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.

Who this course is for:

Begginer Python developers who want to get started with Machine Learning

Basic operations in Numpy, Scipy, Pandas, and Matplotlib

Vector, Matrix, and Tensor manipulation

Visualizing data

Reading, writing, and manipulating DataFrames

Requirements

Linear Algebra, Probability, and Python Programming

Description

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:

linear algebra

probability

Python programming

Who this course is for:

Anyone who wants to implement Machine Learning algorithms

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:

Part1 – Introduction and setting Up environment

Part2 – Data Collection

Part3 – Data Analysis and Visualization

Part4 – Data Preprocessing

Part5 – Data Modelling

Part6 – Model Validation

Part7 – Ensemble Learning

Part8 – Dimensionality reduction

Part9 – Outro

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.

Who this course is for:

Beginner Python developers curious about Machine learning.