Professional Certificate in Data Science 2024

Professional Certificate in Data Science 2024, available at $54.99, has an average rating of 4.05, with 177 lectures, based on 154 reviews, and has 900 subscribers.

You will learn about Python Programming Basics For Data Science 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 Evaluating the Machine Learning Algorithms : Precision, Recall, F-Measure, Confusion Matrices, 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. Algorithm Analysis For Data Scientists KERAS Tutorial – Developing an Artificial Neural Network in Python -Step by Step Deep Learning -Handwritten Digits Recognition [Step by Step] [Complete Project ] Deep Convolutional Generative Adversarial Networks (DCGAN) Java Programming For Data Scientists Kaggle – Covid 19- Classification (Chest X-ray.) – Covid-19 & Pneumonia Developing a CNN From Scratch for CIFAR-10 Photo Classification This course is ideal for individuals who are Anyone who wish to start the career in Data Science It is particularly useful for Anyone who wish to start the career in Data Science.

Enroll now: Professional Certificate in Data Science 2024

Summary

Title: Professional Certificate in Data Science 2024

Price: $54.99

Average Rating: 4.05

Number of Lectures: 177

Number of Published Lectures: 170

Number of Curriculum Items: 177

Number of Published Curriculum Objects: 170

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Python Programming Basics For Data Science

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

Evaluating the Machine Learning Algorithms : Precision, Recall, F-Measure, Confusion Matrices,

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.

Algorithm Analysis For Data Scientists

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

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

Deep Convolutional Generative Adversarial Networks (DCGAN)

Java Programming For Data Scientists

Kaggle – Covid 19- Classification (Chest X-ray.) – Covid-19 & Pneumonia

Developing a CNN From Scratch for CIFAR-10 Photo Classification

Who Should Attend

Anyone who wish to start the career in Data Science

Target Audiences

Anyone who wish to start the career in Data Science

At the end of the Course you will have all the skills to become a Data Science Professional.  (The most comprehensive Data Science course )

1) Python Programming Basics For Data Science – Python programming plays an important role in the field of Data Science

2) Introduction to Machine Learning – [A -Z] Comprehensive Training with Step by step guidance

3) Setting up the Environment for Machine Learning – Step by step guidance

4) Supervised Learning – (Univariate Linear regression, Multivariate Linear Regression, Logistic regression, Naive Bayes Classifier, Trees, Support Vector Machines, Random Forest)

5) Unsupervised Learning

6) Evaluating the Machine Learning Algorithms

7) Data Pre-processing

8) Algorithm Analysis For Data Scientists

9) Deep Convolutional Generative Adversarial Networks (DCGAN)

10) Java Programming For Data Scientists

Course Learning Outcomes

To provide awareness of the two most integral branches (Supervised & Unsupervised learning) coming under Machine Learning

Describe intelligent problem-solving methods via 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 solutions to resolve real-world problems by using appropriate Machine Learning techniques from a pool of techniques available.

To critically review and select the most appropriate machine learning solutions

To use ML evaluation methodologies to compare and contrast supervised and unsupervised ML algorithms using an established machine learning framework.

Beginners guide for python programming is also inclusive.

Introduction to Machine Learning – Indicative Module Content

Introduction to Machine Learning:-  What is  Machine Learning  ?,  Motivations for Machine Learning,  Why Machine Learning? Job Opportunities for Machine Learning

Setting up the Environment for Machine Learning:-Downloading & setting-up Anaconda, Introduction to Google Collabs

Supervised Learning Techniques:-Regression techniques, Bayer’s theorem, Na?ve Bayer’s, Support Vector Machines (SVM),  Decision Trees and Random Forest.

Unsupervised Learning Techniques:- Clustering, K-Means clustering

Artificial Neural networks [Theory and practical sessions – hands-on sessions]

Evaluation and Testing mechanisms :- Precision, Recall, F-Measure, Confusion Matrices,

Data Protection &  Ethical Principles

Setting up the Environment for Python Machine Learning

Understanding Data With Statistics & Data Pre-processing  (Reading data from file, Checking dimensions of Data, Statistical Summary of Data, Correlation between attributes)

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

The course will have step by step guidance for machine learning & Data Science with Python.

You can enhance your core programming skills to reach the advanced level. By the end of these videos, you will get the understanding of following areas the

Python Programming Basics For Data Science– Indicative Module Content

Python Programming

Setting up the environment

Python For Absolute Beginners : Setting up the Environment : Anaconda

Python For Absolute Beginners : Variables , Lists, Tuples , Dictionary

Boolean operations

Conditions , Loops

(Sequence , Selection, Repetition/Iteration)

Functions

File Handling in Python

Algorithm Analysis For Data Scientists

This section will provide a very basic knowledge about Algorithm Analysis. (Big O, Big Omega, Big Theta)

Java Programming for Data Scientists

Deep Convolutional Generative Adversarial Networks (DCGAN)

Generative Adversarial Networks (GANs) &  Deep Convolutional Generative Adversarial Networks (DCGAN)are one of the most interesting and trending ideas in computer science today. Two models are trained simultaneously by an adversarial process. A generator , learns to create images that look real, while a discriminator learns to tell real images apart from fakes.

At the end of this section you will understand the basics  of Generative Adversarial Networks (GANs) &  Deep Convolutional Generative Adversarial Networks (DCGAN) .

This  will have step by step guidance

Import TensorFlow and other libraries

Load and prepare the dataset

Create the models (Generator & Discriminator)

Define the loss and optimizers (Generator loss , Discriminator loss)

Define the training loop

Train the model

Analyze the output

Does the course get updated?

We  continually update the course as well.

What if you have questions?

we offer full support, answering any questions you have.

Who this course is for:

Beginners with no previous python programming experience looking to obtain the skills to get their first programming job.

Anyone looking to to build the minimum Python programming skills necessary as a pre-requisites for moving into machine learning, data science, and artificial intelligence.

Who want to improve their career options by learning the Python Data Engineering skills.

Course Curriculum

Chapter 1: Python Programming Basics For Data Science

Lecture 1: Downloading and Setting up Python and PyCharm IDE

Lecture 2: Python For Absolute Beginners : Setting up the Environment : Anaconda

Lecture 3: Python For Beginners : Variables : Part 1

Lecture 4: Python For Beginners : Variables : Part 2

Lecture 5: Python For Beginners : Variables : Part 3

Lecture 6: Python For Beginners – Lists

Lecture 7: Python For Beginners – Lists Part 2

Lecture 8: Python For Beginners – Lists Part 3

Lecture 9: Python – Conditions – if, if-else and elif Part 1

Lecture 10: Python – Conditions – if, if-else and elif Part 2

Lecture 11: Python – Relational Operators Boolean operators

Lecture 12: Python For beginners – Loops #Iteration

Lecture 13: Python Programming Tutorial : Loops part 1 #Guess the number program

Lecture 14: Python Programming Tutorial : Loops part 2 #Getting a random number

Lecture 15: Python Programming Tutorial : Loops part 1 #Guess the number program #Modified

Lecture 16: Python program to Find the Class Average

Lecture 17: Python : Functions : Demonstration

Lecture 18: Pass by reference vs value

Lecture 19: Python Function – Arguements (Required, Keyword, Default)

Lecture 20: Python: For Loops #Iteration # Repetition

Lecture 21: Python File Handling – Part 1

Lecture 22: Introduction to Software Design – Problem Solving

Lecture 23: Software Design – Flowcharts – Sequence

Lecture 24: Software Design – Repetition

Lecture 25: Flowcharts Questions and Answers # Problem Solving

Lecture 26: Add two numbers

Lecture 27: Selection Sort Algorithm

Lecture 28: Bubble Sort Algorithm

Lecture 29: Python hands-On Tutorial 1

Lecture 30: Python hands-On – Tutorial 2 – Built-In Functions

Lecture 31: Tutorial 3 – if conditions

Lecture 32: Tutorial 4 – while loops

Lecture 33: Our Youtube Free content

Chapter 2: Introduction to Machine Learning

Lecture 1: Motivations for Machine Learning

Lecture 2: Why Machine Learning

Chapter 3: Setting up the Environment for Machine Learning

Lecture 1: Downloading and Setting up Anaconda for Machine Learning

Lecture 2: Introduction to Google Colabs

Chapter 4: Supervised Learning

Lecture 1: Univariate Linear regression Part 1

Lecture 2: Univariate Linear regression Part 2

Lecture 3: Multivariate Linear Regression

Lecture 4: Logistic regression

Lecture 5: Naive Bayes Classifier

Lecture 6: Trees

Lecture 7: SVM

Lecture 8: Support Vector Machines – Hands – On with Google Colabs

Lecture 9: Decision Trees – Hands – On with Google Collabs

Lecture 10: Random Forest – Hands – On with Google Collabs

Chapter 5: Unsupervised Learning

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 – Code walkthrough with Theory & Practical

Chapter 6: Artificial Neural Networks

Lecture 1: Introduction to Artificial Neural Networks

Chapter 7: Data Pre-processing

Lecture 1: Data Pre-processing – Scaling with a demonstration in python

Lecture 2: Data Pre-processing – Normalization , Binarization , Standardization in Python

Lecture 3: Feature Selection Techniques : Univariate Selection

Chapter 8: Real world projects [Hands-on]

Lecture 1: SVM-Hands On

Lecture 2: Trees Hands On.

Lecture 3: Random Forest – Hands – On with Google Collabs

Chapter 9: Algorithm Analysis For Data Scientists

Lecture 1: Algorithms : Introduction to Algorithms

Lecture 2: Entering the World of Algorithms

Lecture 3: Algorithms , Flowcharts & Pseudocodes

Lecture 4: Algorithms : Dynamic Connectivity

Lecture 5: Algorithms : Dynamic Connectivity part 2

Lecture 6: Algorithms : Quick-Find [Eager Approach]

Lecture 7: Algorithms : Quick-Find Demo [Example from Princeton Uni]

Lecture 8: Algorithms : QuickFind – Part 1

Lecture 9: Algorithms : QuickFind – Part 2

Lecture 10: Algorithm Analysis – Part 1

Lecture 11: Algorithm Analysis – Part 2 [Theoretical Analysis & Big O Notation ]

Lecture 12: Algorithm Analysis – Part 3 Big O Arithmetic

Lecture 13: Sum of 3 problem and solution

Lecture 14: Selection Sort Algorithm

Lecture 15: Big O, Big Omega, and Big Theta Notation Lecture / Tutorial – Part 1

Lecture 16: Big O, Big Omega, and Big Theta Notation Lecture / Tutorial – Part 2

Lecture 17: Big O, Big Omega, and Big Theta Notation Lecture / Tutorial – Part 3

Lecture 18: Big O, Big Omega, and Big Theta Notation Lecture / Tutorial – Part 4

Lecture 19: Big O, Big Omega, and Big Theta Notation Lecture / Tutorial – Part 5

Chapter 10: MIT Introduction to Deep Learning – Guest Lecture – Online

Lecture 1: Introduction to Deep Learning

Lecture 2: Recurrent Neural Networks | MIT

Lecture 3: Convolutional Neural Networks

Lecture 4: Deep Generative Modeling | MIT

Chapter 11: Deep Convolutional Generative Adversarial Networks (DCGAN)

Lecture 1: What are GANs ? Generative Adversarial Networks (GANs)

Lecture 2: Import TensorFlow and other libraries

Lecture 3: Load and prepare the dataset

Lecture 4: Create the models – The Generator

Lecture 5: Create the models – The Discriminator

Lecture 6: Define the loss and optimizers

Lecture 7: Define the training loop

Lecture 8: Train the model – Part

Instructors

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

Rating Distribution

1 stars: 10 votes

2 stars: 7 votes

3 stars: 16 votes

4 stars: 33 votes

5 stars: 88 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!