Free Download Machine Learning Bootcamp™: Hand-On Python in Data Science, with this course you will have the option to Learn Complete hands-on guide to implementing Supervised Machine Learning Algorithm in Python including ANN, CNN & RNN. It contains 86 recordings. Remember that numerous understudies are tried out this course Machine Learning Bootcamp™: Hand-On Python in Data Science, so don't hold on to download it presently, it's totally free. You can start learning Machine Learning Bootcamp™: Hand-On Python in Data Science by clicking the download link below.
Before you can start learning from this course explained in English (US) you need to have Basic Knowledge of any programming language, Passion for learning.
Machine Learning Bootcamp™: Hand-On Python in Data Science is targeting for people that have interset in Those who are interested in AI and Machine Learning, Those who have basic knowledge of any programming language, Those who want to be create awesome Machine Learning and AI modules, And those who want to earn some handsome amount of money from Machine Learning Field in Future.
Finally you will learn how to Basics of Python (Introduction to Spyder & Jupyter Notebook), Numpy (•Introduction to the Library •Nd-array Object •Data Types •Array Attributes •Indexing and Slicing •Array Manipulation), Pandas (•Introduction to the Library •Series Data Structures •Pandas Data Frame •Pandas Basic Functionality • Crash Course – Data Visualization • Crash Course – ScikitLearn), Tensorflow (•Introduction to the Library •Basic Syntax •Tensorflow Graphs •Variable Place Holders •Neural Network •Tensorboard), Seaborn (•Distribution Plots •Categorical Plots •Regression Plots •Style and Color), Plotly and Cufflinks, Regression (• Simple Linear Regression •Multiple Linear Regression •Polynomial Regression •Support Vector Regression • Decision Tree Regression • Random Forest Regression, Classification (•Logistic Regression •K-Nearest Neighbors • Support Vector Machine •Kernel SVM •Naïve Bayes •Decision Tree Classification •Random Forest), Deep Learning (•Artificial Neural Networks •Convolutional Neural Networks •Recurrent Neural Networks).