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Machine Learning Full Course for Beginners | Machine Learning Tutorial | Machine Learning Course

Hello and welcome to the Machine Learning Full Course for Beginners using python. In this video, you will learn from basics to advanced machine learning concepts from Great Learningβs top faculties, including professor Mukesh Rao, Bharani Akella & many other leading industry experts. If you are an enthusiast who wants to start with machine learning from scratch, this machine learning beginner video is the best to start with.

#machinelearningfullcourse #machinelearning #machinelearningbasics

Agenda:

β’ Python for Machine Learning

β’ Role of Statistics in Machine Learning

β’ Introduction to Machine Learning and its types

β’ How does a Machine learning model learn?

β’ Supervised and Unsupervised learning algorithms

β’ Principal component analysis for dimensionality reduction

β’ Application of Machine Learning

Topics Covered:

00:01:09 β What Is Machine learning? (Introduction to Machine Learning)

00:03:00 β Why Machine Learning?

00:04:22 β Road Map to Machine Learning

00:01:09 β How to Use Kaggle (www.kaggle.com)

Machine Learning with Python (Python Libraries for Machine Learning)

00:11:25 – NumPy Python Tutorial (How to Create NumPy Array)

00:14:58 – How to Initialize NumPy Array

00:22:19 – How to check the shape of NumPy arrays

00:24:42 – How to Join NumPy Arrays

00:28:15 – NumPy Intersection & Difference

00:31:50 – NumPy Array Mathematics

00:39:15 – NumPy Matrix

00:42:28 – How to Transpose NumPy Matrix

00:43:21 – NumPy Matrix Multiplication

00:45:45 – NumPy Save & Load

00:47:44 – Python Pandas Tutorial

00:48:09 – Pandas Series Object

00:58:44 – Pandas Dataframe

01:12:00 – Matplotlib Python Tutorial

01:12:12 – Line plot

01:26:32 – Bar plot

01:32:37 – Scatter Plot

01:40:35 – Histogram

01:46:16 – Box Plot

01:51:03 – Violin Plot

01:51:57 – Pie Chart

01:56:39 – DoughNut Chart

01:59:04 – SeaBorn Line Plot

02:07:27 – SeaBorn Bar Plot

02:15:15 – SeaBorn ScatterPlot

02:20:25 – SeaBorn Histogram/Distplot

02:26:52 – SeaBorn JointPlot

02:30:23 – SeaBorn BoxPlot

02:38:59 β Role of Mathematics in Data Science

02:40:23 β What is data?

02:42:34 β What is Information?

02:43:21 β What is Statistics?

02:43:58 β What is Population?

02:46:48 β What is Sample?

02:47:33 β What are Parameters?

02:47:55 β Measures of Central Tendency

02:51:10 β Understanding Empirical Rule

02:53:16 β What is Mean, median, and mode?

02:57:04 β Measures of Spread (Understanding Range, Inter Quartile Range & Box-plot)

03:12:56 β Types of Machine Learning (Supervised, Unsupervised & Reinforcement Learning)

03:27:43 β How does a Machine Learning Model Learn?

03:35:31 β Supervised Machine Learning (Mukesh Rao)

04:34:51 β Python for Machine Learning

04:46:40 β Linear Regression Algorithm (Hands-on)

05:21:13 β What is Logistic Regression

05:29:39 β Linear Regression vs Logistic Regression

05:40:15 β NaΓ―ve Bayes Algorithm

05:49:32 β Diabetes Prediction using NaΓ―ve Bayes

06:15:18 β Decision Tree and Random Forest Algorithm

07:55:01 β Introduction to Support Vector Machines (SVMs)

08:07:08 β Kernel Functions

08:11:56 β Advantages & Disadvantages of SVMs

08:31:37 β K-NN Algorithm (K-Nearest Neighbour Algorithm)

08:40:13 β Introduction to Unsupervised Learning – Clustering

08:48:35 β Introduction to Principal Component Analysis

09:09:39 β PCA for Dimensionality Reduction

09:15:27 β Introduction to Hierarchical Clustering

09:28:38 β Types of Hierarchical Clustering

09:34:02 β How does Agglomerative hierarchical clustering work

09:42:32 β Euclidean Distance

09:45:10 β Manhattan Distance

09:48:01 β Minkowski Distance

09:50:02 β Jaccard Similarity Coefficient/Jaccard Index

09:54:02 β Cosine Similarity

09:58:18 β How to find an optimal number for clustering

10:03:02 β Applications Machine Learning

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Enroll now for Machine Learning Certification Course from Reputed Universities:

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Time Map for the Topics Covered:

00:01:09 β What Is Machine learning? (Introduction to Machine Learning)

00:03:00 β Why Machine Learning?

00:04:22 β Road Map to Machine Learning

00:01:09 β How to Use Kaggle (www.kaggle.com)

Machine Learning with Python (Python Libraries for Machine Learning)

00:11:25 – NumPy Python Tutorial (How to Create NumPy Array)

00:14:58 – How to Initialize NumPy Array

00:22:19 – How to check the shape of NumPy arrays

00:24:42 – How to Join NumPy Arrays

00:28:15 – NumPy Intersection & Difference

00:31:50 – NumPy Array Mathematics

00:39:15 – NumPy Matrix

00:42:28 – How to Transpose NumPy Matrix

00:43:21 – NumPy Matrix Multiplication

00:45:45 – NumPy Save & Load

00:47:44 – Python Pandas Tutorial

00:48:09 – Pandas Series Object

00:58:44 – Pandas Dataframe

01:12:00 – Matplotlib Python Tutorial

01:12:12 – Line plot

01:26:32 – Bar plot

01:32:37 – Scatter Plot

01:40:35 – Histogram

01:46:16 – Box Plot

01:51:03 – Violin Plot

01:51:57 – Pie Chart

01:56:39 – DoughNut Chart

01:59:04 – SeaBorn Line Plot

02:07:27 – SeaBorn Bar Plot

02:15:15 – SeaBorn ScatterPlot

02:20:25 – SeaBorn Histogram/Distplot

02:26:52 – SeaBorn JointPlot

02:30:23 – SeaBorn BoxPlot

02:38:59 β Role of Mathematics in Data Science

02:40:23 β What is data?

02:42:34 β What is Information?

02:43:21 β What is Statistics?

02:43:58 β What is Population?

02:46:48 β What is Sample?

02:47:33 β What are Parameters?

02:47:55 β Measures of Central Tendency

02:51:10 β Understanding Empirical Rule

02:53:16 β What is Mean, median, and mode?

02:57:04 β Measures of Spread (Understanding Range, Inter Quartile Range & Box-plot)

03:12:56 β Types of Machine Learning (Supervised, Unsupervised & Reinforcement Learning)

03:27:43 β How does a Machine Learning Model Learn?

03:35:31 β Supervised Machine Learning (Mukesh Rao)

04:34:51 β Python for Machine Learning

04:46:40 β Linear Regression Algorithm (Hands-on)

05:21:13 β What is Logistic Regression

05:29:39 β Linear Regression vs Logistic Regression

05:40:15 β NaΓ―ve Bayes Algorithm

05:49:32 β Diabetes Prediction using NaΓ―ve Bayes

06:15:18 β Decision Tree and Random Forest Algorithm

07:55:01 β Introduction to Support Vector Machines (SVMs)

08:07:08 β Kernel Functions

08:11:56 β Advantages & Disadvantages of SVMs

08:31:37 β K-NN Algorithm (K-Nearest Neighbour Algorithm)

08:40:13 β Introduction to Unsupervised Learning – Clustering

08:48:35 β Introduction to Principal Component Analysis

09:09:39 β PCA for Dimensionality Reduction

09:15:27 β Introduction to Hierarchical Clustering

09:28:38 β Types of Hierarchical Clustering

09:34:02 β How does Agglomerative hierarchical clustering work

09:42:32 β Euclidean Distance

09:45:10 β Manhattan Distance

09:48:01 β Minkowski Distance

09:50:02 β Jaccard Similarity Coefficient/Jaccard Index

09:54:02 β Cosine Similarity

09:58:18 β How to find an optimal number for clustering

10:03:02 β Applications Machine Learning

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I recommend for those who need knowledge just for doing the college project

It's very usefull

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Ankit bhai achhe se padh

comment section me kya kar rha he

Thank You π― β οΈπ―π―π―π€π€π―π―π―π―β οΈβ οΈβ οΈβ οΈβ οΈπ―π―π―π€π€π€π€π€π€π€π€π€

What is the pre request for learning ML.