Machine Learning, Business analytics with R Programming Py

Machine Learning, Business analytics with R Programming & Py, available at $49.99, has an average rating of 4, with 114 lectures, 2 quizzes, based on 43 reviews, and has 1389 subscribers.

You will learn about 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 This course is ideal for individuals who are Students or Working professionals looking to move into data science & machine learning career or Statisticians interested in machine learning It is particularly useful for Students or Working professionals looking to move into data science & machine learning career or Statisticians interested in machine learning.

Enroll now: Machine Learning, Business analytics with R Programming & Py

Summary

Title: Machine Learning, Business analytics with R Programming & Py

Price: $49.99

Average Rating: 4

Number of Lectures: 114

Number of Quizzes: 2

Number of Published Lectures: 114

Number of Published Quizzes: 2

Number of Curriculum Items: 121

Number of Published Curriculum Objects: 121

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Students

Working professionals looking to move into data science & machine learning career

Statisticians interested in machine learning

Target Audiences

Students

Working professionals looking to move into data science & machine learning career

Statisticians interested in machine learning

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

1. Multi class linear regression with Python

2. 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  

Na?ve 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

Course Curriculum

Chapter 1: Complete machine learning & data science course Introduction

Lecture 1: Introduction

Lecture 2: How to get help for Machine learning & Data science

Lecture 3: Data science & machine learning as career option

Lecture 4: How to make right decisions for your career in data science & machine learning

Lecture 5: Various Job options for aspiring data scientists & machine learning engineers

Lecture 6: AI Vs ML Vs DL with Types of machine learning

Chapter 2: Job hunting strategy

Lecture 1: Strategy 1 with tips on resume/cv building

Lecture 2: Strategy 2 to target job avenues to get more calls & offers

Chapter 3: Hands-on R programming for machine learning & data science

Lecture 1: R Introduction with installation of rstudio

Lecture 2: Vectors, Matrix & Data frame

Lecture 3: Data types in R

Lecture 4: Variables & Objects in R

Lecture 5: Comments & Vectors in R

Lecture 6: Data wrangling with R-Part 1

Lecture 7: Data wrangling with R-Part 2

Lecture 8: Operators in R-Part 1

Lecture 9: Operators in R-Part 2

Lecture 10: Loops in R

Lecture 11: If Else conditional blocks in R

Lecture 12: Functions in R

Lecture 13: Assignment for R Programming fundamentals

Chapter 4: Machine learning fundamentals

Lecture 1: Reading various kind of files with R

Lecture 2: Data pre-processing introduction- selection & manipulation

Lecture 3: Data selection & manipulation-Rows & Columns

Lecture 4: Data selection & manipulation with Dplyr- Part 1

Lecture 5: Data selection & manipulation with Dplyr- Part 2

Lecture 6: Data selection & manipulation with Subset & Merge

Lecture 7: Data selection & manipulation-Handling missing data

Lecture 8: Data manipulation & selection assignment

Chapter 5: Data visualization with R

Lecture 1: Data visualization with R- introduction

Lecture 2: Histogram vs bar plot with plotting missing values

Lecture 3: Bar plots and Histograms with R

Lecture 4: Horizontal bar plots and Plot function

Lecture 5: More on Plot function with heat map

Lecture 6: Boxplot with Pair & Par commands

Lecture 7: Line graphs and Maps

Lecture 8: GGPlot 2 Introduction

Lecture 9: Data visualization with GGPlot2

Lecture 10: Lattice and Scatter3d plot libraries

Lecture 11: Assignment

Chapter 6: Applied Statistics for Machine learning

Lecture 1: Introduction to applied statistics with Variables and Sample Size

Lecture 2: Descriptive vs Inferential analysis

Lecture 3: Mean, Median, Mode and Range

Lecture 4: Variance and Standard deviation

Lecture 5: Standard Error- Skewness with Kurtosis

Lecture 6: P value with confidence interval

Lecture 7: T test and F ratio

Lecture 8: Hypothesis testing

Chapter 7: Introduction to Machine learning models

Lecture 1: Machine learning fundamentals

Lecture 2: Regression fundamentals

Lecture 3: Classification fundamentals

Lecture 4: Fundamentals of dimension reduction and data reduction models

Chapter 8: ANOVA with R

Lecture 1: ANOVA introduction & fundamentals

Lecture 2: ANOVA in R

Lecture 3: ANOVA Project

Chapter 9: Evaluation metrics or loss function for linear regression

Lecture 1: Evaluation metrics or loss function for linear regression

Chapter 10: Linear regression with R

Lecture 1: Fundamentals of Linear regression

Lecture 2: Implementation of linear regression in R

Lecture 3: Linear regression project

Chapter 11: Logistic Regression with R

Lecture 1: Fundamentals of Logistic Regression

Lecture 2: Logistic Regression with R- Part 1- Data Wrangling

Lecture 3: Logistic regression with R-Part 2 Data Wrangling and visualization

Lecture 4: Logistic regression with R-Part 3 Conclusion with Prediction

Lecture 5: Logistic Regression Project

Chapter 12: Dimension reduction technique with principal component analysis

Lecture 1: Fundamentals of Dimension reduction technique with principal component analysis

Lecture 2: PCA implementation in r with princomp

Lecture 3: PCA project

Chapter 13: Clustering with K-Means

Lecture 1: Fundamentals of clustering with K-Means

Lecture 2: K-Means implementation in r

Lecture 3: K-Means Project

Chapter 14: Tree based models- CART technique & Random Forest

Lecture 1: Fundamentals of Decision tree and CART technique

Lecture 2: CART Implementation in R

Lecture 3: Fundamentals of Ensemble techniques with Random forest machine learning model

Lecture 4: Random Forest with R

Lecture 5: Random Forest Project

Chapter 15: KNN- K Nearest Model

Lecture 1: Fundamentals of KNN

Lecture 2: Implementation of KNN in R

Lecture 3: KNN Project

Instructors

Akhilendra Singh MBA, CSPO, PSM1
Helping aspiring Product managers & Business analysts

Rating Distribution

1 stars: 2 votes

2 stars: 2 votes

3 stars: 6 votes

4 stars: 20 votes

5 stars: 13 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!