Professional Certificate in Machine Learning

Professional Certificate in Machine Learning, available at $44.99, has an average rating of 4.65, with 196 lectures, based on 72 reviews, and has 591 subscribers.

You will learn about 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, SVM, Random Forest) Unsupervised Learning – Clustering, K-Means clustering Data Pre-processing – Data Preprocessing is that step in which the data gets transformed, or Encoded 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 This course is ideal for individuals who are Anyone who wish to start a career in Machine Learning It is particularly useful for Anyone who wish to start a career in Machine Learning.

Enroll now: Professional Certificate in Machine Learning

Summary

Title: Professional Certificate in Machine Learning

Price: $44.99

Average Rating: 4.65

Number of Lectures: 196

Number of Published Lectures: 196

Number of Curriculum Items: 196

Number of Published Curriculum Objects: 196

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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, SVM, Random Forest)

Unsupervised Learning – Clustering, K-Means clustering

Data Pre-processing – Data Preprocessing is that step in which the data gets transformed, or Encoded

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

Who Should Attend

Anyone who wish to start a career in Machine Learning

Target Audiences

Anyone who wish to start a career in Machine Learning

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 2023.

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

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

Course Curriculum

Chapter 1: Setting up the Environment for Python Machine Learning

Lecture 1: Python For machine Learning : Setting up the Environment : Anaconda

Lecture 2: Downloading and Setting up Python and PyCharm IDE

Chapter 2: Python Basics For Machine Learning

Lecture 1: Python For Absolute Beginners – Variables – Part 1

Lecture 2: Python For Absolute Beginners – Variables – Part 2

Lecture 3: Python For Absolute Beginners – Variables – Part 3

Lecture 4: Python For Absolute Beginners – Lists

Lecture 5: Python For Absolute Beginners – Lists Part 2

Lecture 6: Python For Absolute Beginners – Lists Part 3

Lecture 7: Software Design – Problem Solving

Lecture 8: Software Design – Flowcharts – Sequence

Lecture 9: Software Design – Repetition

Lecture 10: Flowcharts Questions and Answers # Problem Solving

Chapter 3: Understanding Data With Statistics & Data Pre-processing

Lecture 1: Understanding Data with Statistics: Reading data from file

Lecture 2: Understanding Data with Statistics: Checking dimensions of Data

Lecture 3: Understanding Data with Statistics: Statistical Summary of Data

Lecture 4: Understanding Data with Statistics Correlation between attributes

Lecture 5: Data Pre-processing – Scaling with a demonstration in python

Lecture 6: Data Pre-processing – Normalization , Binarization , Standardization in Python

Lecture 7: feature Selection Techniques : Univariate Selection

Chapter 4: Data Visualization with Python

Lecture 1: Data preparation and Bar Chart

Lecture 2: Data Visualization with Python Histogram , Pie Chart, etc..

Chapter 5: Artificial Neural Networks [ Comprehensive Sessions]

Lecture 1: Introduction to Artificial Neural Networks

Lecture 2: Creating the First ANN from Scratch with Python

Lecture 3: Multiple Input Neuron

Lecture 4: Creating a simple layer of neurons, with 4 inputs. # Python # From scratch

Lecture 5: ANN – Illustrative Example

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

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

Chapter 6: Naive Bayes Classifier with Python [Lecture & Demo]

Lecture 1: Lecture & Demo: Naive bayes classifier

Chapter 7: Natural Language Processing for Data Scientists

Lecture 1: Introduction to Natural Language Processing [Theory-Guest Lecture by Professor]

Lecture 2: Setting up the Environment for NLP – ACH

Lecture 3: Introduction to Tokenization

Lecture 4: Downloading and Setting up NLTK

Lecture 5: Tokenization Tutorial

Lecture 6: Introduction to Normalization

Lecture 7: Normalization Tutorial

Lecture 8: Introduction to Part of Speech Tagging

Lecture 9: Part of Speech Tagging Tutorial

Lecture 10: Introduction to Stopwords

Lecture 11: Named Entity Recognition Lecture

Lecture 12: Named Entity Recognition Tutorial

Lecture 13: Classification Lecture

Lecture 14: Classification Tutorial Part 1: Preprocessing movie reviews

Lecture 15: Classification Tutorial Part 2: Feature Sets

Lecture 16: Classification Tutorial Part 3: Naive Bayes

Lecture 17: Classification Homework Exercise

Lecture 18: Real World Applications of NLP [Complete Project] – Introduction

Lecture 19: Twitter Application Descriptions

Lecture 20: Creating a Twitter Application

Lecture 21: Getting the Test Set

Lecture 22: Preparing the Training Set

Lecture 23: Preprocessing

Lecture 24: Classification

Lecture 25: Testing the Model

Lecture 26: Python For Beginners : Variables : Part 1

Lecture 27: Python For Beginners : Variables : Part 2

Lecture 28: Python For Beginners : Variables : Part 3

Lecture 29: Python For Beginners – Lists

Lecture 30: Python For Beginners – Lists Part 2

Lecture 31: Python For Beginners – Lists Part 3

Chapter 8: Linear regression

Lecture 1: Linear regression

Lecture 2: Univariate Linear Regression Demo [Hands-on] Part 1- Linear Regression

Lecture 3: Univariate Linear Regression Demo [Hands-on] Part 2- Linear Regression

Lecture 4: Multivariate Linear Regression Demo [Hands-on] Linear Regression

Chapter 9: Logistic regression

Lecture 1: Logistic regression

Chapter 10: Introduction to clustering [K – Means Clustering ]

Lecture 1: What is clustering in Machine Learning

Lecture 2: K – Means Clustering

Lecture 3: [hands-on] K – Means clustering with python step by step implementation

Lecture 4: K – Means Clustering [Source code – Complete Project]

Lecture 5: K-Means clustering – Code walkthrough with Theory & Practical

Chapter 11: Extra Reading

Lecture 1: Neural Network Optimization

Lecture 2: Popular resources from Top Universities of the world

Lecture 3: Machine Learning – Source codes

Chapter 12: Java programming for Data Scientists

Lecture 1: Major Java Features

Lecture 2: JDK,JRE ,JVM, Platform & Classloader

Lecture 3: Entering the Object oriented programming world – Classes & Objects

Lecture 4: Classes & Objects

Lecture 5: Creating Objects from Classes

Lecture 6: Constructors

Lecture 7: Methods (parameter vs arguement)

Lecture 8: Method Overloading

Lecture 9: Method Overloading Demo

Lecture 10: Data Abstraction

Lecture 11: Encapsulation

Lecture 12: Inheritance

Lecture 13: Inheritance Demo

Lecture 14: Inheritance – instanceof Demo

Lecture 15: Static

Instructors

Academy of Computing & Artificial Intelligence
Senior Lecturer / Project Supervisor / Consultant

Rating Distribution

1 stars: 2 votes

2 stars: 2 votes

3 stars: 5 votes

4 stars: 14 votes

5 stars: 49 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!