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

• 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