What you'll learn:
Data mining & 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
Evaluating the Machine Learning Algorithms : Precision, Recall, F-Measure, Confusion Matrices,
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.
Deep Convolutional Generative Adversarial Networks (DCGAN)
KERAS Tutorial - Developing an Artificial Neural Network in Python -Step by Step
Java Programming For Data Scientists
Python Programming Basics For Data Science
Algorithm Analysis For Data Scientists
Web Development for Data Scientists
Data Visualization
500+ Artificial Intelligence Projects with source
40 Machine Learning Algorithms with source code and guided tutorial 00:00

Requirements
Computer & Internet Connection
Passion for Data mining & Machine Learning

Description
Academy of Computing & Artificial Intelligence proudly presents you the course "Professional Certificate in Data Mining & Machine Learning".

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

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

Does the course get updated?

We continually update the course as well.

What if you have questions?

we offer full support, answering any questions you have.

There's no risk !

Who this course is for:

Anyone who is interested of Data Mining & Machine Learning

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

Citación :

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