Practical Machine Learning Using Python

Are you aspiring to become a Machine Learning Engineer or Data Scientist? If yes, then this course is for you. This course covers Python for Data Science and Machine Learning in great detail and is absolutely essential for beginners in Python. In this course, you will use cases and learn about:

• Core concepts of Machine Learning. 

• The role of data and challenges of Bias

• Variance and Overfitting

• Choosing the right performance metrics

• Model evaluation techniques

• Model optimization using Hyperparameter tuning

• Grid Search Cross Validation techniques

Practical Machine Learning Using Python Overview

This course covers the use of Numpy and Pandas Libraries extensively for teaching Exploratory Data Analysis. There is also an introductory lesson included on Deep Neural Networks with a worked-out example of Image Classification using TensorFlow and Keras.

You will learn how to build Classification Models using a range of Algorithms, Regression Models, and Clustering Models. You will learn the scenarios and use cases of deploying Machine Learning models.

Most of this course is hands-on, through completely worked-out projects and examples taking you through Exploratory Data Analysis, Model development, Model Optimization, and Model Evaluation techniques.