ExpiredMachine Learning & Deep Learning in Python & R
Description
You’re looking for a complete Machine Learning and Deep Learning course that can help you launch a flourishing career in the field of Data Science & Machine Learning, right?
You’ve found the right Machine Learning course!
After completing this course you will be able to:
· Confidently build predictive Machine Learning and Deep Learning models to solve business problems and create business strategy
· Answer Machine Learning related interview questions
· Participate and perform in online Data Analytics competitions such as Kaggle competitions
Check out the table of contents below to see what all Machine Learning and Deep Learning models you are going to learn.
How this course will help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.
If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning.
Why should you choose this course?
This course covers all the steps that one should take while solving a business problem through linear regression.
Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course
We are also the creators of some of the most popular online courses – with over 600,000 enrollments and thousands of 5-star reviews like these ones:
This is very good, i love the fact the all explanation given can be understood by a layman – Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price. – Daisy
Our Promise
Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.
Download Practice files, take Quizzes, and complete Assignments
With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.
Table of Contents
Section 1 – Python basic
This section gets you started with Python.
This section will help you set up the python and Jupyter environment on your system and it’ll teach
you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.
Section 2 – R basic
This section will help you set up the R and R studio on your system and it’ll teach you how to perform some basic operations in R.
Section 3 – Basics of Statistics
This section is divided into five different lectures starting from types of data then types of statistics
then graphical representations to describe the data and then a lecture on measures of center like mean
median and mode and lastly measures of dispersion like range and standard deviation
Section 4 – Introduction to Machine Learning
In this section we will learn – What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.
Section 5 – Data Preprocessing
In this section you will learn what actions you need to take a step by step to get the data and then
prepare it for the analysis these steps are very important.
We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation.
Section 6 – Regression Model
This section starts with simple linear regression and then covers multiple linear regression.
We have covered the basic theory behind each concept without getting too mathematical about it so that you
understand where the concept is coming from and how it is important. But even if you don’t understand
it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.
We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem.
Section 7 – Classification Models
This section starts with Logistic regression and then covers Linear Discriminant Analysis and K-Nearest Neighbors.
We have covered the basic theory behind each concept without getting too mathematical about it so that you
understand where the concept is coming from and how it is important. But even if you don’t understand
Who this course is for:
People pursuing a career in data science
Working Professionals beginning their Data journey
Statisticians needing more practical experience
