Machine Learning & Data Science Bootcamp with R & Python

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mp4 | Video: 1280x720, 1433 Kbps , avc1 | Audio:N/A, N/A, N/A Ch
Genre: eLearning | Duration: (05:06:45) | Size:2.79 GB

Learn R, Python, Machine Learning, Deep Learning, Google Colab, Real world projects with Code and step by step guidance

mp4 | Video: 1280x720, 1433 Kbps , avc1 | Audio:N/A, N/A, N/A Ch
Genre: eLearning | Duration: (05:06:45) | Size:2.79 GB

Learn R, Python, Machine Learning, Deep Learning, Google Colab, Real world projects with Code and step by step guidance
What you'll learn:
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
Types of Learning in Machine learning (Supervised, Unsupervised, Reinforcement )
Classification & Clustering
Setting up the Environment for Machine Learning - R language and R studio , Python, Anaconda
Introduction to Deep Learning - Guest Lecture
Machine learning project : Car Price Prediction Project
Kaggle - Covid 19- Classification (Chest X-ray.) - Covid-19 & Pneumonia
Supervised Learning
Unsupervised Learning

Requirements
Computer with Internet connection
Python or any programming language

Description
Academy of Computing & Artificial Intelligence proudly present you the course "Data Engineering with Python". It all started when the expert team of Academy of Computing & Artificial Intelligence (PhD, PhD Candidates, Senior Lecturers , Consultants , Researchers) and Industry Experts . hiring managers were having a discussion on the most highly paid jobs & skills in the IT/Computer Science / Engineering / Data Science sector in 2021.

At the end of the Course you will be able to start your career in Data Mining & Machine Learning.

1) Introduction to Machine Learning - [A -Z] Comprehensive Training with Step by step guidance

2) Setting up the Environment for Machine Learning - Step by step guidance [R Programming & Python]

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

4) Unsupervised Learning

5) Convolutional Neural Networks - CNN

6) Artificial Neural Networks

7) Real World Projects with Source

Course Learning Outcomes

To provide awareness of (Supervised & Unsupervised learning) coming under Machine Learning (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)

Describe intelligent problem-solving methods via appropriate usage of Machine Learning techniques.

To build appropriate neural models from using state-of-the-art python framework.

To setup the Environment for Machine Learning - Step by step guidance [R Programming & Python]

Convolutional Neural Networks - CNN

Resources from MIT and many famous Universities

Projects with Source

Who this course is for
Anyone who wish to start the career in Data Science & Machine Learning

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