & Sales for an AI/ML solutions company Xtage Labs, he is responsible to drive business across borders and set the direction of company’s growth globally. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx

Some machine learning proponents claim you only have to provide data to get value. However, reality is a bit more complex. On the way to active analytics for business, we have to answer two big questions: What must happen to data before running machine learning algorithms, and how should machine learning output be used to generate actual business value?

Jean-François Puget demonstrates the vital role of human context in answering those questions. You’ll discover why human context should be embraced as a guide to building better, smarter systems that people will use, trust, and love.

This keynote is sponsored by IBM.

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Here is a quick an simple explanation of what machine learning is. To summarize, machines learn from lots and lots of data just as humans learn from past experiences.

UPDATES: I’ve developed a Product Management Course for AI & Data Science for those interested in the industry or wanting to get into Product Management. Here’s the link! https://www.udemy.com/course/the-product-management-for-data-science-ai-course/?referralCode=DE25D5190902F792E9A1

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And here is the article referenced about Amazon using machine learning to sort through resumes: https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G

This video will discuss on the topic *How to Make Money in 2020 with Artificial Intelligence and Machine Learning*

Video contains about the 9 startup ideas based on AI and ML which can be started by any individual who is learning and working on Machine Learning and Artificial Intelligence.

video describes about the different industry where you can start your journey as a entrepreneur in 2020.

Checkout the whole video to get more information.

Visit our website for more Machine Learning and Artificial Intelligence blogs

01:08 First Idea where you can use Artificial Intelligence and Machine Learning.
02:04 Second Idea to make money in AI and ML Field.
03:05 Third Idea for ML and AI.
03:52 Fourth Idea of Startup in AI and ML.
04:21 Fifth Startup Idea in the field of AI and ML.
05:08 Sixth Amazing Startup Idea.
06:05 Seventh Idea
06:50 Eighth Idea for ML and AI.
07:20 Ninth and The last Idea to start your journey in AI and ML

For the rest of all the technology check out the video

Checkout Best Programming Language of 2020

Check why you should learn Data Science

Check how you can Learn, Earn and do Competition in Data Science

Join Us to Telegram for Free Placement and Coding Challenge and Machine Learning Resources ~ @Code Wrestling


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sound credits

#startupideas #artificialintelligence #money2020

Ever since Morse opened developer Tania Finlayson’s world, she’s been working to make it accessible for everyone.

Interested in learning Morse code or using the keyboard Tania developed with the Gboard team? Visit https://g.co/morse

🔥 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
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