Udacity is a platform for lifelong learners to learn the skills they need, secure the jobs they want, and create the lives they deserve. The demand for online education is growing daily. With Udacity, you can learn anything you want, anytime you want, from the comfort of your own home. If you are interested in learning more about Udacity, you can check out this article or their website for more information.
Udacity provides its prospective learners all over the world with the opportunity to engage in — and contribute to — some of the world’s most fascinating and creative fields. Here are some of the best free courses on Udacity that you can do to kick start your career:😄✨
List of Best Free Udacity Courses Online
1. Intro to Machine Learning using Microsoft Azure:
Learn about machine learning at a high level and get ready to utilise Azure Machine Learning Studio to train machine learning models. In addition, learn how to use Azure Machine Learning labs to conduct a number of tasks, such as data import, transformation, and management, as well as training, validating, and assessing models.
USPs:
- Rich Learning Content
- Interactive QuizzesInteractive Quizzes
- Taught by Industry ProsTaught by Industry Pros
- Self-Paced LearningSelf-Paced Learning
Course Cost: Free
Timeline: Approx. 2 Months
Skill Level: Intermediate
What You Will Learn:
- Introduction to Machine Learning
- Model Training
- Supervised & Unsupervised Learning
- Applications of Machine Learning
- Managed Services for Machine Learning
- Responsible AI
Prerequisites and Requirements:
The Introduction to Machine Learning with Microsoft Azure course is designed for students with prior programming experience in any language, especially Python, and who are comfortable building scripts. Basic statistics expertise will also be useful for deploying the Machine Learning models in this course.
Why Take This Course:
This course is the first step toward launching a new career in Machine Learning with Microsoft Azure. As more services migrate to the cloud, the demand for machine learning expertise in cloud infrastructure grows. In reality, cloud computing is used by approximately 90% of businesses. Microsoft Azure is a top cloud provider for Fortune 500 firms, and this online course seeks to help a new generation of machine learning practitioners advance their careers.
2. Linear Algebra Refresher Course:
This mini-course is designed for those who need a refresher on the fundamentals of linear algebra. In addition to “what” linear algebra is, the course strives to provide a rationale for “why” linear algebra is significant.
Students will learn linear algebra concepts by putting them to use in computer applications. You will have coded your own personal library of linear algebra functions by the end of the course, which you can use to address real-world problems.
USPs:
- Rich Learning Content
- Interactive QuizzesInteractive Quizzes
- Taught by Industry ProsTaught by Industry Pros
- Self-Paced LearningSelf-Paced Learning
Course Cost: Free
Timeline: Approx. 4 Months
Skill Level: Intermediate
What You Will Learn:
- Vectors
- Intersection
Prerequisites and Requirements:
Except for prior expertise with a programming language, there are no formal prerequisites for this course. Working knowledge of high school algebra and trigonometry will also be beneficial.
Why Take This Course:
You must enrol in this course if:
- You want to brush up on the fundamentals of linear algebra or learn them for the first time.
- You’d like to see how linear algebra can be used to solve real-world situations.
- You wish to learn linear algebra in a programming setting.
3. AI Fundamentals:
This course provides an introduction to the field of artificial intelligence (AI) by utilising Microsoft’s cloud-based technologies, such as Azure Machine Learning and Azure Cognitive Services. You will get the opportunity to learn and experience firsthand how to train and deliver machine learning models, as well as how to leverage Azure Cognitive Services for common AI workloads like computer vision, natural language processing, and conversational AI.
USPs:
- Rich Learning Content
- Interactive QuizzesInteractive Quizzes
- Taught by Industry ProsTaught by Industry Pros
- Self-Paced LearningSelf-Paced Learning
Course Cost: Free
Timeline: Approx. 1 Month
Skill Level: Beginner
What You Will Learn:
- Introduction to AI Fundamentals with Azure
- AI and ML Core Concepts
- Machine Learning
- Computer Vision
- Natural Language Processing
- Conversational AI
Prerequisites and Requirements:
Although no prior experience with ML or AI is required, students enrolled in this course should already:
- Understand the fundamentals of linear algebra, probability, and statistics.
- Understand the fundamentals of Python.
- Have prior experience configuring Azure computing resources (recommended but not required)
- Understand fundamental programming concepts such as conditional and loop statements in any language, particularly Python.
- Have a rudimentary understanding of statistics, such as calculating standard deviation and utilising linear regression methods.
- Have a basic understanding of machine learning (recommended but not required)
Why Take This Course:
AI is a burgeoning subject, and as more businesses implement these tactics, the skills gap grows and job openings increase. Udacity will teach you the fundamentals of AI without requiring any prior experience of AI or machine learning. This course can serve as a stepping stone to more advanced AI training and well-paying employment.
4. Machine Learning: Unsupervised Learning:
Unsupervised Learning is closely similar to pattern recognition in that it involves evaluating data and looking for patterns. It is a very effective method for detecting structure in data. This course focuses on how to uncover structure in unlabeled data using Unsupervised Learning methodologies such as randomised optimization, clustering, and feature selection and transformation.
USPs:
- Rich Learning Content
- Interactive QuizzesInteractive Quizzes
- Taught by Industry ProsTaught by Industry Pros
- Self-Paced LearningSelf-Paced Learning
Course Cost: Free
Timeline: Approx. 1 Month
Skill Level: Intermediate
What You Will Learn:
- Randomized optimization
- Clustering
- Feature Selection
- Feature Transformation
- Information Theory
- Unsupervised Learning Project
Prerequisites and Requirements:
This class will assume you have prior programming knowledge because you will be working with Python modules such as NumPy and scikit. A solid understanding of probability and statistics is also essential. Udacity’s Intro to Statistics, particularly Lessons 8 and 9, may serve as a good reminder.
An introductory course, such as Udacity’s Introduction to Artificial Intelligence, is also a good starting point for this course.
Why Take This Course:
Unsupervised Learning techniques such as randomised optimization, clustering, feature selection and transformation, and information theory will be discussed and practised.
In this course, you will learn important Machine Learning concepts, techniques, and best practices, as well as acquire hands-on experience implementing them through a hands-on final project in which you will create a movie recommendation system (exactly like Netflix!).
5. Big Data Analytics in Healthcare:
Many sectors rely heavily on data science. Scalable machine learning and data mining methods and systems become critical for data scientists when confronted with enormous amounts of heterogeneous data.
Large amounts of disparate medical data have become available in many healthcare institutions (payers, providers, pharmaceuticals). This data could be a valuable resource for gaining insights into how to improve medical delivery and reduce waste. The size and complexity of these datasets provide significant obstacles in analysis and subsequent application to a therapeutic setting.
USPs:
- Rich Learning Content
- Interactive QuizzesInteractive Quizzes
- Taught by Industry ProsTaught by Industry Pros
- Self-Paced LearningSelf-Paced Learning
Course Cost: Free
Timeline: Approx. 0
Skill Level: Intermediate
What You Will Learn:
- Big Data
- Healthcare
- Technologies
Prerequisites and Requirements:
- Classification and clustering are fundamental machine learning and data mining concepts.
- Python, Java, and Scala programming and system expertise are required.
- Knowledge and expertise dealing with data are required (recommended skills include SQL, NoSQL such as MongoDB).
Why Take This Course:
In this course, we will discuss the properties of medical data as well as the data mining issues connected with dealing with such data. We discuss numerous big data analytics algorithms and platforms. We are interested in investigating big data techniques in the context of specific healthcare analytic applications such as predictive modelling, computational phenotyping, and patient similarity. We also look into big data analytics technology:
Machine learning methods that are scalable, such as online learning and quick similarity search;
Hadoop family (Hive, Pig, HBase), Spark, and Graph DB are examples of big data analysis systems.
6. Intro to Descriptive Statistics:
Statistics is a branch of mathematics that is used to analyse, evaluate, and forecast data results. Descriptive statistics will teach you the fundamental ideas of data description. This is an excellent introductory course for anyone interested in Data Science, Economics, Psychology, Machine Learning, Sports Analytics, or any other discipline.
USPs:
- Rich Learning Content
- Interactive QuizzesInteractive Quizzes
- Taught by Industry ProsTaught by Industry Pros
- Self-Paced LearningSelf-Paced Learning
Course Cost: Free
Timeline: Approx. 2 Months
Skill Level: Beginner
What You Will Learn:
- Intro to Research Methods
- Visualizing Data
- Central Tendency
- Variability
- Standardizing
- Normal Distribution
- Sampling Distributions
Prerequisites and Requirements:
This course assumes an understanding of basic algebra and arithmetic.
Why Take This Course:
This course will teach you the fundamental vocabulary and concepts of statistics, as well as walk you through the basics of probability.
You will discover how to….
- Make use of statistical research techniques.
- Values such as Mean, Median, Mode, Sample, Population, and Standard Deviation must be computed and interpreted.
- Simple probabilities should be computed.
- Investigate data with bar graphs, histograms, box plots, and other standard representations.
- Investigate distributions and learn about their properties.
- To create probabilistic predictions on data, manipulate distributions.
7. Intel® Edge AI Fundamentals with OpenVINO™:
Maintain your position at the forefront of AI technology by learning practical skills for deploying cutting-edge AI. Learn how to leverage the OpenVINOTM toolkit’s Intel® Distribution to deploy computer vision capabilities in a variety of edge applications. Use the Intel® Distribution of the OpenVINOTM toolbox to accelerate the creation of high-performance computer vision and deep learning inference applications.
USPs:
- Rich Learning Content
- Interactive QuizzesInteractive Quizzes
- Taught by Industry ProsTaught by Industry Pros
- Self-Paced LearningSelf-Paced Learning
Course Cost: Free
Timeline: Approx. 1 Month
Skill Level: Intermediate
What You Will Learn:
- Leveraging Pre-Trained Models
- The Model Optimizer
- The Inference Engine
- Deploying an Edge App
Prerequisites and Requirements:
Python knowledge is required. Basic knowledge of computer vision and AI model construction.
Why Take This Course:
This curriculum will teach students how to use some of today’s most cutting-edge technologies. The course will expose students to the Intel® Distribution of the OpenVINOTM Toolkit, which enables developers to deploy pre-trained deep learning models via a high-level C++ or Python inference engine API coupled with application logic. The toolbox, which is based on convolutional neural networks (CNN), extends workloads across Intel® hardware (including accelerators) and maximises performance.
8. Artificial Intelligence:
In this introductory graduate-level course, you will learn the principles of artificial intelligence. It covers a wide range of subjects in the discipline, as well as an in-depth study of core ideas such as classical search, probability, machine learning, logic, and planning.
USPs:
- Rich Learning Content
- Interactive QuizzesInteractive Quizzes
- Taught by Industry ProsTaught by Industry Pros
- Self-Paced LearningSelf-Paced Learning
Course Cost: Free
Timeline: Approx. 4 Months
Skill Level: Intermediate
What You Will Learn:
- Search
- Constraints and Bayes Nets
- Basics of Machine Learning
Prerequisites and Requirements:
Courses in undergraduate computer algorithm and data structure that encompass O notation, time and space limitations; working understanding of college-level mathematics such as calculus, probability, and linear algebra. You must also be familiar with Python and be comfortable making changes to huge projects.
Please go through the following questions again, and refresh your knowledge and gain more skills if you respond “no” to any of them:
- Are you familiar with Python programming, especially IPython notebooks? If not, do you think you’ll be able to learn a language in the first week of class?
- Have you taken any classes that required a lot of programming?
- Have you studied algorithms and data structures?
- Are you willing to devote at least 9 hours a week to this course?
Why Take This Course:
Artificial intelligence is on track to become one of the most transformative technologies of our time. We connect with intelligent systems and services in a variety of ways, including apps on our phones, websites, devices, and so on. As a result, the demand for AI Engineers is increasing. Take this course to learn about the fundamental concepts and methods underlying artificial intelligence, as well as how to apply them to a variety of real-world situations such as game playing, navigation, sign-language recognition, and so on.
9. Secure and Private AI:
This online course will expose you to three cutting-edge privacy-preserving AI technologies: Federated Learning, Differential Privacy, and Encrypted Computation. You’ll learn how to employ the most up-to-date privacy-protection solutions, such as OpenMined’s PySyft. PySyft augments Deep Learning tools, such as PyTorch, with the cryptography and distributed technologies required to train AI models on distributed private data in a safe and secure manner.
USPs:
- Rich Learning Content
- Interactive QuizzesInteractive Quizzes
- Taught by Industry ProsTaught by Industry Pros
- Self-Paced LearningSelf-Paced Learning
Course Cost: Free
Timeline: Approx. 2 Months
Skill Level: Advanced
What You Will Learn:
- Differential Privacy
- Federated Learning
- Encrypted Computation
Prerequisites and Requirements:
We recommend the following to get the best experience in this course:
- Deep Learning or Machine Learning abilities for beginners
- Beginner-level abilities in at least one Deep Learning framework (such as PyTorch)
- Python abilities at the beginner level are required; no prior knowledge of cryptography or complex mathematics is necessary.
Why Take This Course:
Learn how to apply Deep Learning to private data while protecting users’ privacy, allowing you to train on more data while remaining socially responsible, allowing you to tackle more complex challenges and produce smarter, more effective AI models.
10. Model Building and Validation:
This course will teach you how to answer real-world questions using data from the ground up. Machine learning is a minor component of this process. This course will guide you through the process of creating numerous models. This procedure entails asking questions, collecting and manipulating data, developing models, and finally testing and assessing them.
USPs:
- Rich Learning Content
- Interactive QuizzesInteractive Quizzes
- Taught by Industry ProsTaught by Industry Pros
- Self-Paced LearningSelf-Paced Learning
Course Cost: Free
Timeline: Approx. 8 Weeks
Skill Level: Advanced
What You Will Learn:
- Introduction to the QMV Process
- Question Phase
- Modelling Phase
- Validation Phase
- Identify Hacking Attempts from Network Flow Logs
Prerequisites and Requirements:
This is an advanced course, and the ideal students for this class are those who have:
- Python programming skills, as well as familiarity with Python tools such as Ipython Notebook and data analysis packages such as Scikit-learn, Scipy, and Pandas, are required.
- Statistics knowledge, including descriptive, inferential, and predictive statistics
- Calculus knowledge, particularly derivatives and integrals
- Basic matrix algebra knowledge is required, including matrices, vectors, determinants, identity matrices, multiplication, and inverse matrix multiplication.
- Have taken Intro to Machine Learning and have a working knowledge of common supervised and unsupervised learning methods such as SVM and k-means clustering?
Why Take This Course:
This course will take a more generic approach, walking through the model creation process’s questioning, modelling, and validation components.
The idea is to get you to think about a topic in-depth and come up with your own answers. Many of the cases we will attempt will not have a single correct answer and will require you to work through the challenges using the strategies we intend to demonstrate throughout this semester.
11. Data Visualization and D3.js:
Data Visualization and D3.jsLearn the principles of data visualisation and practice interacting with it. This course teaches you how to visualise data using design principles, human perception, colour theory, and successful narrative. If you communicate data to others, aim to be an analyst or data scientist, or want to get more technical with visualisation tools, this course can help you.
The course does not discuss exploratory approaches to data discovery. Instead, the course focuses on how to graphically encode and communicate data to an audience after discovering an insight.
USPs:
- Rich Learning Content
- Interactive QuizzesInteractive Quizzes
- Taught by Industry ProsTaught by Industry Pros
- Self-Paced LearningSelf-Paced Learning
Course Cost: Free
Timeline: Approx. 7 Weeks
Skill Level: Intermediate
What You Will Learn:
- Visualization Fundamentals
- Building Blocks
- Design Principles
- Dimple js
- Narratives
- Animation and Interaction
Prerequisites and Requirements:
- To succeed in this course, you should be familiar with fundamental programming concepts such as data types (strings, arrays, booleans, and so on), if-else statements, and for loops. You should be able to describe notions such as functions and objects as well.
- Basic HTML and CSS (web page structure and styling) knowledge is not required but strongly encouraged. If you have no prior familiarity with HTML or CSS, it is recommended to take the Intro to HTML and CSS course.
Why Take This Course:
To learn about the field, you will study existing data visualisations and create new ones. Data visualisation is, at its essence, a type of communication. Learn how to be a great communicator and how to provide readers with insight and knowledge through your graphics. To generate data visualisations, this course also makes use of open web standards (HTML, CSS, and SVG).
12. Machine Learning for Trading:
This course introduces students to the real-world problems of developing machine learning-based trading strategies, as well as the algorithmic procedures from data collection to market orders. The emphasis is on using probabilistic machine learning algorithms to make trading judgments. This course examines statistical methodologies such as linear regression, KNN, and regression trees, as well as how to apply them to real-world stock trading scenarios.
USPs:
- Rich Learning Content
- Interactive QuizzesInteractive Quizzes
- Taught by Industry ProsTaught by Industry Pros
- Self-Paced LearningSelf-Paced Learning
Course Cost: Free
Timeline: Approx. 4 Months
Skill Level: Intermediate
What You Will Learn:
- Manipulating Financial Data in Python
- Computational Investing
- Machine Learning Algorithms for Trading
Prerequisites and Requirements:
- Students should have solid coding abilities as well as some knowledge of equity markets. There is no prior knowledge of finance or machine learning.
- It should be noted that this course is intended for students majoring in computer science as well as students with backgrounds in other fields such as industrial systems engineering, management, or mathematics.
- Python will be used largely for programming. This course will heavily rely on numerical computing libraries such as NumPy and Pandas.
Why Take This Course:
Complete real-world projects built by industry experts are included in this course, which covers topics ranging from asset management to trading signal development. You may learn to trade using AI algorithms and construct a portfolio that will prepare you for a job.
13. Machine Learning:
Machine Learning is a graduate-level subject in Artificial Intelligence that deals with computer programmes that alter and enhance their performance based on their experiences.
The first section of the course discusses Supervised Learning, a machine learning task that allows your phone to recognise your voice, your email to filter spam, and computers to learn a variety of other fun things.
Part two will teach you about Unsupervised Learning.
USPs:
- Rich Learning Content
- Interactive QuizzesInteractive Quizzes
- Taught by Industry ProsTaught by Industry Pros
- Self-Paced LearningSelf-Paced Learning
Course Cost: Free
Timeline: Approx. 4 Months
Skill Level: Intermediate
What You Will Learn:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Prerequisites and Requirements:
- Strong familiarity with Probability Theory, Linear Algebra and Statistics is required.
- An understanding of Students should also have some experience in programming (perhaps through Introduction to CS) and a familiarity with Neural Networks (as covered in Introduction to Artificial Intelligence).
Why Take This Course:
You will learn about and apply a range of Supervised, Unsupervised, and Reinforcement Learning techniques. Their purpose is to teach you the skills you need to grasp these tools and evaluate their output, which is necessary for solving a variety of data science challenges. And for surviving a robot uprising.
14. Intro to Hadoop and MapReduce:
The ApacheTM Hadoop® project creates open-source software for scalable, distributed computing. Learn the fundamental ideas that underpin it and how you may harness its power to make sense of your Big Data.
USPs:
- Rich Learning Content
- Interactive Quizzes
- Taught by Industry Pros
- Self-Paced Learning
- Instructor videos
- Learn by doing exercises
Course Cost: Free
Timeline: Approx. 1 Month
Skill Level: Intermediate
What You Will Learn:
- Big Data
- HDFS and MapReduce
- MapReduce code
- MapReduce Design Patterns
Prerequisites and Requirements:
To get the most out of the class, you must have fundamental Python programming skills at the level provided by introductory courses such as our Introduction to Computer Science course.
Why Take This Course:
- What role does Hadoop play in the world? (recognize the problems it solves)
- Recognize the concepts of HDFS and MapReduce (find out how it solves the problems)
- Make MapReduce programmes (see how we solve the problems)
- Practice problem-solving on your own.
15. Real-Time Analytics with Apache Storm:
Learn how to use Apache Storm, the “Hadoop of Real-Time,” to scalably process tweets or any massive data stream in real-time to drive d3 visualisations. Storm is open source, free, and a lot of fun to use! Intro to Programming at Udacity is your first step toward a career in Web and App Development, Machine Learning, Data Science, AI, and other fields. This curriculum is ideal for newcomers.
USPs:
- Rich Learning Content
- Interactive Quizzes
- Interactive Quizzes
- Taught by Industry Pros
Course Cost: Free
Timeline: Approx. 2 Weeks
Skill Level: Intermediate
What You Will Learn:
- Basic Storm Topologies
- Storm Basics
- Beyond Storm Basics
- Final Project
- Final Project: Construct a Storm Topology
- Project Extensions
Prerequisites and Requirements:
- Programming language required: Java
- No prior knowledge of Ubuntu, git, Maven, Redis, Flask (Python), or d3 is required (Javascript). Python is beneficial, but it is not compulsory. A fundamental course, such as CS101 or OO in Python, would be beneficial.
Why Take This Course:
The latency of batch processing, popularised by Hadoop, exceeds the required real-time expectations of current mobile, networked, always-on customers. To accommodate this demand, stream processing with a reaction time of seconds is required. Twitter is a world leader in large-scale real-time processing. Learn about the future from the company that is defining it.
16. Data Analysis with R:
Exploratory data analysis is a method for summarising and displaying the key features of a data set. Exploratory data analysis, as promoted by John Tukey, focuses on exploring data to understand the data’s underlying structure and variables, to develop intuition about the data set, to consider how that data set came to exist and to decide how it can be investigated with more formal statistical methods.
USPs:
- Rich Learning Content
- Interactive Quizzes
- Interactive Quizzes
- Taught by Industry Pros
Course Cost: Free
Timeline: Approx. 2 Months
Skill Level: Intermediate
What You Will Learn:
- What is EDA?
- R Basics
- Explore One Variable
- Explore Two Variables
- Explore Many Variables
- Diamonds and Price Predictions
Prerequisites and Requirements:
A background in statistics is advantageous but not necessary. Before taking this course, consider completing Intro to Descriptive Statistics. Topics of interest include:
- The terms mean, median, and mode are used interchangeably.
- Distributions that are normal, uniform, or skewed
- Box plots and histograms
Students will benefit from familiarity with the following CS and Math topics:
- Assignment of variables
- If else statements
- Comparison and logical operators (, >, =, >=, ==, &, | )
- Square roots, logarithms, and exponentials
Why Take This Course:
You’ll…
- Understand data analysis as a journey and a way to explore data through EDA.
- Using proper visualisations, investigate data at many levels.
- Acquire statistical expertise for data summarization Demonstrate inquiry and scepticism when conducting data analysis
- Develop intuition about a data set and learn how it was created.
17. Knowledge-Based AI: Cognitive Systems:
This is a fundamental artificial intelligence course. It is intended to be a difficult course with extensive individual work, readings, assignments, and projects. It discusses structured knowledge representations as well as knowledge-based problem solving, planning, decision-making, and learning approaches.
USPs:
- Rich Learning Content
- Interactive QuizzesInteractive Quizzes
- Taught by Industry ProsTaught by Industry Pros
- Self-Paced LearningSelf-Paced Learning
Course Cost: Free
Timeline: Approx. 7 Weeks
Skill Level: Advanced
What You Will Learn:
- Introduction to KBAI and Cognitive Systems
- Fundamentals
- Common Sense Reasoning
- Planning
- Learning
- Analogical Reasoning
- Visuospatial Reasoning
- Design & Creativity
- Metacognition
Prerequisites and Requirements:
You must be able to respond ‘Yes’ to the following four questions in order to succeed in this course:
- Do you have any experience in computer programming?
- Are you familiar with data structure and object-oriented programming concepts like inheritance and polymorphism?
- Are you familiar with algorithm ideas such as sorting and searching algorithms?
- Do you have experience with either Java or Python?
Why Take This Course:
At the conclusion of this class, you will be able to accomplish three primary tasks. First, utilising the methodologies presented in the course, you will be able to develop and implement a knowledge-based artificial intelligence agent capable of addressing a challenging task. Second, you will be able to use this agent to reflect on the human cognition process. Third, you will be able to apply both of these methods to practical problems in a variety of fields.
18. Introduction to TensorFlow Lite:
The TensorFlow team and Udacity collaborated to create this course as a practical method to model deployment for software developers. As you deploy deep learning models on Android, iOS, and even an embedded Linux platform, you’ll gain hands-on expertise with the TensorFlow Lite framework. By the end of this course, you will have mastered all of the abilities required to begin incorporating your own deep learning models into your apps.
USPs:
- Rich Learning Content
- Interactive QuizzesInteractive Quizzes
- Taught by Industry ProsTaught by Industry Pros
- Self-Paced LearningSelf-Paced Learning
Course Cost: Free
Timeline: Approx. 2 Months
Skill Level: Intermediate
What You Will Learn:
- Introduction to TensorFlow Lite
- TensorFlow Lite on Android
- TensorFlow Lite on Swift
- TensorFlow Lite on IoT
Prerequisites and Requirements:
General Knowledge: Familiarity with the TensorFlow Lite framework, as well as competence with Object-Oriented Programming, Python, Swift, Android, and Machine Learning.
Why Take This Course:
The Google TensorFlow team has developed TensorFlow Lite, the next generation of the TensorFlow Framework, which is specifically designed to enable machine learning at low latency on mobile and embedded devices. This course was designed for software developers as a hands-on approach to model deployment, delivering hands-on experience installing deep learning models on Android, iOS, and even an embedded Linux platform. Begin now to be at the forefront of machine learning practises.
19. Intro to TensorFlow for Deep Learning:
Learn how to use TensorFlow to create deep learning applications. The TensorFlow team and Udacity collaborated to create this course as a practical approach to deep learning for software engineers. You’ll receive hands-on experience creating your own cutting-edge image classifiers and deep learning models. Your TensorFlow models will also be used in the real world on mobile devices, on the cloud, and in browsers.
Finally, you’ll work with enormous datasets using advanced techniques and algorithms. By the end of this course, you will have acquired all of the skills required to begin developing your own AI applications.
USPs:
- Rich Learning Content
- Interactive QuizzesInteractive Quizzes
- Taught by Industry ProsTaught by Industry Pros
- Self-Paced LearningSelf-Paced Learning
Course Cost: Free
Timeline: Approx. 2 Months
Skill Level: Intermediate
What You Will Learn:
- Introduction to Machine Learning
- Your First Model: Fashion MNIST
- Introduction to Convolutional Neural Networks (“CNNs”)
- Going Further with CNNs
- Transfer Learning
- Saving and Loading Models
- Time Series Forecasting
- Introduction to TensorFlow Lite
Prerequisites and Requirements:
We recommend the following to make the most of your experience:
- Python syntax for beginners, encompassing variables, functions, classes, and object-oriented programming.
- Algebra fundamentals
Why Take This Course:
Learn how to use TensorFlow to create deep learning applications. You’ll receive hands-on experience creating your own cutting-edge image classifiers and deep learning models. Your TensorFlow models will also be used in the real world on mobile devices, on the cloud, and in browsers. By the end of this course, you will have acquired all of the skills required to begin developing your own AI applications.
20. Eigenvectors and Eigenvalues:
Eigenvectors and Eigenvalues are two of the most fascinating things to illustrate in Linear Algebra. Here you will learn how to simply compute them as well as how they are applicable and particularly interested in machine learning implementations.
USPs:
- Rich Learning Content
- Interactive QuizzesInteractive Quizzes
- Taught by Industry ProsTaught by Industry Pros
- Self-Paced LearningSelf-Paced Learning
Course Cost: Free
Timeline: Approx. 1 Weeks
Skill Level: Beginner
What You Will Learn:
- Vectors
- Definitions and Calculations
- Why is it relevant to Machine Learning?
Prerequisites and Requirements:
You must have a mathematical foundation in Linear Algebra to comprehend this lesson. Before you begin, review the topics of Linear Transformation, Determinants, and a System of Linear Equations.
Why Take This Course:
In the computational world of AI, massive amounts of data must be handled on a regular basis. Often, the data volume will be so large that some type of data reduction approach will be required. Eigen-concepts are an important part of the mathematical background required to comprehend a powerful data reduction tool known as Principal Component Analysis (PCA).
21. Intro to Artificial Intelligence:
Artificial intelligence (AI) is a field with a long history that is still continuously expanding and changing. This course will teach you the fundamentals of current AI as well as some of its most notable applications. Along the journey, we hope to excite you about the diverse uses and vast possibilities in the field of artificial intelligence, which continues to increase human capability beyond our wildest dreams.
USPs:
- Rich Learning Content
- Interactive QuizzesInteractive Quizzes
- Taught by Industry ProsTaught by Industry Pros
- Self-Paced LearningSelf-Paced Learning
Course Cost: Free
Timeline: Approx. 4 Months
Skill Level: Intermediate
What You Will Learn:
- Fundamentals of AI
- Applications of AI
Prerequisites and Requirements:
Introduction to Artificial Intelligence will cover topics such as probability theory and linear algebra. You should have a working knowledge of probability theory comparable to what is covered in our Intro to Statistics course.
Why Take This Course:
Artificial intelligence (AI) technology is becoming more common in our daily lives. It has applications in a wide range of industries, including gaming, journalism/media, and finance, as well as cutting-edge research fields such as robots, medical diagnosis, and quantum science.
This course will teach you the fundamentals and applications of artificial intelligence, such as machine learning, probabilistic reasoning, robotics, computer vision, and natural language processing.
22. Artificial Intelligence for Robotics:
Learn how to programme all of a robotic car’s major systems from the head of Google and Stanford’s autonomous driving teams. This course will teach you fundamental AI approaches such as probabilistic inference, planning and search, localization, tracking, and control, with a focus on robots. Extensive programming examples and assignments will apply these concepts to the development of self-driving cars.
This course is part of the Georgia Tech Masters in Computer Science programme. The new course adds a final project in which you must track down a runaway robot that is attempting to flee!
USPs:
- Rich Learning Content
- Interactive QuizzesInteractive Quizzes
- Taught by Industry ProsTaught by Industry Pros
- Self-Paced LearningSelf-Paced Learning
Course Cost: Free
Timeline: Approx. 2 Months
Skill Level: Advanced
What You Will Learn:
- Localization
- Kalman Filters
- Particle Filters
- Search
- PID Control
- SLAM (Simultaneous Localization and Mapping)
Prerequisites and Requirements:
- This course demands some programming experience as well as some mathematics competence.
- Python is used for programming in this course. If you are unfamiliar with Python but have familiarity with another language, you should be able to pick up the syntax quite fast.
- Probability and linear algebra will be the focus of the math. You don’t have to be an expert in either, but any knowledge of probability concepts (such as probabilities must add to one, conditional probability, and Bayes’ rule) will be quite beneficial.
Why Take This Course:
This course will educate you about probabilistic inference, planning and search, localization, tracking, and control, all with a robotics focus.
At the end of the course, you will use what you have learned by resolving the problem of a rogue robot that you must track and hunt down!
23. Intro to Deep Learning with PyTorch:
In this course, you will study the fundamentals of deep learning and create your own deep neural networks with PyTorch. You will gain hands-on experience with PyTorch through coding exercises and projects that implement cutting-edge AI applications such as style transfer and text generation.
USPs:
- Rich Learning Content
- Interactive QuizzesInteractive Quizzes
- Taught by Industry ProsTaught by Industry Pros
- Self-Paced LearningSelf-Paced Learning
Course Cost: Free
Timeline: Approx. 2 Months
Skill Level: Intermediate
What You Will Learn:
- Introduction to Deep Learning
- Introduction to PyTorch
- Deep Learning with PyTorch
- Convolutional Neural Networks
- Style Transfer
- Recurrent Neural Networks
- Natural Language Classification
- Deploying with PyTorch
Prerequisites and Requirements:
- To be successful in this course, you must be familiar with Python and data processing packages such as NumPy and Matplotlib.
- It is encouraged, but not needed, to have a basic understanding of linear algebra and calculus.
Why Take This Course:
Deep learning is propelling the AI revolution, and PyTorch makes it easier than ever for anyone to create deep learning applications. This course will provide you with hands-on experience developing and training deep neural networks with PyTorch. You will be able to use these abilities for your own personal projects.
24. AWS DeepRacer:
This course will show you how to build, train, and fine-tune reinforcement learning models using the AWS DeepRacer 3D racing simulator. You will be able to train and deploy your racing model in both simulated and real-world courses using AWS and the car’s technical specs, assembly, and calibration.
USPs:
- Rich Learning Content
- Interactive QuizzesInteractive Quizzes
- Taught by Industry ProsTaught by Industry Pros
- Self-Paced LearningSelf-Paced Learning
Course Cost: Free
Timeline: Approx. 2 Weeks
Skill Level: Intermediate
What You Will Learn:
- Intro to AWS DeepRacer
- Reinforcement learning in DeepRacer
- Unboxing AWS DeepRacer
- Under the Hood recap
- DeepRacer Assembly
- Steering calibration
- Throttle Calibration
- Track Preview
Prerequisites and Requirements:
The students should have a basic understanding of Python.
Why Take This Course:
AWS DeepRacer is a 1/18th size race car that provides a unique and enjoyable method to begin using reinforcement learning (RL). With AWS DeepRacer, you can now experiment with RL, learn through autonomous driving, and get hands-on experience.
You can begin by using the cloud-based 3D racing simulator’s virtual automobile and racetrack. You can race in the worldwide AWS DeepRacer League, the world’s first global autonomous racing league for developers, with your trained models deployed on AWS DeepRacer.
Conclusion: We hope you enjoyed the article on Udacity and the Best Audacity Courses. This article was a great resource for finding new audacity courses and Udacity courses available to help you learn new skills. If you are interested in learning more about Udacity or audacity courses, please choose any of the courses that suit you. Thanks for reading! 🤓👍
