Data Science: Supervised Machine Learning Bootcamp in Python

This course focuses on one of the main branches of Machine Learning that is Supervised Learning in Python. If you are not familiar with Python, there is nothing to worry about because the Lectures comprising the Python Libraries will train you enough and will make you comfortable with the programming language.

The course is divided into two sections, in the first section, you will be having lectures about Python and the fundamental libraries like Numpy, Pandas, Seaborn, Scikit-Learn and Tensorflow that are necessary for one to be familiar with before putting his hands-on Supervised Machine Learning.

Then is the Supervised Learning part, which basically comprises three main chapters Regression, Classification, and Deep Learning, each chapter is thoroughly explained, both theoretically and experimentally.

During all of these lectures, we’ll be learning how to use the different machine learning algorithms to create some mind-blowing modules of Machine Learning, and at the end of the course, you’ll be trained enough that you would be able to develop you own Recognitions Systems and Prediction Models and many more.

Let’s get started!

Who this course is for:

• 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

What you’ll learn

• 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)
  

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