Machine Learning & Data Science Bootcamp with R & Python

The course “Data Engineering with Python” is proudly presented by the Academy of Computing and Artificial Intelligence. It all began when the Academy of Computing and Artificial Intelligence’s expert team (PhD, PhD candidates, Senior Lecturers, Consultants, Researchers) and Industry Experts came together. In 2021, hiring managers were debating the highest-paying positions and talents in the IT/Computer Science/Engineering/Data Science sector.

After completing the course, you will be prepared to begin a career in Data Mining and Machine Learning.

1) [A-Z] Introduction to Machine Learning Comprehensive Instruction with Step-by-Step Directions

2) Creating a Machine Learning Environment – Step-by-Step Instructions [R Programming & Python]

(Univariate Linear Regression, Multivariate Linear Regression, Logistic Regression, Naive Bayes Classifier, Trees, Support Vector Machines (SVM), Random Forest) 3) Supervised Learning

4) Learning Without Supervision

5) CNNs (Convolutional Neural Networks)

Artificial Neural Networks (A.N.N.)

7) Projects in the Real World using Sources

Outcomes of Course Learning

To raise awareness of the two types of machine learning (supervised and unsupervised) (Why we need Data Mining & Machine Learning, What is Data Mining, What is Machine Learning, Traditional Programming Vs Machine Learning, Steps to Solve a Data Mining & Machine Learning Problem, Classification, Clustering)

Explain how to use Machine Learning approaches to create intelligent problem-solving strategies.

Using a cutting-edge Python framework, create appropriate neural models.

Setting up a Machine Learning Environment – Step-by-Step Instructions [R Programming & Python]

CNN stands for Convolutional Neural Networks.

MIT and a number of other prestigious universities have provided resources.

Source-based projects