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Complete Machine Learning & Data Science with R & Python for 2019

Become a data scientist & build machine learning models using R, Numpy, Pandas & Scikit. More than 15 projects, Code files included & 30 Days full money Refund

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Machine learning & Data Science with R & Python for 2019

Invest in yourself in 2019. Job market is changing like never before & without machine learning & data science skills in your cv, you can't do much.

Everything you need to start your career as data scientist. Learn machine learning fundamentals, applied statistics, R programming, data visualization with ggplot2, seaborn, matplotlib and build machine learning models with R, pandas, numpy & scikit-learn using rstudio & jupyter notebook.More than 15 projects, Code files included & 30 Days full money Refund guarantee.

Right now, you can buy this course at 80% discount. If you don't have the coupon, subscribe now to receive the discount coupon.

Learn complete Machine learning & Data Science with R & Python covering applied statistics, R programming, numpy, pandas, jupyter notebook,data visualization & machine learning models like neural network, CART, Logistic regression & more.

30 Days Money Back (full refund) Guarantee if you don't like it.

Unlike most machine learning courses out there, the Complete Machine Learning & Data Science with R & Python-2019 is affordable and comprehensive. Here are some highlights of the program:

  • Anaconda distribution & Jupyter notebook overview
  • Pandas
  • Numpy
  • Scikit Learn library
  • Seaborn & Matplotlib for visualization with Python
  • Visualization with R for machine learning
  • Applied statistics for machine learning
  • Machine learning fundamentals
  • ANOVA Implementation with R
  • Linear regression with R & Python
  • Logistic Regression
  • Dimension Reduction Technique
  • Tree-based machine learning techniques
  • KNN Implementation
  • Naïve Bayes
  • Neural network machine learning technique

What you will get

  • Career guidance on data scientist roles and how to get into it
  • How to build your portfolio to show your skills
  • Over 10 projects to add to your portfolio
  • Quizzes and projects to sharpen your skills

What you need

  • Laptop/desktop/mobile phone with internet to watch videos
  • Laptop/desktop to practice your knowledge
  • Desire to learn machine learn

Intended Audience

  • Anybody who is looking to get into data science or machine learning roles

Course Details

Section 1- Introduction to course and machine learning

  • Introduction to Artificial Intelligence, Data Science, Business Analytics & Machine learning
  • Data Science career opportunities and job details in field of machine learning.
  • Career planning guidance.
  • How to build your machine learning portfolio for better job opportunities

Section 2- R Fundamentals

  • Introduction to R and R studio
  • How to download R and r studio
  • R Fundamentals covering following topics;
  • Vectors, Matrx & Data frames
  • Data Types
  • Variables & objects in R
  • Various Operators in R
  • Loops – while, for
  • Conditional- if, else
  • Functions

Section 3- Data Wrangling with R

  • Reading files in various format
  • Data selection with R in detail
  • Data manipulation with R in details
  • Handling missing values

Section 4- Visualization with R for machine learning

  • Histogram Vs bar plots
  • Bar plots and histograms in R
  • Horizontal bar plots & plots
  • Heatmaps in R
  • Scatter plots
  • Create maps
  • Pair plots for coefficients
  • Mfpar for better visualization
  • Data visualization with ggplot2
  • Data visualization with lattice & 3d plots with scatter3d plot

Section 5- Applied statistics for machine learning

  • Introduction to statistics for machine learning
  • Descriptive vs Inferential analysis
  • Descriptive statistics- Mean, Median, Mode & Range
  • Standard error
  • Central limit theorem with confidence interval & p value
  • Overview of T test & f ratio
  • Hypothesis testing

Section 6- Machine learning fundamentals

  • Machine learning fundamentals
  • Regression fundamentals
  • Classification fundamentals
  • Clustering fundamentals
  • Data Reduction technique with clustering and dimension reduction technique introduction with PCA.

Section 7- ANOVA Implementation with R

  • ANOVA introduction and fundamentals
  • 1 Way and 2 way anova implementation with R

Section 8- Linear regression with R

  • Linear regression fundamentals and detailed explanation
  • Linear regression implementation in R

Section 9- Logistic Regression

  • Logistic regression fundamentals & detailed explanation
  • Logistic regression implementation with R.

Section 10- Dimension Reduction Technique

  • Dimension reduction technique introduction with focus on PCA.
  • Principal component analysis implementation with R

Section 11 -Clustering

  • Clustering overview with K-means clustering explanation
  • K-means implementation with R

Section 12- Tree based machine learning techniques

  • CART model (classification & regression tree) Introduction
  • CART implementation in R
  • Introduction to ensemble techniques with focus on random forest
  • Random forest machine learning model implementation with R

Section 13- KNN Implementation

  • KNN Fundamentals (k nearest neighbor)
  • KNN Implementation with R

Section 14- naïve Bayes

  • Introduction to naïve bayes and fundamentals
  • Naïve bayes implementation with R

Section 15- Neural network machine learning technique

  • Introduction to neural network technique
  • Implementation of neural network with R

Section 16- 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

Section 16- Data Visualization with Python

  • Data Visualization with Pandas
  • Data visualization with Matplotlib

Next update will include

  • Linear regression with python
  • Logistic regression with python
  • PCA with Python


Your Instructor


Akhilendra pratap singh
Akhilendra pratap singh

Hi, My name is Akhilendra Singh. I am a Business Analyst with a Fortune 500 company and have been practicing business analysis for over 10 years now.

I am a MBA, Certified Business Analyst Professional(CBAP), Certified scrum product owner, Professional Scrum Master I and quite a few other certifications on machine learning, innovation, entrepreneurship and product design.

I created my site akhilendra.com where I share information about machine learning, business analysis and various other fields. I have been a webmaster for over 8 years now & in these 8 years, i have learned a lot about digital marketing space which i intend to share through my courses.

If you have any question about my courses or any question at all, just message me at;

Email- info@akhilendra.com

Website- http://akhilendra.com

Linkedin Profile- https://www.linkedin.com/in/akhilendra-singh-cbap-28459315/


Class Curriculum


  Data Visualization with Python-Pandas, Matplotlib & Seaborn
Available in days
days after you enroll

Frequently Asked Questions


When does the course start and finish?
The course starts now and never ends! It is a completely self-paced online course - you decide when you start and when you finish.
How long do I have access to the course?
How does lifetime access sound? After enrolling, you have unlimited access to this course for as long as you like - across any and all devices you own.
What if I am unhappy with the course?
We would never want you to be unhappy! If you are unsatisfied with your purchase, contact us in the first 30 days and we will give you a full refund.

Get started now!