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
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]
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
Who this course is for:
- Anyone who wish to start a career in Machine Learning
- 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 ]