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Professional Certificate in Machine Learning

Name: Professional Certificate in Machine Learning
Rating: 4.3 (76 reviews)

Learn all the skills to become a Data Scientist & Build 500+ Artificial Intelligence Projects with source

Created byAcademy of Computing & Artificial Intelligence

Last updated 6/2024

English

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, 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

Course content

16 sections • 196 lectures • 24h 0m total length

Python For machine Learning : Setting up the Environment : Anaconda4:13
Set up your machine learning environment with Anaconda, install the graphical installer, launch Jupyter notebook, and prepare Python tools to run practical ML examples.
Downloading and Setting up Python and PyCharm IDE6:15
Download and install Python and the PyCharm IDE, set up Anakonda with Python notebooks, and run machine learning tasks across Windows, Mac, and Linux.

Python For Absolute Beginners - Variables - Part 15:14
Python For Absolute Beginners - Variables - Part 24:37
Explore variables in Python and string operations, including concatenation, zero-based indexing, and negative indexing, with examples like names and social networks; learn how index errors occur when out of range.
Python For Absolute Beginners - Variables - Part 33:31
Explore Python variables and string operations, using zero-based indexing and slicing with examples like Facebook or name strings, and learn how to get string length.
Python For Absolute Beginners - Lists3:13
Python For Absolute Beginners - Lists Part 23:27
Python For Absolute Beginners - Lists Part 33:11
Software Design - Problem Solving4:46
Software Design - Flowcharts - Sequence9:53
Explore flowcharts as a graphical representation of processes. Learn standard symbols for start/end, input/output, and decision making, and apply sequence, selection, and iteration.
Software Design - Repetition3:21
Flowcharts Questions and Answers # Problem Solving13:31

Understanding Data with Statistics: Reading data from file8:10
Understanding Data with Statistics: Checking dimensions of Data7:57
Understanding Data with Statistics: Statistical Summary of Data11:03
Understanding Data with Statistics Correlation between attributes7:13
Data Pre-processing - Scaling with a demonstration in python11:41
Data Pre-processing - Normalization , Binarization , Standardization in Python8:42
feature Selection Techniques : Univariate Selection18:38

Introduction to Artificial Neural Networks15:14
Creating the First ANN from Scratch with Python13:39
Explore artificial neural networks from scratch by implementing a single neuron in Python, summing weighted inputs x1, x2, x3 with a bias, before applying any activation function.
Multiple Input Neuron8:25
Creating a simple layer of neurons, with 4 inputs. # Python # From scratch26:57
ANN - Illustrative Example8:46
Explore an illustrative neural network example using a perceptron to classify apples and oranges from three inputs: shape, texture, and weight, based on learned weights and bias.
KERAS Tutorial - Developing an Artificial Neural Network in Python -Step by Step29:35
Deep Learning -Handwritten Digits Recognition [Step by Step] [Complete Project ]13:03

Introduction to Natural Language Processing [Theory-Guest Lecture by Professor]
Setting up the Environment for NLP - ACH4:13
Introduction to Tokenization10:58
Downloading and Setting up NLTK6:48
Tokenization Tutorial7:51
Introduction to Normalization6:45
Normalization Tutorial10:59
Introduction to Part of Speech Tagging12:09
Part of Speech Tagging Tutorial12:09
Introduction to Stopwords8:52
Named Entity Recognition Lecture9:15
Named Entity Recognition Tutorial6:19
Classification Lecture8:20
Classification Tutorial Part 1: Preprocessing movie reviews14:31
Classification Tutorial Part 2: Feature Sets12:43
Classification Tutorial Part 3: Naive Bayes7:37
Classification Homework Exercise3:21
Experiment with a sentiment analysis classifier in natural language processing by tweaking training/test splits, adjusting most common words, applying normalization, and removing stop words to boost accuracy beyond 82 percent.
Real World Applications of NLP [Complete Project] - Introduction6:22
Twitter Application Descriptions0:47
Creating a Twitter Application13:33
Getting the Test Set10:59
Preparing the Training Set19:14
Preprocessing15:40
Classification13:16
Testing the Model7:53
Python For Beginners : Variables : Part 15:14
Python For Beginners : Variables : Part 24:37
Python For Beginners : Variables : Part 33:31
Python For Beginners - Lists3:13
Python For Beginners - Lists Part 23:27
Python For Beginners - Lists Part 33:11

Linear regression2:29
Compare linear regression and logistic regression using an age-based insurance example to show why linear regression cannot handle non-linearly separable data, and how logistic regression provides a decision boundary.
Univariate Linear Regression Demo [Hands-on] Part 1- Linear Regression13:51
Univariate Linear Regression Demo [Hands-on] Part 2- Linear Regression27:11
Multivariate Linear Regression Demo [Hands-on] Linear Regression20:52

Requirements

Computer & Internet Connection

Description

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

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

Who this course is for:

Anyone who wish to start a career in Machine Learning

Professional Certificate in Machine Learning

What you'll learn

Explore related topics

Course content

Setting up the Environment for Python Machine Learning2 lectures • 10min

Python Basics For Machine Learning10 lectures • 55min

Understanding Data With Statistics & Data Pre-processing7 lectures • 1hr 13min

Data Visualization with Python2 lectures • 20min

Artificial Neural Networks [ Comprehensive Sessions]7 lectures • 1hr 56min

Naive Bayes Classifier with Python [Lecture & Demo]1 lecture • 17min

Natural Language Processing for Data Scientists31 lectures • 4hr 14min

Linear regression4 lectures • 1hr 4min

Logistic regression1 lecture • 20min

Introduction to clustering [K - Means Clustering ]5 lectures • 1hr

Requirements

Description

Who this course is for: