Machine Learning with Python

 

What you'll learn

Describe the various types of Machine Learning algorithms and when to use them  

Compare and contrast linear classification methods including multiclass prediction, support vector machines, and logistic regression  

Write Python code that implements various classification techniques including K-Nearest neighbors (KNN), decision trees, and regression trees 

Evaluate the results from simple linear, non-linear, and multiple regression on a data set using evaluation metrics  

 There are 6 modules in this course

Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to advance your Data Science career or get started in Machine Learning and Deep Learning.  

This course will begin with a gentle introduction to Machine Learning and what it is, with topics like supervised vs unsupervised learning, linear & non-linear regression, simple regression and more.  

You will then dive into classification techniques using different classification algorithms, namely K-Nearest Neighbors (KNN), decision trees, and Logistic Regression. You’ll also learn about the importance and different types of clustering such as k-means, hierarchical clustering, and DBSCAN. 

With all the many concepts you will learn, a big emphasis will be placed on hands-on learning. You will work with Python libraries like SciPy and scikit-learn and apply your knowledge through labs. In the final project you will demonstrate your skills by building, evaluating and comparing several Machine Learning models using different algorithms.  

By the end of this course, you will have job ready skills to add to your resume and a certificate in machine learning to prove your competency.

Join free - Machine Learning with Python

Read More

IBM Full Stack Software Developer Professional Certificate

 

Prepare for a career as a full stack developer. Gain the in-demand skills and hands-on experience to get job-ready in less than 4 months. No prior experience required.

What you'll learn

Master the most up-to-date practical skills and tools that full stack developers use in their daily roles

Learn how to deploy and scale applications using Cloud Native methodologies and tools such as Containers, Kubernetes, Microservices, and Serverless

Develop software with front-end development languages and tools such as HTML, CSS, JavaScript, React, and Bootstrap

Build your GitHub portfolio by applying your skills to multiple labs and hands-on projects, including a capstone

Professional Certificate - 12 course series

Prepare for a career in the high-growth field of software development. In this program, you’ll learn in-demand skills and tools used by professionals for front-end, back-end, and cloud native application development to get job-ready in less than 4 months, with no prior experience needed. 

Full stack refers to the end-to-end computer system application, including the front end and back end coding. This Professional Certificate covers development for both of these scenarios. Cloud native development refers to developing a program designed to work on cloud architecture. The flexibility and adaptability that full stack and cloud native developers provide make them highly sought after in this digital world. 

You’ll  learn how to build, deploy, test, run, and manage full stack cloud native applications. Technologies covered includes Cloud foundations, GitHub, Node.js, React, CI/CD, Containers, Docker, Kubernetes, OpenShift, Istio, Databases, NoSQL, Django ORM, Bootstrap, Application Security, Microservices, Serverless computing, and more. 

After completing the program you will have developed several applications using front-end and back-end technologies and deployed them on a cloud platform using Cloud Native methodologies. You will publish these projects through your GitHub repository to share your portfolio with your peers and prospective employers.

This program is ACE® recommended—when you complete, you can earn up to 18 college credits.

Applied Learning Project

Throughout the courses in the Professional Certificate, you will develop a portfolio of hands-on projects involving various popular technologies and programming languages in Full Stack Cloud Application Development. These projects include creating:

HTML pages on Cloud Object Storage

An interest rate calculator using HTML, CSS, and JavaScript

An AI program deployed on Cloud Foundry using DevOps principles and CI/CD toolchains with a NoSQL database

A Node.js back-end application and a React front-end application

A containerized guestbook app packaged with Docker deployed with Kubernetes and managed with OpenShift

A Python app bundled as a package

A database-powered application using Django ORM and Bootstrap

An app built using Microservices & Serverless

A scalable, Cloud Native Full Stack application using the technologies learned in previous courses

You will publish these projects through your GitHub repository to share your skills with your peers and prospective employers.

Join - IBM Full Stack Software Developer Professional Certificate

Read More

Object-Oriented Python: Inheritance and Encapsulation

 

What you'll learn

How to architect larger programs using object-oriented principles

Re-use parts of classes using inheritance

Encapsulate relevant information and methods in a class

There are 4 modules in this course

Code and run your first python program in minutes without installing anything!

This course is designed for learners with limited coding experience, providing a solid foundation of not just python, but core Computer Science topics that can be transferred to other languages. The modules in this course cover inheritance, encapsulation, polymorphism, and other object-related topics. Completion of the prior 3 courses in this specialization is recommended.

To allow for a truly hands-on, self-paced learning experience, this course is video-free. Assignments contain short explanations with images and runnable code examples with suggested edits to explore code examples further, building a deeper understanding by doing. You'll benefit from instant feedback from a variety of assessment items along the way, gently progressing from quick understanding checks (multiple choice, fill in the blank, and un-scrambling code blocks) to small, approachable coding exercises that take minutes instead of hours.

Join free - Object-Oriented Python: Inheritance and Encapsulation

Read More

Computers, Waves, Simulations: A Practical Introduction to Numerical Methods using Python (Free Course)

 

What you'll learn

How to solve a partial differential equation using the finite-difference, the pseudospectral, or the linear (spectral) finite-element method.

Understanding the limits of explicit space-time simulations due to the stability criterion and spatial and temporal sampling requirements.

Strategies how to plan and setup sophisticated simulation tasks.

Strategies how to avoid errors in simulation results. 

There are 9 modules in this course

Interested in learning how to solve partial differential equations with numerical methods and how to turn them into python codes? This course provides you with a basic introduction how to apply methods like the finite-difference method, the pseudospectral method, the linear and spectral element method to the 1D (or 2D) scalar wave equation. The mathematical derivation of the computational algorithm is accompanied by python codes embedded in Jupyter notebooks. In a unique setup you can see how the mathematical equations are transformed to a computer code and the results visualized. The emphasis is on illustrating the fundamental mathematical ingredients of the various numerical methods (e.g., Taylor series, Fourier series, differentiation, function interpolation, numerical integration) and how they compare. You will be provided with strategies how to ensure your solutions are correct, for example benchmarking with analytical solutions or convergence tests. The mathematical aspects are complemented by a basic introduction to wave physics, discretization, meshes, parallel programming, computing models. 

The course targets anyone who aims at developing or using numerical methods applied to partial differential equations and is seeking a practical introduction at a basic level. The methodologies discussed are widely used in natural sciences,  engineering, as well as economics and other fields. 

Join - Computers, Waves, Simulations: A Practical Introduction to Numerical Methods using Python

Read More

Programming for Everybody (Getting Started with Python)

 

What you'll learn

Install Python and write your first program

Describe the basics of the Python programming language

Use variables to store, retrieve and calculate information

Utilize core programming tools such as functions and loops

There are 7 modules in this course

This course aims to teach everyone the basics of programming computers using Python. We cover the basics of how one constructs a program from a series of simple instructions in Python.  The course has no pre-requisites and avoids all but the simplest mathematics. Anyone with moderate computer experience should be able to master the materials in this course. This course will cover Chapters 1-5 of the textbook “Python for Everybody”.  Once a student completes this course, they will be ready to take more advanced programming courses. This course covers Python 3.

Join - Programming for Everybody (Getting Started with Python)

Read More

Computer Vision with Embedded Machine Learning (Free Course)

 

What you'll learn

How to train and develop an image classification system using machine learning

How to train and develop an object detection system using machine learning

How to deploy a machine learning model to a microcontroller

There are 3 modules in this course

Computer vision (CV) is a fascinating field of study that attempts to automate the process of assigning meaning to digital images or videos. In other words, we are helping computers see and understand the world around us! A number of machine learning (ML) algorithms and techniques can be used to accomplish CV tasks, and as ML becomes faster and more efficient, we can deploy these techniques to embedded systems.

This course, offered by a partnership among Edge Impulse, OpenMV, Seeed Studio, and the TinyML Foundation, will give you an understanding of how deep learning with neural networks can be used to classify images and detect objects in images and videos. You will have the opportunity to deploy these machine learning models to embedded systems, which is known as embedded machine learning or TinyML.

Familiarity with the Python programming language and basic ML concepts (such as neural networks, training, inference, and evaluation) is advised to understand some topics as well as complete the projects. Some math (reading plots, arithmetic, algebra) is also required for quizzes and projects. If you have not done so already, taking the "Introduction to Embedded Machine Learning" course is recommended.

This course covers the concepts and vocabulary necessary to understand how convolutional neural networks (CNNs) operate, and it covers how to use them to classify images and detect objects. The hands-on projects will give you the opportunity to train your own CNNs and deploy them to a microcontroller and/or single board computer. 

Join free - Computer Vision with Embedded Machine Learning

Read More

Python for Everybody Specialization

 

Learn to Program and Analyze Data with Python. Develop programs to gather, clean, analyze, and visualize data.

Specialization - 5 course series

This Specialization builds on the success of the Python for Everybody course and will introduce fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language. In the Capstone Project, you’ll use the technologies learned throughout the Specialization to design and create your own  applications for data retrieval, processing, and visualization.

Join - Python for Everybody Specialization

Read More

Python Programming for Beginners: An Introduction to the Python Computer Language and Computer Programming

 

If you want to learn how to program in Python, but don't know where to start read on.

Knowing where to start when learning a new skill can be a challenge, especially when the topic seems so vast. There can be so much information available that you can't even decide where to start. Or worse, you start down the path of learning and quickly discover too many concepts, commands, and nuances that aren't explained. This kind of experience is frustrating and leaves you with more questions than answers.

Python Programming for Beginners doesn't make any assumptions about your background or knowledge of Python or computer programming. You need no prior knowledge to benefit from this book. You will be guided step by step using a logical and systematic approach. As new concepts, commands, or jargon are encountered they are explained in plain language, making it easy for anyone to understand.

Here is what you will learn by listening to Python Programming for Beginners:

• When to use Python 2 and when to use Python 3.
• How to install Python on Windows, Mac, and Linux. Screenshots included.
• How to prepare your computer for programming in Python.
• The various ways to run a Python program on Windows, Mac, and Linux.
• Suggested text editors and integrated development environments to use when coding in Python.
• How to work with various data types including strings, lists, tuples, dictionaries, booleans, and more.
• What variables are and when to use them.
• How to perform mathematical operations using Python.
• How to capture input from a user.
• Ways to control the flow of your programs.
• The importance of white space in Python.
• How to organize your Python programs - Learn what goes where.
• What modules are, when you should use them, and how to create your own.
• How to define

Get it - Python Programming for Beginners: An Introduction to the Python Computer Language and Computer Programming

Read More

Learn to Program: The Fundamentals (Free Course)

 

There are 7 modules in this course

Behind every mouse click and touch-screen tap, there is a computer program that makes things happen. This course introduces the fundamental building blocks of programming and teaches you how to write fun and useful programs using the Python language.

This module gives an overview of the course, the editor we will use to write programs, and an introduction to fundamental concepts in Python including variables, mathematical expressions, and functions.

Join Free- Learn to Program: The Fundamentals

Read More

Getting Started With Game Development Using PyGame

 

About this Guided Project

In this 1-hour long project-based course, you will learn how to create a basic single-player Pong replica using the PyGame library for Python, creating a welcome screen, a game that responds to user input to move the paddle, scoring, and a game over screen with user options. By the end of the course, learners will have a basic understanding of the PyGame library and will be able to create simple games built on shapes. No previous experience with PyGame is required, as this is a basic introduction to the library, but familiarity with Python is recommended.

Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

Create the display and the ball

Add motion to the ball and a paddle

Detect collisions and create a Game Over screen

Expand game play options

Reset the game to continue play

Scorekeeping, randomizing, and expansion options

Join  - Getting Started With Game Development Using PyGame

Read More

Python Project for Data Science

 

What you'll learn

Play the role of a Data Scientist / Data Analyst working on a real project.

Demonstrate your Skills in Python - the language of choice for Data Science and Data Analysis. 

Apply Python fundamentals, Python data structures, and working with data in Python.

Build a dashboard using Python and libraries like Pandas, Beautiful Soup and Plotly using Jupyter notebook.

There is 1 module in this course

This mini-course is intended to for you to demonstrate foundational Python skills for working with data. This course primarily involves completing a project in which you will assume the role of a Data Scientist or a Data Analyst and be provided with a real-world data set and a real-world inspired scenario to identify patterns and trends. 

You will perform specific data science and data analytics tasks such as extracting data, web scraping, visualizing data and creating a dashboard. This project will showcase your proficiency with Python and using libraries such as Pandas and Beautiful Soup within a Jupyter Notebook. Upon completion you will have an impressive project to add to your job portfolio.   

PRE-REQUISITE: **Python for Data Science, AI and Development** course from IBM is a pre-requisite for this project course. Please ensure that before taking this course you have either completed the Python for Data Science, AI and Development course from IBM or have equivalent proficiency in working with Python and data.  

NOTE: This course is not intended to teach you Python and does not have too much instructional content. It is intended for you to apply prior Python knowledge.

Join - Python Project for Data Science

Read More

Machine Learning Basics (Free Course)

 

There are 4 modules in this course

In this course, you will:   

a) understand the basic concepts of machine learning.

b) understand a typical memory-based method, the K nearest neighbor method.

c) understand linear regression.

d) understand model analysis.

Please make sure that you’re comfortable programming in Python and have a basic knowledge of mathematics including matrix multiplications, and conditional probability.

JOIN Free - Machine Learning Basics

Read More

Problem Solving, Python Programming, and Video Games (Free Course)

 

There are 12 modules in this course

This course is an introduction to computer science and programming in Python.  Upon successful completion of this course, you will be able to:

1.  Take a new computational problem and solve it, using several problem solving techniques including abstraction and problem decomposition.

2.  Follow a design creation process that includes: descriptions, test plans, and algorithms.

3.  Code, test, and debug a program in Python, based on your design.

Important computer science concepts such as problem solving (computational thinking), problem decomposition, algorithms, abstraction, and software quality are emphasized throughout.

This course uses problem-based learning. The Python programming language and video games are used to demonstrate computer science concepts in a concrete and fun manner. The instructional videos present Python using a conceptual framework that can be used to understand any programming language. This framework is based on several general programming language concepts that you will learn during the course including: lexics, syntax, and semantics.

Other approaches to programming may be quicker, but are more focused on a single programming language, or on a few of the simplest aspects of programming languages. The approach used in this course may take more time, but you will gain a deeper understanding of programming languages. After completing the course,  in addition to learning Python programming, you will be able to apply the knowledge and skills you acquired to: non-game problems, other programming languages, and other computer science courses.

You do not need any previous programming, Python, or video game experience.  However, several basic skills are needed: computer use (e.g., mouse, keyboard, document editing), elementary mathematics, attention to detail (as with many technical subjects), and a “just give it a try” spirit will be keys to your success.  Despite the use of video games for the main programming project, PVG is not about computer games.  For each new programming concept, PVG uses non-game examples to provide a basic understanding of computational principles, before applying these programming concepts to video games.

The interactive learning objects (ILO) of the course provide automatic, context-specific guidance and feedback, like a virtual teaching assistant, as you develop problem descriptions, functional test plans, and algorithms.  The course forums are supported by knowledgeable University of Alberta personnel, to help you succeed.

All videos, assessments, and ILOs are available free of charge.  There is an optional Coursera certificate available for a fee. 

JOIN Free  - Problem Solving, Python Programming, and Video Games

Read More

Data Structures and Algorithms Specialization

 

Master Algorithmic Programming Techniques. Advance your Software Engineering or Data Science Career by Learning Algorithms through Programming and Puzzle Solving. Ace coding interviews by implementing each algorithmic challenge in this Specialization. Apply the newly-learned algorithmic techniques to real-life problems, such as analyzing a huge social network or sequencing a genome of a deadly pathogen.

What you'll learn

Play with 50 algorithmic puzzles on your smartphone to develop your algorithmic intuition!  Apply algorithmic techniques (greedy algorithms, binary search, dynamic programming, etc.) and data structures (stacks, queues, trees, graphs, etc.) to solve 100 programming challenges that often appear at interviews at high-tech companies. Get an instant feedback on whether your solution is correct.

Apply the newly learned algorithms to solve real-world challenges: navigating in a Big Network  or assembling a genome of a deadly pathogen from millions of short substrings of its DNA.

Learn exactly the same material as undergraduate students in “Algorithms 101” at top universities and more! We are excited that students from various parts of the world are now studying our online materials in the Algorithms 101 classes at their universities. Here is a quote from the website of Professor 

If you decide to venture beyond Algorithms 101, try to solve more complex programming challenges (flows in networks, linear programming, streaming algorithms, etc.) and complete an equivalent of a graduate course in algorithms!

Specialization - 6 course series

Computer science legend Donald Knuth once said “I don’t understand things unless I try to program them.” We also believe that the best way to learn an algorithm is to program it. However, many excellent books and online courses on algorithms, that excel in introducing algorithmic ideas, have not yet succeeded in teaching you how to implement algorithms, the crucial computer science skill that you have to master at your next job interview. We tried to fill this gap by forming a diverse team of instructors that includes world-leading experts in theoretical and applied algorithms at UCSD (Daniel Kane, Alexander Kulikov, and Pavel Pevzner) and a former software engineer at Google (Neil Rhodes). This unique combination of skills makes this Specialization different from other excellent MOOCs on algorithms that are all developed by theoretical computer scientists. While these MOOCs focus on theory, our Specialization is a mix of algorithmic theory/practice/applications with software engineering. You will learn algorithms by implementing nearly 100 coding problems in a programming language of your choice. To the best of knowledge, no other online course in Algorithms comes close to offering you a wealth of programming challenges (and puzzles!) that you may face at your next job interview. We invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs.  

Applied Learning Project

The specialization contains two real-world projects: Big Networks and Genome Assembly. You will analyze both road networks and social networks and will learn how to compute the shortest route between New York and San Francisco 1000 times faster than the shortest path algorithms you learn in the standard Algorithms 101 course! Afterwards, you will learn how to assemble genomes from millions of short fragments of DNA and how assembly algorithms fuel recent developments in personalized medicine.

JOIN free - Data Structures and Algorithms Specialization

Read More

Introduction to Embedded Machine Learning (Free Course)

 

What you'll learn

The basics of a machine learning system

How to deploy a machine learning model to a microcontroller

How to use machine learning to make decisions and predictions in an embedded system

There are 3 modules in this course

Machine learning (ML) allows us to teach computers to make predictions and decisions based on data and learn from experiences. In recent years, incredible optimizations have been made to machine learning algorithms, software frameworks, and embedded hardware. Thanks to this, running deep neural networks and other complex machine learning algorithms is possible on low-power devices like microcontrollers.

This course will give you a broad overview of how machine learning works, how to train neural networks, and how to deploy those networks to microcontrollers, which is known as embedded machine learning or TinyML. You do not need any prior machine learning knowledge to take this course. Familiarity with Arduino and microcontrollers is advised to understand some topics as well as to tackle the projects. Some math (reading plots, arithmetic, algebra) is also required for quizzes and projects.

We will cover the concepts and vocabulary necessary to understand the fundamentals of machine learning as well as provide demonstrations and projects to give you hands-on experience.

Join Free - Introduction to Embedded Machine Learning

Read More

Algorithms, Part I (Free Course)

 

There are 13 modules in this course

This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.

All the features of this course are available for free.  It does not offer a certificate upon completion.

Join Free  - Algorithms, Part I

Read More

Generative AI with Large Language Models (Free Course)

 

What you'll learn

Gain foundational knowledge, practical skills, and a functional understanding of how generative AI works

Dive into the latest research on Gen AI to understand how companies are creating value with cutting-edge technology

Instruction from expert AWS AI practitioners who actively build and deploy AI in business use-cases today

Skills you'll gain

Generative AI

LLMs

large language models

Machine Learning

Python Programming

There are 3 modules in this course

In Generative AI with Large Language Models (LLMs), you’ll learn the fundamentals of how generative AI works, and how to deploy it in real-world applications.

By taking this course, you'll learn to:

- Deeply understand generative AI, describing the key steps in a typical LLM-based generative AI lifecycle, from data gathering and model selection, to performance evaluation and deployment

- Describe in detail the transformer architecture that powers LLMs, how they’re trained, and how fine-tuning enables LLMs to be adapted to a variety of specific use cases

- Use empirical scaling laws to optimize the model's objective function across dataset size, compute budget, and inference requirements

- Apply state-of-the art training, tuning, inference, tools, and deployment methods to maximize the performance of models within the specific constraints of your project 

- Discuss the challenges and opportunities that generative AI creates for businesses after hearing stories from industry researchers and practitioners

Developers who have a good foundational understanding of how LLMs work, as well the best practices behind training and deploying them, will be able to make good decisions for their companies and more quickly build working prototypes. This course will support learners in building practical intuition about how to best utilize this exciting new technology.

This is an intermediate course, so you should have some experience coding in Python to get the most out of it. You should also be familiar with the basics of machine learning, such as supervised and unsupervised learning, loss functions, and splitting data into training, validation, and test sets. If you have taken the Machine Learning Specialization or Deep Learning Specialization from DeepLearning.AI, you’ll be ready to take this course and dive deeper into the fundamentals of generative AI.

Join Free  - Generative AI with Large Language Models

Read More

IBM Data Science Professional Certificate

 

There are 10 courses in this program

• Course 1: What is Data Science?

• Course 2: Tools for Data Science

• Course 3: Data Science Methodology

• Course 4: Python for Data Science, AI & Development

• Course 5: Python Project for Data Science

• Course 6: Databases and SQL for Data Science with Python

• Course 7: Data Analysis with Python

• Course 8: Data Visualization with Python

• Course 9: Machine Learning with Python

• Course 10: Applied Data Science Capstone

Read More

Introduction to Image Generation (Free Course)

 

What you'll learn

How diffusion models work

Real use-cases for diffusion models

Unconditioned diffusion models

Advancements in diffusion models (text-to-image)

There is 1 module in this course

This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI. 

JOIN FREE  - Introduction to Image Generation

Read More

Free Python and Statistics for Financial Analysis

 

There are 4 modules in this course

Python is now becoming the number 1 programming language for data science. Due to python’s simplicity and high readability, it is gaining its importance in the financial industry.  The course combines both python coding and statistical concepts and applies into analyzing financial data, such as stock data.

By the end of the course, you can achieve the following using python:

- Import, pre-process, save and visualize financial data into pandas Dataframe

- Manipulate the existing financial data by generating new variables using multiple columns

- Recall and apply the important statistical concepts (random variable, frequency, distribution, population and sample, confidence interval, linear regression, etc. ) into financial contexts

- Build a trading model using multiple linear regression model 

- Evaluate the performance of the trading model using different investment indicators

Jupyter Notebook environment is configured in the course platform for practicing python coding without installing any client applications.

JOIN  - Python and Statistics for Financial Analysis

Read More