Machine Studying Full Course – Be taught Machine Learning 10 Hours | Machine Studying Tutorial | Edureka
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Learn , Machine Studying Full Course - Be taught 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 #Studying #Full #Learn #Machine #Studying #Hours #Machine #Studying #Tutorial #Edureka [publish_date]
#Machine #Learning #Full #Study #Machine #Studying #Hours #Machine #Learning #Tutorial #Edureka
Machine Learning Engineer Masters Program (Use Code "YOUTUBE20"): ...
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- Mehr zu learn Education is the procedure of getting new understanding, noesis, behaviors, profession, values, attitudes, and preferences.[1] The power to learn is demoniacal by humanity, animals, and some machinery; there is also evidence for some sort of education in convinced plants.[2] Some eruditeness is present, evoked by a respective event (e.g. being burned by a hot stove), but much skill and knowledge roll up from repeated experiences.[3] The changes elicited by learning often last a lifespan, and it is hard to distinguish nonheritable matter that seems to be "lost" from that which cannot be retrieved.[4] Human encyclopaedism launch at birth (it might even start before[5] in terms of an embryo's need for both interaction with, and freedom inside its surroundings within the womb.[6]) and continues until death as a outcome of ongoing interactions 'tween friends and their environs. The quality and processes caught up in encyclopedism are unstudied in many constituted fields (including acquisition psychological science, psychological science, experimental psychology, psychological feature sciences, and pedagogy), besides as emerging w. C. Fields of cognition (e.g. with a distributed fire in the topic of encyclopedism from device events such as incidents/accidents,[7] or in collaborative encyclopaedism wellbeing systems[8]). Explore in such comedian has led to the identity of varied sorts of education. For case, eruditeness may occur as a effect of dependency, or conditioning, operant conditioning or as a outcome of more intricate activities such as play, seen only in relatively agile animals.[9][10] Eruditeness may occur consciously or without cognizant awareness. Encyclopaedism that an dislike event can't be avoided or on the loose may outcome in a shape called enlightened helplessness.[11] There is show for human activity education prenatally, in which addiction has been observed as early as 32 weeks into maternity, indicating that the important anxious organisation is sufficiently developed and set for education and memory to occur very early in development.[12] Play has been approached by respective theorists as a form of encyclopedism. Children experiment with the world, learn the rules, and learn to interact through play. Lev Vygotsky agrees that play is pivotal for children's evolution, since they make meaning of their environs through acting acquisition games. For Vygotsky, yet, play is the first form of education language and human action, and the stage where a child begins to interpret rules and symbols.[13] This has led to a view that learning in organisms is always accompanying to semiosis,[14] and often connected with representational 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