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[Discount] Professional Certificate in Machine Learning


Last updated on June 9, 2021 21:14
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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.


  • 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

  1. Setting up the Environment for Python Machine Learning

  2. Understanding Data With Statistics & Data Pre-processing 

  3. Data Pre-processing – Scaling with a demonstration in python, Normalization , Binarization , Standardization in Python,feature Selection Techniques : Univariate Selection

  4. Data Visualization with Python -charting will be discussed here with step by step guidance, Data preparation and Bar Chart,Histogram , Pie Chart, etc..

  5. Artificial Neural Networks with Python, KERAS

  6. KERAS Tutorial – Developing an Artificial Neural Network in Python -Step by Step

  7. Deep Learning -Handwritten Digits Recognition [Step by Step] [Complete Project ]

  8. Naive Bayes Classifier with Python [Lecture & Demo]

  9. Linear regression

  10. Logistic regression

  11. Introduction to clustering [K – Means Clustering ]

  12. 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

Who this course is for:

  • Anyone who wish to start a career in Machine Learning

Course content

  • Setting up the Environment for Python Machine Learning
  • Python Basics For Machine Learning
  • Understanding Data With Statistics & Data Pre-processing
  • Data Visualization with Python
  • Artificial Neural Networks [ Comprehensive Sessions]
  • Naive Bayes Classifier with Python [Lecture & Demo]
  • Natural Language Processing for Data Scientists
  • Linear regression
  • Logistic regression
  • Introduction to clustering [K – Means Clustering ]


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