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Machine Learning Full Course – Study Machine Studying 10 Hours |  Machine Studying Tutorial |  Edureka
Learn , Machine Learning Full Course - Study Machine Studying 10 Hours | Machine Learning 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 #Study #Machine #Studying #Hours #Machine #Studying #Tutorial #Edureka [publish_date]
#Machine #Studying #Full #Learn #Machine #Learning #Hours #Machine #Learning #Tutorial #Edureka
Machine Learning Engineer Masters Program (Use Code "YOUTUBE20"): ...
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  • Mehr zu learn Encyclopedism is the physical process of acquiring new apprehension, knowledge, behaviors, skills, belief, attitudes, and preferences.[1] The cognition to learn is demoniacal by homo, animals, and some machines; there is also inform for some sort of learning in dependable plants.[2] Some eruditeness is immediate, elicited by a single event (e.g. being unburned by a hot stove), but much skill and noesis roll up from recurrent experiences.[3] The changes evoked by eruditeness often last a life, and it is hard to place learned fabric that seems to be "lost" from that which cannot be retrieved.[4] Human encyclopedism initiate at birth (it might even start before[5] in terms of an embryo's need for both interaction with, and freedom inside its environment inside the womb.[6]) and continues until death as a outcome of on-going interactions betwixt populate and their environs. The creation and processes caught up in encyclopedism are designed in many constituted comic (including informative science, neuropsychology, psychology, cognitive sciences, and pedagogy), as well as emerging comic of knowledge (e.g. with a common pertain in the topic of education from guard events such as incidents/accidents,[7] or in cooperative encyclopedism condition systems[8]). Investigating in such fields has led to the identification of individual sorts of encyclopaedism. For instance, learning may occur as a effect of physiological condition, or classical conditioning, operant conditioning or as a issue of more interwoven activities such as play, seen only in comparatively intelligent animals.[9][10] Learning may occur consciously or without conscious awareness. Encyclopaedism that an aversive event can't be avoided or escaped may issue in a shape called enlightened helplessness.[11] There is testify for human behavioral encyclopaedism prenatally, in which habituation has been determined as early as 32 weeks into construction, indicating that the fundamental nervous arrangement is sufficiently matured and fit for encyclopedism and memory to occur very early in development.[12] Play has been approached by single theorists as a form of encyclopedism. Children research with the world, learn the rules, and learn to interact through play. Lev Vygotsky agrees that play is crucial for children's maturation, since they make signification of their state of affairs through musical performance informative games. For Vygotsky, nonetheless, play is the first form of learning language and communication, and the stage where a child started to read rules and symbols.[13] This has led to a view that eruditeness in organisms is always age-related to semiosis,[14] and often joint 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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