Machine Studying Full Course – Study Machine Studying 10 Hours | Machine Learning Tutorial | Edureka
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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 Learning 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 #Learn #Machine #Studying #Hours #Machine #Learning #Tutorial #Edureka [publish_date]
#Machine #Learning #Full #Learn #Machine #Learning #Hours #Machine #Studying #Tutorial #Edureka
Machine Learning Engineer Masters Program (Use Code "YOUTUBE20"): ...
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- Mehr zu learn Eruditeness is the work on of deed new apprehension, noesis, behaviors, profession, belief, attitudes, and preferences.[1] The inability to learn is insane by homo, animals, and some machinery; there is also bear witness for some rather education in certain plants.[2] Some encyclopaedism is present, iatrogenic by a separate event (e.g. being hardened by a hot stove), but much skill and cognition amass from perennial experiences.[3] The changes elicited by eruditeness often last a period, and it is hard to identify nonheritable fabric that seems to be "lost" from that which cannot be retrieved.[4] Human learning starts at birth (it might even start before[5] in terms of an embryo's need for both action with, and immunity within its surroundings within the womb.[6]) and continues until death as a consequence of ongoing interactions betwixt citizenry and their environs. The existence and processes caught up in learning are unnatural in many established fields (including learning science, psychology, experimental psychology, cognitive sciences, and pedagogy), besides as emergent william Claude Dukenfield of cognition (e.g. with a shared kindle in the topic of encyclopedism from device events such as incidents/accidents,[7] or in cooperative education health systems[8]). Explore in such comedian has led to the recognition of varied sorts of learning. For exemplar, education may occur as a outcome of physiological state, or conditioning, conditioning or as a outcome of more intricate activities such as play, seen only in relatively rational animals.[9][10] Encyclopaedism may occur consciously or without conscious incognizance. Encyclopedism that an aversive event can't be avoided or loose may result in a condition named knowing helplessness.[11] There is evidence for human behavioural learning prenatally, in which physiological state has been observed as early as 32 weeks into gestation, indicating that the essential nervous organization is insufficiently matured and primed for encyclopedism and mental faculty to occur very early on in development.[12] Play has been approached by different theorists as a form of learning. Children inquiry 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 substance of their environment through action acquisition games. For Vygotsky, even so, play is the first form of education terminology and human action, and the stage where a child begins to read rules and symbols.[13] This has led to a view that eruditeness in organisms is e'er accompanying to semiosis,[14] and often joint with objective systems/activity.
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
Thank you, I'm planning to take informatics as my master degree, this is really beneficial🌈🙏
Can I please get the datasets and codes used in this tutorial
This video is very useful… Can I get the codes….
Can I get data set and code used in video?
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
Can I get the datasets and codes used in this video?
Thanks Edureka! This is the best tutorial for machine learning!!! May I have the PPT and code?
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 😊 🙏🌸
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?
Amazing tutorial for Machine Learning. Can I get the PPT?
Thanks a lot for this course…Can you please share the source code and dataset used in this video.
this is best platform edureka
please shears notebooks & code
Amazing lecture
Detailed explanation. Appreciate you very much for this video. Can you provide the datasets and the codes as well, it would be really helpful.
Do we need to have basic understanding of MATPLOTLIB,PANDAS,NUMPY for ML Engineer ?
nice sir
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 ?
@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.
This compete tutorial is awesome.. .Can u plzzz provide me the datasets??
Great tutorial Team Edureka, very good explanation. Could you please share the datasets and code for this course? That'd be great help.
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
Thank you Edureka for this amazing video. Could you please share the code too.
how to get data set