Complete Machine Learning, Business analytics & Data Science with R programming & Python

Learn machine learning, data science & business analytics with R programming, Python, Numpy, Pandas, Scikit & keras.Build models with rstudio & jupyter notebook

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Course Curriculum

  Evaluation Metrics for Regression & Classification Algorithms
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  Data Visualization with Python-Pandas, Matplotlib & Seaborn
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  Multi class Linear regression with Python & Scikit learn
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Machine learning & Data Science with R & Python

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Are you interested in learning how to use cutting-edge machine learning techniques to solve real-world problems? Our course on machine learning, data science, and business analytics is just what you need!

With this course, you'll learn how to use the powerful R programming language and popular Python libraries like Numpy, Pandas, Scikit, and Keras to build machine learning models and analyze data. You'll also get hands-on experience using popular tools like RStudio and Jupyter Notebook to build and test your models.

Whether you're a beginner looking to get started in data science or an experienced professional looking to expand your skillset, this course has something for you. You'll learn how to apply machine learning techniques to a wide range of problems, from predicting customer behavior to identifying patterns in complex data sets.

Don't wait any longer to start building your skills in machine learning, data science, and business analytics. Enroll in our course today and start your journey towards becoming a data-driven decision maker!

14 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-2021 is affordable and comprehensive. Here are some highlights of the program:

  • Deep learning models
  • Keras
  • Image recognition
  • 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
  • Evaluation Metrics for Regression & Classification like MSE, RMSE, MAE etc.
  • Machine learning fundamentals
  • ANOVA Implementation with R
  • Linear regression with R & Python
  • Logistic Regression with R & Python
  • 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

  • Programmers and developers looking to learn machine learning and data science
  • Managers who are looking to develop fundamental knowledge of the domain
  • Business analysts
  • Students (Tech/MBAs)

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 & Python.

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

Section 16- Deep learning with Keras & Python

  • Introduction to Deep learning
  • Fundamentals of deep learning
  • Deep learning methodology
  • Deep learning architecture
  • Why Activation function in deep learning
  • Relu & Softmax activation functions with Keras introduction
  • Image recognition with Python, Keras & MLP- Part 1
  • Image recognition with Python, Keras & MLP- Part 2
  • Image recognition with Python, Keras & MLP- Part 3
  • Image recognition with Python, Keras & MLP- Part 4

Next update will include

  • CNN with tensorflow

Your Instructor

Akhilendra pratap singh
Akhilendra pratap singh

Hi, My name is Akhilendra Singh. I am a product manager with a top IT company and have been practicing product management, program management, business analysis for over 12 years now.

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

I am founder of where I share information about machine learning, business analysis and various other fields. I have been a webmaster for over 12 years now & in these 12 years, i have learned a lot about digital marketing space which i intend to share through my courses.

Primary objective of these courses is to help you and improve your chances of getting better job in technology. If you have any career related question, please reach out to me.

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

Email- [email protected]


Linkedin Profile- Akhilendra pratap Singh | LinkedIn


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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 1 year access sound? After enrolling, you have unlimited access to this course for 1 year - 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 14 days and we will give you a full refund.

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