Professional Certificate in Machine Learning | Udemy


Professional Certificate in Machine Learning | Udemy
English | Size: 10.61 GB
Genre: eLearning

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

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

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

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About WoW Team

I'm WoW Team , I love to share all the video tutorials. If you have a video tutorial, please send me, I'll post on my website. Because knowledge is not limited to, irrespective of qualifications, people join hands to help me.

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