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Machine Learning, Business analytics with R Programming & Py

Name: Machine Learning, Business analytics with R Programming & Py
Rating: 4.1 (45 reviews)

Machine learning, data science & business analytics with R & Python. Build models with rstudio, jupyter notebook & keras

Created byAkhilendra Singh MBA, CSPO, PSM1

Last updated 12/2020

English

What you'll learn

Machine learning & Data science with R & Python
Fundamentals of Machine learning
Data science
Deep learning models
Image recognition
Keras
R programming
Anaconda distribution & jupyter notebook
Numpy & pandas
Multi-layer perceptron
Data visualization with pandas, seaborn & matplotlib
Data visualization with base R & libraries like ggplot2, lattice, scatter3d plot & more
Applied statistics for machine learning covering important topics like standard error, variance, p value, t-test etc.
Machine learning models like Neural network, linear regression, logistic regression & more.
Handle advance concepts like dimension reduction & data reduction techniques with PCA & K-Means
Classification & Regression Tree with Random Forest machine learning model
Real life projects to help you understand industry application
Tips & Tools to create your online portfolio to promote your skills
Tutorial on job searching strategy to find appropriate jobs in machine learning, data science or any other industry.
Learn business analytics
Tips to improve your resume and linkedin profile

Course content

23 sections • 114 lectures • 18h 25m total length

Introduction5:21
Course introduction.
How to get help for Machine learning & Data science4:22
Data science & machine learning as career option8:12
In this video and next few videos, we will look at career options in data science and machine learning.
How to make right decisions for your career in data science & machine learning11:39
This video will teach you how to make correct decision in your career to switch to data science and choose correct profile for you.
Various Job options for aspiring data scientists & machine learning engineers9:34
In this video, we will explore various roles and job profiles in the field of machine learning & data science. It will help you in charting your course.
AI Vs ML Vs DL with Types of machine learning12:37
In this video, we will look at the differences between machine learning & artificial intelligence. We will also cover classification of machine learning in this video.

Strategy 1 with tips on resume/cv building10:33
You will learn about the first strategy which you can use to directly target your favorite employers along with few tips on how to build your CV.
Strategy 2 to target job avenues to get more calls & offers18:50
In this video, we will target job sites and you will learn profile optimization to rank better on these job portals so that you can get more high-quality job options.

R Introduction with installation of rstudio17:02
In this video, you will install rstudio, setup your environment and download all code files from the resource section.
Vectors, Matrix & Data frame6:56
You need to learn fundamentals of vectors, matrix and data frames before learning anything in data science or machine learning.
Data types in R15:30
Learn various types of data types in R.
Variables & Objects in R9:57
You will learn about various types of variables and objects in R. Learn how to create and use them.
Comments & Vectors in R8:51
Learn comments in R and create vectors with R.
Data wrangling with R-Part 19:26
As a data scientist, data wrangling will take most of your time. We will start learning that in this video.
Data wrangling with R-Part 214:20
Learn more techniques about data wrangling in R.
Operators in R-Part 110:57
Learn operators in R programming.
Operators in R-Part 23:13
More operators in R.
Loops in R17:16
Loops are very confusing but important, in this video you will learn about Loops in R programming.
If Else conditional blocks in R9:48
Conditional blocks are integral to any programming language and R language is not an exception. Learn about ifelse blocks in R.
Functions in R13:41
Learn about functions in R programming.
Assignment for R Programming fundamentals7:23
Test your knowledge of R programming language.
Assignment-Submission

Reading various kind of files with R17:19
Before you start with machine learning and data science, you need to learn how to load data in rstudio and read various file types. You will do that in this video.
Data pre-processing introduction- selection & manipulation8:46
Learn how to do data selection and manipulation in R programming. This is one of the most important activity for a data scientist.
Data selection & manipulation-Rows & Columns6:26
You will learn various techniques to select rows and columns in any data set with r. Data sub-setting is one of the most common and time consuming activity in machine learning.
Data selection & manipulation with Dplyr- Part 16:00
Learn to apply various techniques like select and filter in your machine learning & data science project using Dplyr package in R. Dyplyr is one of the most used package in the machine learning industry.
Data selection & manipulation with Dplyr- Part 27:47
We will continue with data selection & manipulation for machine learning, in this video we will cover arranging and mutating your data with Dplyr.
Data selection & manipulation with Subset & Merge4:47
Creating subset of the data sets and merging them is quite common, in this video you will learn them. This is used widely in data science.
Data selection & manipulation-Handling missing data15:09
Handling missing values is critical for today's data scientists, you will learn to do that in this video.
Data manipulation & selection assignment2:27
Machine learning fundamentals Quiz

Data visualization with R- introduction5:16
Data visualization is vital to data analysis and data science. In this video, we will start with that.
Histogram vs bar plot with plotting missing values6:16
There is lots of confusion around histograms and bar plots. You will learn the differences between them and where to use them.
Bar plots and Histograms with R5:59
Create bar plots and histograms with R programming language.
Horizontal bar plots and Plot function5:59
You will learn to create horizontal bar plots in video and start with Plot function.
More on Plot function with heat map5:01
Explore plot function with learning heat maps.
Boxplot with Pair & Par commands10:19
Learn boxplots, pair and par commands in R to create better data visualization for your machine learning projects.
Line graphs and Maps5:47
Line graphs are common and maps are on demand. Learn them to create with R programming.
GGPlot 2 Introduction9:30
GGPlot2 is one of most popular data visualization library among machine learning engineers and data scientists.
Data visualization with GGPlot23:14
Learn more visualization with ggplot2.
Lattice and Scatter3d plot libraries10:33
You will learn about lattice and scatter3d plots library.
Assignment1:59
Data Visualization Quiz
Assignment-Submission

Introduction to applied statistics with Variables and Sample Size10:53
Fundamentals of applied statistics which are must for machine learning and data science.
Descriptive vs Inferential analysis5:32
Learn what is descriptive analysis and inferential analysis with their differences.
Mean, Median, Mode and Range9:14
Learn mean, median, mode and range.
Variance and Standard deviation5:54
learn variance and standard deviation.
Standard Error- Skewness with Kurtosis4:24
Learn standard error, skewness and kurtosis.
P value with confidence interval7:45
learn confidence interval and p value.
T test and F ratio6:36
Learn t-test and f ratio.
Hypothesis testing6:34
Learn hypothesis testing and how to use it in data science and machine learning.

Machine learning fundamentals13:58
Learn the fundamentals of machine learning and types of machine learning models.
Regression fundamentals3:03
Learn fundamentals of regression machine learning models.
Classification fundamentals2:36
Learn fundamentals of classification problems and how to use them.
Fundamentals of dimension reduction and data reduction models5:01
Learn Fundamentals of dimension reduction like PCA and data reduction models like clustering models.

Requirements

Laptop/desktop/phone with internet connection
Desire to learn machine learning & data science

Description

Learn complete Machine learning, Deep learning, business analytics & Data Science with R & Python covering applied statistics, R programming, data visualization & machine learning models like pca, neural network, CART, Logistic regression & more.

You will build models using real data and learn how to handle machine learning and deep learning projects like image recognition.

You will have lots of projects, code files, assignments and we will use R programming language as well as python.

Release notes- 01 March

Deep learning with Image recognition & Keras

Fundamentals of deep learning
Methodology of deep learning
Architecture of deep learning models
What is activation function & why we need them
Relu & Softmax activation function
Introduction to Keras
Build a Multi-layer perceptron model with Python & Keras for Image recognition

Release notes- 30 November 2019 Updates;

Machine learning & Data science with Python

Introduction to machine learning with python
Walk through of anaconda distribution & Jupyter notebook
Numpy
Pandas
Data analysis with Python & Pandas

Data Visualization with Python

Data Visualization with Pandas
Data visualization with Matplotlib
Data visualization with Seaborn

Multi class linear regression with Python
Logistic regression with Python

I am avoiding repeating same models with Python but included linear regression & logistic regression for continuation purpose.

Going forward, I will cover other techniques with Python like image recognition, sentiment analysis etc.

Image recognition is in progress & course will be updated soon with it.

Unlike most machine learning courses out there, the Complete Machine Learning & Data Science with R-2019 is comprehensive. We are not only covering popular machine learning techniques but also additional techniques like ANOVA & CART techniques.

Course is structured into various parts like R programming, data selection & manipulation, applied statistics & data visualization. This will help you with the structure of data science and machine learning.

Here are some highlights of the program:

Visualization with R for machine learning

Applied statistics for machine learning
Machine learning fundamentals
ANOVA Implementation with R
Linear regression with R
Logistic Regression

Dimension Reduction Technique
Tree-based machine learning techniques
KNN Implementation
Naïve Bayes
Neural network machine learning technique

When you sign up for the course, you also:

Get career guidance to help you get into data science
Learn how to build your portfolio

Create over 10 projects to add to your portfolio
Carry out the course at your own pace with lifetime access

Who this course is for:

Students
Working professionals looking to move into data science & machine learning career
Statisticians interested in machine learning

Machine Learning, Business analytics with R Programming & Py

What you'll learn

Explore related topics

Course content

Complete machine learning & data science course Introduction6 lectures • 52min

Job hunting strategy2 lectures • 29min

Hands-on R programming for machine learning & data science13 lectures • 2hr 24min

Machine learning fundamentals8 lectures • 1hr 9min

Data visualization with R11 lectures • 1hr 10min

Applied Statistics for Machine learning8 lectures • 57min

Introduction to Machine learning models4 lectures • 25min

ANOVA with R3 lectures • 32min

Evaluation metrics or loss function for linear regression1 lecture • 15min

Linear regression with R3 lectures • 45min

Requirements

Description

Who this course is for: