Getting Started with Machine Learning
This course is especially for beginners who want to get started their journey in the field of machine learning. This course provides the hands-on experience with the python and scikit learn. So if you are new to the machine learning Get started with this course will be a good choice.
Basic operations in Numpy, Scipy, Pandas, and Matplotlib
Vector, Matrix, and Tensor manipulation
Visualizing data
Reading, writing, and manipulating DataFrames
Welcome! This is Deep Learning, Machine Learning, and Data Science Prerequisites: The Numpy Stack in Python (V2).
The reason I made this course is because there is a huge gap for many students between machine learning “theory” and writing actual code.
As I’ve always said: “If you can’t implement it, then you don’t understand it”.
Without basic knowledge of data manipulation, vectors, and matrices, students are not able to put their great ideas into working form, on a computer.
This course closes that gap by teaching you all the basic operations you need for implementing machine learning and deep learning algorithms.
The goal is that, after you take this course, you will learn about machine learning algorithms, and implement those algorithms in code using the tools and techniques you learned in this course.
Suggested Prerequisites:
Machine learning
Prediction Models
Scikit learn
Python
Numpy
Pandas
Matplotlib
This is a practical machine learning course for people who wan to kickstart their career in Machine learning. This course will give you an understanding of what is machine learning and the concepts related to it. The course is structured in the following way:
At the end of this course you will learn how to create a simple pipeline for a prediction model and make it feasible for real time deployment.
