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
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:
Applied statistics for machine learning
Machine learning fundamentals
ANOVA Implementation with R
Linear regression with R
Dimension Reduction Technique
Tree-based machine learning techniques
Neural network machine learning technique
When you sign up for the course, you also: