Description
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 MarchDeep learning with Image recognition & KerasFundamentals of deep learningMethodology of deep learningArchitecture of deep learning modelsWhat is activation function & why we need themRelu & Softmax activation functionIntroduction to KerasBuild a Multi-layer perceptron model with Python & Keras for Image recognitionRelease notes- 30 November 2019 Updates;Machine learning & Data science with PythonIntroduction to machine learning with pythonWalk through of anaconda distribution & Jupyter notebookNumpyPandasData analysis with Python & PandasData Visualization with PythonData Visualization with PandasData visualization with MatplotlibData visualization with SeabornMulti class linear regression with PythonLogistic regression with PythonI 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
