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This Decision Tree in the Machine Learning tutorial will help you understand all the basics of the Decision Tree and how the Decision Tree algorithm works. In the end, we will implement a Decision Tree algorithm in Python on loan payment prediction. This Decision Tree tutorial is ideal for both beginners as well as professionals who want to learn Machine Learning Algorithms. Below topics are covered in this Decision Tree Algorithm Tutorial: 0. Intro (0:00) 1. What is Machine Learning? ( 02:25 ) 2. Types of Machine Learning? ( 03:27 ) 3. Problems in Machine Learning ( 04:43 ) 4. What is a Decision Tree? ( 06:29 ) 5. What are the problems a Decision Tree Solves? ( 07:11 ) 6. Advantages of Decision Tree ( 07:54 ) 7. How does Decision Tree Work? ( 10:55 ) 8. Use Case - Loan Repayment Prediction ( 14:32

This Logistic Regression in R video will help you understand what is a regression, the need for regression and the types of regression. You will learn why logistic regression is important, what is logistic regression and at the end, you will also see a use case implementation of logistic regression in R example. Let's begin this data science algorithm! Below topics are explained in this logistic regression in R video: 00:00 Introduction 01:06 Why regression? 03:24 What is regression? 04:38 Types of regression 05:57 Why logistic regression? 08:47 What is logistic regression? 11:53 Use case - College admission using logistic regression

This Logistic Regression video will help you understand how a Logistic Regression algorithm works in Machine Learning. You will learn what is Supervised Learning, what is a classification problem, and the maths behind Logistic Regression. In the end, you will see a demo on how to predict the number present in an image using Logistic Regression in Python. Below topics are covered in this Logistic Regression Tutorial: 0:00 - 01:52 Introduction 01:52 - 05:35 What is Supervised Learning? 05:35 - 06:10 What is Classification? 06:10 - 15:49 What is Logistic Regression? 15:49 - 16:58 Comparing Linear and Logistic regression 16:58 - 18:40 Logistic regression applications 18:40 - 38:17 Use case - Predicting the number in an image

This Linear regression in R video will help you understand what is linear regression, why linear regression, and linear regression in R with example. You will also look at a use case predicting the revenue of a company using multiple linear regression. Now, let's deep dive into this video and understand this data science algorithm. Below topics are explained in this "Linear Regression in R" video: 00:00 Introduction 00:28 Why linear regression? 03:09 What is linear regression? 03:38 How linear regression works? 10:05 Use case - Predicting the revenue using linear regression

Below topics are covered in this Linear Regression Analysis Tutorial: 1. Introduction to Machine Learning 2. Machine Learning Algorithms 3. Applications of Linear Regression 4. Understanding Linear Regression 5. Multiple Linear Regression 6. Usecase - Profit estimation of companies What is Linear Regression Analysis? Machine Learning is an application of Artificial Intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. Linear regression is a statistical model used to predict the relationship between independent and dependent variables by examining two factors: Which variables, in particular, are significant predictors of the outcome variable? How significant is the regression line in terms of making predictions with the highest possible accuracy?

Python is the most widely used programming language today. When it comes to solving Data Science tasks and challenges, Python never ceases to surprise its audience. Most data scientists are already leveraging the power of Python programming every day. Python is easy to learn, easier to debug, widely used, object-oriented, open source, high-performance language and there are many more benefits of using Python programming. Python has been built with extraordinary libraries which are used by programmers everyday in solving the problems. So, now let us talk about the Top 5 Python libraries for Data Science. Below are the Top 5 Python libraries for Data Science: 1. Tensorflow ( 00:29 ) 2. Numpy ( 03:01 ) 3. Scipy ( 06:38 ) 4. Pandas ( 08:20 ) 5. Matplotlib ( 11:41

Statistics is a branch of applied mathematics, that is the study and manipulation of data, including ways to gather, review, analyze, and draw conclusions. This is why statistics still holds a very important place in today’s data science and business intelligence world. In this statistics tutorial, you will learn all about statistics, including, percentile in statistics, what is normal distribution, Central Limit Theorem, and probability density function. Start learning the statistics tutorial now. Statistics is primarily an applied branch of mathematics, which tries to make sense of observations in the real world. Statistics is generally regarded as one of the pillars of data science. ➡️ Data scientist Masters Program What are the course objectives? This course will enable you to: 1. Gain a foundational understanding of business analytics 2. Install R, R-studio, and workspace setup. You will also learn about the various R packages 3. Master the R programming and understand how various statements are executed in R 4. Gain an in-depth understanding of data structure used in R and learn to import/export data in R 5. Define, understand and use the various apply functions and DPLYP functions 6. Understand and use the various graphics in R for data visualization 7. Gain a basic understanding of the various statistical concepts 8. Understand and use the hypothesis testing method to drive business decisions 9. Understand and use linear, non-linear regression models, and classification techniques for data analysis 10. Learn and use the various association rules and Apriori algorithm 11. Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering Who should take this course? There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training, especially for the following professionals: IT professionals looking for a career switch into data science and analytics Software developers looking for a career switch into data science and analytics Professionals working in data and business analytics Graduates looking to build a career in analytics and data science Anyone with a genuine interest in the data science field Experienced professionals who would like to harness data science in their fields Who should take this course? There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training, especially for the following professionals: 1. IT professionals looking for a career switch into data science and analytics 2. Software developers looking for a career switch into data science and analytics 3. Professionals working in data and business analytics 4. Graduates looking to build a career in analytics and data science 5. Anyone with a genuine interest in the data science field 6. Experienced professionals who would like to harness data science in their fields

This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course. Why learn Data Science? Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data. You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn’s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to: 1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics. Install the required Python environment and other auxiliary tools and libraries 2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions 3. Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave 4. Perform data analysis and manipulation using data structures and tools provided in the Pandas package 5. Gain expertise in machine learning using the Scikit-Learn package The Data Science with python is recommended for: 1. Analytics professionals who want to work with Python 2. Software professionals looking to get into the field of analytics 3. IT professionals interested in pursuing a career in analytics 4. Graduates looking to build a career in analytics and data science 5. Experienced professionals who would like to harness data science in their fields

Will cover all the below-given topics required for a complete Data Science Tutorial: 0. Introduction (0:00) 1. Data Science basics (01:28) 2. What is Data Science (05:51) 3. Need for Data Science (06:38) 4. Business intelligence vs Data Science (17:30) 5. Prerequisites for Data Science (22:31) 6. What does a Data Scientist do? (30:23) 7. Demand for Data Scientist (53:03) 8. Linear regression (2:30:10) 9. Decision trees (2:53:39) 10. Logistic regression in R (3:09:12) 11. What is a decision tree? (3:27:04) 12. What is clustering? (4:35:40) 13. Divisive clustering (4:51:14) 14. Support vector machine (5:17:21) 15. K-means clustering 96:44:13) 16. Time series analysis (7:33:05) 17. How to Become a Data Scientist (8:26:54) 18. Job roles in Data Science (8:30:59) 19. Simplilearn certifications in Data Science (8:33:50) 20. Who is a Data Science engineer? (8:34:34) 21. Data Science engineer resume (9:00:04) 22. Data Science interview questions and answers (9:04:42)

This What is Data Science Video will give you an idea of a life of Data Scientist. This Data Science for Beginners video will also explain the steps involved in the Data Science project, roles & salary offered to a Data Scientist. Data Science is basically dealing with unstructured and structured data. Data Science is a field that comprises of everything that is related to data cleansing, preparation, and data analysis.

Dosto, aaj mae apko ek aisa platform bataunga jaha se aap data science bilkul basic se advanced level tak sikh sakte ho, wo bhi both online and classroom learning k sath. Aur yaha apko placement assistant bhi milega. Data Science Certificate Courses: https://bit.ly/3noGBx7