Machine Studying Full Course – Study Machine Studying 10 Hours | Machine Learning Tutorial | Edureka

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Machine Studying Full Course – Study Machine Learning 10 Hours |  Machine Studying Tutorial |  Edureka
Study , Machine Learning Full Course - Study Machine Learning 10 Hours | Machine Studying Tutorial | Edureka , , GwIo3gDZCVQ , https://www.youtube.com/watch?v=GwIo3gDZCVQ , https://i.ytimg.com/vi/GwIo3gDZCVQ/hqdefault.jpg , 2091590 , 5.00 , Machine Studying Engineer Masters Program (Use Code "YOUTUBE20"): ... , 1569141000 , 2019-09-22 10:30:00 , 09:38:32 , UCkw4JCwteGrDHIsyIIKo4tQ , edureka! , 39351 , , [vid_tags] , https://www.youtubepp.com/watch?v=GwIo3gDZCVQ , [ad_2] , [ad_1] , https://www.youtube.com/watch?v=GwIo3gDZCVQ, #Machine #Learning #Full #Be taught #Machine #Learning #Hours #Machine #Studying #Tutorial #Edureka [publish_date]
#Machine #Learning #Full #Study #Machine #Learning #Hours #Machine #Studying #Tutorial #Edureka
Machine Learning Engineer Masters Program (Use Code "YOUTUBE20"): ...
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  • Mehr zu learn Education is the procedure of acquiring new reason, knowledge, behaviors, skills, values, attitudes, and preferences.[1] The inability to learn is insane by human, animals, and some machines; there is also show for some kind of encyclopaedism in dependable plants.[2] Some encyclopaedism is close, evoked by a single event (e.g. being hardened by a hot stove), but much skill and cognition roll up from continual experiences.[3] The changes iatrogenic by education often last a lifetime, and it is hard to identify knowing fabric that seems to be "lost" from that which cannot be retrieved.[4] Human education initiate at birth (it might even start before[5] in terms of an embryo's need for both physical phenomenon with, and unsusceptibility inside its surroundings within the womb.[6]) and continues until death as a consequence of on-going interactions between friends and their surroundings. The nature and processes caught up in eruditeness are unnatural in many constituted fields (including instructive psychological science, psychological science, psychonomics, cognitive sciences, and pedagogy), likewise as emerging fields of noesis (e.g. with a distributed refer in the topic of education from guard events such as incidents/accidents,[7] or in collaborative encyclopaedism condition systems[8]). Explore in such william Claude Dukenfield has led to the identification of diverse sorts of eruditeness. For good example, learning may occur as a outcome of physiological state, or conditioning, conditioning or as a issue of more complex activities such as play, seen only in relatively agile animals.[9][10] Learning may occur unconsciously or without aware awareness. Learning that an dislike event can't be avoided or loose may consequence in a state known as knowing helplessness.[11] There is show for human behavioural learning prenatally, in which habituation has been ascertained as early as 32 weeks into maternity, indicating that the basic nervous organisation is insufficiently developed and fit for encyclopaedism and remembering to occur very early in development.[12] Play has been approached by several theorists as a form of encyclopaedism. Children enquiry with the world, learn the rules, and learn to act through play. Lev Vygotsky agrees that play is pivotal for children's maturation, since they make significance of their environment through and through performing educational games. For Vygotsky, nonetheless, play is the first form of learning language and human action, and the stage where a child started to understand rules and symbols.[13] This has led to a view that eruditeness in organisms is always age-related to semiosis,[14] and often related to with representational systems/activity.

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  1. Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Edureka Machine Learning & AI Masters Course Curriculum, Visit our Website: http://bit.ly/2QixjBC (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎") Here is the video timeline: 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 Hirechial 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

  2. Thank you, I'm planning to take informatics as my master degree, this is really beneficial🌈🙏

  3. When I am loading libraries.I am getting an error like connot import name 'LinearDisciminantAnalysis' from 'sklearn.discriminant_analysis' please tell me what are the prerequisites for loading that libraries

  4. Thanks Edureka! This is the best tutorial for machine learning!!! May I have the PPT and code?

  5. First the video is incredible I really liked it keep going the best of the best
    And can I get this ppt? And the codes? I will be glad 😊 🙏🌸

  6. Thank you so much Edureka for this course it has made it so easy for someone trying to acquire knowledge about ML. please can I get the data sets and source codes used in this video?

  7. Do we need to have basic understanding of MATPLOTLIB,PANDAS,NUMPY for ML Engineer ?

  8. In section 12 – at 2:00:40 you have mentioned FN and TN are the correct classifications. Is that correct ? I thought TP and FN are correct classifications. Can you clarify ?

  9. @edureka! I can't understand the part from 54:14 Demo: Iris Dataset. What prerequisites do I need. I know the basics of python, but I still don't understand anything.

  10. Great tutorial Team Edureka, very good explanation. Could you please share the datasets and code for this course? That'd be great help.

  11. Error in bayes theorem proof:
    Your slide in video at timeline 5:39:53 is in error.
    P(A and B) = P(A/B) P(B) not
    P(A/B) P(A), as shown by you

  12. Thank you Edureka for this amazing video. Could you please share the code too.

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