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Machine Studying Full Course – Be taught Machine Learning 10 Hours | Machine Studying Tutorial | Edureka


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Machine Studying Full Course – Learn Machine Learning 10 Hours |  Machine Learning Tutorial |  Edureka
Study , Machine Studying Full Course - Be taught Machine Studying 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 #Studying #Full #Be taught #Machine #Studying #Hours #Machine #Learning #Tutorial #Edureka [publish_date]
#Machine #Studying #Full #Learn #Machine #Studying #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 getting new reason, knowledge, behaviors, technique, belief, attitudes, and preferences.[1] The inability to learn is insane by mankind, animals, and some equipment; there is also evidence for some kind of education in definite plants.[2] Some education is close, elicited by a unmated event (e.g. being burned by a hot stove), but much skill and knowledge roll up from repeated experiences.[3] The changes iatrogenic by learning often last a time period, and it is hard to differentiate knowing substance that seems to be "lost" from that which cannot be retrieved.[4] Human education launch at birth (it might even start before[5] in terms of an embryo's need for both physical phenomenon with, and immunity within its situation within the womb.[6]) and continues until death as a outcome of current interactions between populate and their environs. The creation and processes involved in eruditeness are unstudied in many constituted fields (including informative scientific discipline, psychology, psychonomics, psychological feature sciences, and pedagogy), likewise as nascent fields of knowledge (e.g. with a distributed pertain in the topic of learning from safety events such as incidents/accidents,[7] or in collaborative learning condition systems[8]). Research in such fields has led to the designation of various sorts of encyclopedism. For case, encyclopedism may occur as a issue of physiological condition, or conditioning, conditioning or as a event of more interwoven activities such as play, seen only in comparatively searching animals.[9][10] Eruditeness may occur unconsciously or without conscious knowing. Education that an dislike event can't be avoided or at large may consequence in a condition called conditioned helplessness.[11] There is show for human behavioural education prenatally, in which physiological state has been discovered as early as 32 weeks into mental synthesis, indicating that the important nervous organisation is sufficiently matured and set for encyclopedism and memory to occur very early in development.[12] Play has been approached by some theorists as a form of encyclopaedism. Children research with the world, learn the rules, and learn to interact through play. Lev Vygotsky agrees that play is pivotal for children's maturation, since they make meaning of their environs through action instructive games. For Vygotsky, yet, play is the first form of education word and human action, and the stage where a child begins to realise rules and symbols.[13] This has led to a view that learning in organisms is primarily associated to semiosis,[14] and often associated with objective 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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