Machine Learning Tutorial | Machine Learning Course | Machine Learning Projects 2022 |Simplilearn

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In this video on Machine Learning with Python full course, you will understand the basics of machine learning, essential applications of machine learning, machine learning concepts and understand why mathematics, statistics, and linear algebra are crucial. We'll also learn about regularization, dimensionality reduction, PCA. We will perform a prediction analysis on the recently held US Elections. Finally, you will study the Machine Learning roadmap for 2021?

00:00:00 Machine Learning Basics
00:08:45 Top 10 applications of machine learning
00:13:40 Machine Learning Tutorial Part-1
0:14:26 Why Machine Learning
0:18:33 What is Machine Learning
0:25:15 Types of Machine Learning
0:25:27 Supervised Learning
0:27:47 Reinforcement Learning
0:29:07 Supervised vs Unsupervised Learning
0:39:23 Decision Trees
01:15:10 Machine Learning Tutorial Part-2
01:19:47 K-Means Algorithm
02:10:47 Mathematics for Machine Learning
2:11:15 What is Data?
02:12:07 Quantitative/Categorical Data
02:14:54 Qualitative/Categorical Data
02:15:12 Linear Algebra
02:38:01 Calculus
02:52:21 Statistics
03:05:16 Demo on Statistics
03:22:27 Probability
03:48:09 Demo on Naive Bayes
04:01:00 Linear Regression Analysis
04:20:37 Logistic Regression
04:38:35 Confusion Matrix
04:58:31 Decision Tree in Machine Learning
05:20:30 Random Forest
05:50:29 K Nearest Neighbors
06:16:56 Support Vector Machine
06:35:57 Regularization in ML
07:05:03 PCA
07:35:16 US Election Prediction
08:03:49 Machine Learning roadmap 2021

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➡️ About Post Graduate Program In AI And Machine Learning
This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots.

✅ Key Features
- Post Graduate Program certificate and Alumni Association membership
- Exclusive hackathons and Ask me Anything sessions by IBM
- 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more
- Master Classes delivered by Purdue faculty and IBM experts
- Simplilearn's JobAssist helps you get noticed by top hiring companies
- Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more
- Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools

✅ Skills Covered
- ChatGPT
- Generative AI
- Explainable AI
- Generative Modeling
- Statistics
- Python
- Supervised Learning
- Unsupervised Learning
- NLP
- Neural Networks
- Computer Vision
- And Many More…

  1. Simplilearn
  2. complete machine learning course
  3. complete machine learning project
  4. k- means clustering algorithm
  5. linear regression machine learning
  6. logistic regression machine learning
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Комментарии
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Got a Question on this topic? Let us know in the comment section below 👇 and we'll have our experts answer it for you.
00:00:00 Machine Learning Basics
00:08:45 Top 10 applications of machine learning
00:13:40 Machine Learning Tutorial Part-1
0:14:26 Why Machine Learning
0:18:33 What is Machine Learning
0:25:15 Types of Machine Learning
0:25:27 Supervised Learning
0:27:47 Reinforcement Learning
0:29:07 Supervised vs Unsupervised Learning
0:39:23 Decision Trees
01:15:10 Machine Learning Tutorial Part-2
01:19:47 K-Means Algorithm
02:10:47 Mathematics for Machine Learning
2:11:15 What is Data?
02:12:07 Quantitative/Categorical Data
02:14:54 Qualitative/Categorical Data
02:15:12 Linear Algebra
02:38:01 Calculus
02:52:21 Statistics
03:05:16 Demo on Statistics
03:22:27 Probability
03:48:09 Demo on Naive Bayes
04:01:00 Linear Regression Analysis
04:20:37 Logistic Regression
04:38:35 Confusion Matrix
04:58:31 Decision Tree in Machine Learning
05:20:30 Random Forest
05:50:29 K Nearest Neighbors
06:16:56 Support Vector Machine
06:35:57 Regularization in ML
07:05:03 PCA
07:35:16 US Election Prediction
08:03:49 Machine Learning roadmap 2021

SimplilearnOfficial
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We hope you liked this video and it was useful. The link for the dataset used in the video is provided in the description. Thanks!

SimplilearnOfficial
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simplilearn is the only which replies to comments. SImplilearn is the NUMBERONE youtube channel. BEST Channel

kushalsharma
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The topics which taught here is crisp and clear and most important is the flow that maintained by the all trainers is greatly appreciated. kudos Simplilearn.

chandrashekarr
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I was digging for this course so madly, and my search stops here .
Thanks. Great Work 👍

shuklatanmay
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A. Clustering
B. Classification
C. Anomaly
D. Regression

anngrahdhar
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An important component in building a solid foundation to becoming a data scientist

karelkoekemoer
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I am happy to find this 9 hour complete Python Machine Learning course. Let me give it my full concentration!

omarpasha
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Scenario1 &3 _supervised learning
Scenario2 _ unsupervised learning

zefdklbji
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Very Nice Tutorials, I am new to machine learning and in-fact even python programming in general. I was trying out the example of muffinsvscupakes and decided to plot between sugar vs butter. My plot somehow shows out of scale could you be able to pin point where the error could possibly be, the hyper plane lines are below all the scatter points, looks different somehow. Is there a help section for these tutorials where I could post and get quicker answers. Thank You

ThePlanck
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Can u say on which topics should I have basic knowledge to start with this vedio

praveenavala
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The convert_object pandas function has been deprecated. Anyone who stuck on this, replace is as below

x = x.apply(pd.to_numeric, errors='coerce')

joxa
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At 6:53 - Scenario 1: Supervised Learning, Scenario 2: Reinforced Learning, Scenario 3: Unsupervised Learning.

_SUSWINP
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It is a great ML course. May I request the data set so that I can practice it please? Thank you much :) Your help is really appreciated.

wienhong
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How can i avaoid this error : TypeError: __init__() got an unexpected keyword argument 'categorical_features' ? ( 4:12:36 )
I tried with ColumnTransformer but i'm stuck in this error.

pietrosemenzato
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Excellent tutorial, thanks a lot Simplilearn.
I just want to know why you used y = mx - c instead of y = mx + c in the SVM tutorial part. I really await your response

happyokoduwa
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It Explains in a good way. no channel thought me maths behind svm, regression ..etc. But I'm only 14 years old. My Skills:- Python, Dart, Flutter, Javascript, MySQL Database. I know these. Expertising Python, Flutter etc. I have a question Simplilearn is this a beginner to advanced ML course.

kushalsharma
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Thanks for the video. May I request for the csv files used in the video.

Anointed_Servant
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Can you bring a android development with kotlin course ? ... Would be great 😁

bhupeshpattanaik
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Hi, thank you for the content, I watched the Decision tree section, I have a query over there, why the graph for Sugar and flour looks different when we plot the SVM line through it.
In the function you created, you identified 1 as Cupcakes, and 0 as muffin, how did we get those two values 0, and 1. Could you please explain me those two queries.

rajshekharghosh