01.06.2021, 12:55
![[Bild: j0ornn2jrbvitny6fiodqn4jct.jpg]](https://abload.de/img/j0ornn2jrbvitny6fiodqn4jct.jpg)
Udemy - Professional Certificate in Machine Learning
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | VTT | Size: 10.4 GB | Duration: 24h 0m
Learn all the skills to become a Data Scientist & Build 500+ Artificial Intelligence Projects with source
What you'll learn
Machine Learning - [A -Z] Comprehensive Training with Step by step guidance
Supervised Learning - (Univariate Linear regression, Multivariate Linear Regression, Logistic regression, Naive Bayes Classifier, Trees, Support Vector Machines, Random Forest)
Unsupervised Learning - Clustering, K-Means clustering
Data Pre-processing - Data Preprocessing is that step in which the data gets transformed, or Encoded, to bring it to such a state that now the machine can easily parse it.
Evaluating the Machine Learning Algorithms : Precision, Recall, F-Measure, Confusion Matrices,
Deep Convolutional Generative Adversarial Networks (DCGAN)
Java Programming For Data Scientists
Python Programming Basics For Data Science
Algorithm Analysis For Data Scientists
Description
Academy of Computing & Artificial Intelligence proudly presents you the course "Professional Certificate in Data Mining & Machine Learning".m
It all started when the expert team of The Academy of Computing & Artificial Intelligence [ACAI] (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 2020.
To make the course more interactive, we have also provided a live code demonstration where we explain to you how we could apply each concept/principle [Step by step guidance]. Each & every step is clearly explained. [Guided Tutorials]
"While artificial intelligence (AI) is the broad science of mimicking human abilities, machine learning is a specific subset of AI that trains a machine how to learn. Watch this video to better understand the relationship between AI and machine learning. You'll see how these two technologies work, with useful examples and a few funny asides."
Course Learning Outcomes
To provide a solid awareness of Supervised & Unsupervised learning coming under Machine Learning
Explain the appropriate usage of Machine Learning techniques.
To build appropriate neural models from using state-of-the-art python framework.
To build neural models from scratch, following step-by-step instructions.
To build end - to - end effective solutions to resolve real-world problems
To critically review and select the most appropriate machine learning solutions
python programming is also inclusive.
Requirements
A computer with internet connection
Passion & commitment
At the end of the Course you will gain the following
# Learn to Build 500+ Projects with source code
# Strong knowledge of Fundamentals in Machine Learning
# Apply for the Dream job in Data Science
# Gain knowledge for your University Project
Setting up the Environment for Python Machine Learning
Understanding Data With Statistics & Data Pre-processing
Data Pre-processing - Scaling with a demonstration in python, Normalization , Binarization , Standardization in Python,feature Selection Techniques : Univariate Selection
Data Visualization with Python -charting will be discussed here with step by step guidance, Data preparation and Bar Chart,Histogram , Pie Chart, etc..
Artificial Neural Networks with Python, KERAS
KERAS Tutorial - Developing an Artificial Neural Network in Python -Step by Step
Deep Learning -Handwritten Digits Recognition [Step by Step] [Complete Project ]
Naive Bayes Classifier with Python [Lecture & Demo]
Linear regression
Logistic regression
Introduction to clustering [K - Means Clustering ]
K - Means Clustering
Homepage
Code:
https://anonymz.com/?https://www.udemy.com/course/professional-certificate-in-machine-learning/![[Bild: 3.univariatelinearreghkkvo.jpg]](https://abload.de/img/3.univariatelinearreghkkvo.jpg)
