Getting Started with Machine Learning
This course is especially for beginners who want to get started their journey in the field of machine learning. This course provides the hands-on experience with the python and scikit learn. So if you are new to the machine learning Get started with this course will be a good choice.
Overview of Supervised, Unsupervised, and Reinforcement Learning
Course Outcome:
Learners completing this course will be able to give definitions and explain the types of problems that can be solved by the 3 broad areas of machine learning: Supervised, Unsupervised, and Reinforcement Learning.
Course Topics and Approach:
This course gives a gentle introduction to the 3 broad areas of machine learning: Supervised, Unsupervised, and Reinforcement Learning. The goal is to explain the key ideas using examples with many plots and animations and little math, so that the material can be accessed by a wide range of learners. The lectures are supplemented by Python demos, which show machine learning in action. Learners are encouraged to experiment with the course demo codes. Additionally, information about machine learning resources is provided, including sources of data and publicly available software packages.
Course Audience:
This course has been designed for ALL LEARNERS!!!
Teaching Style and Resources:
Python Demos:
There are several options for running the Python demos:
2021.09.28 Update
Machine Learning algorithms
Artificial Intelligence algorithms
Information Retrieval algorithms
Deep Learning algorithms
Quantum Computing algorithms
Computer Vision algorithms
Natural Language Processing algorithms
This course focuses on the theoretical aspects of the field of Data Science and Machine Learning. It helps the students to quickly gain an in-depth overview of different algorithmic techniques used in various domains and applications. This course features external links to further enhance the experience and reinforce the concepts acquired. It also provides easy explanations of popular and useful research papers that are driving this field forward.
Emerging Trends in Computer & Information technology Field
The aim of this course is to help students to attain the industry identified competency through various teaching learning experience: acquire knowledge of emerging trends. Advancements and applications of Computer Engineering and Information Technology are ever changing. Emerging trends aims at creating awareness about major trends that will define technological disruption in the upcoming years in the field of Computer Engineering and Information Technology. These are some emerging areas expected to generated revenue, increasing demand as IT professionals and open avenues of entrepreneurship. The Objectives of the course are Differentiate between Machine Learning & Deep Learning, State IoT issues & Challenges in deployment, Describe the given model of Digital Forensics Investigation, Describe the given evidence handling, Describe the need to hack your own systems, Describe Database Vulnerabilities. The outcomes of the course are Describe Artificial Intelligence, Machine Learning & Deep Learning: Describe the concept of AI, State the components of AI, Differentiate between Machine Learning & Deep Learning, Interpret IoT Concepts: Describe IoT Systems in which information and knowledge are inferred from data, State IoT issues and challenges in deployment, Compare Model of Digital Forensic Investigation: Describe the given model of Digital Forensics Investigation, State the ethical and unethical issues in Digital Forensics, Describe Evidence Handling Procedures: List the rules of digital evidence, Describe the given evidence handling procedures, Describe Ethical Hacking Process: Describe the need to hack your own system, Detect Network, Operating System & Application vulnerabilities: Network Infrastructure vulnerabilities (Wired/Wireless),Describe Messaging Systems vulnerabilities.
Discover the exciting world of high ticket courses
Learn how to determine your best customer for your high ticket course
Learn how to develop an irresistible high ticket course program
Learn the best ways to market your course to make high ticket course sales
If you’d like to get started in the online course business the RIGHT way, or if you’re tired of receiving low amounts of money for your hard-earned life experiences, then this just may be the course for you.
In this course, Dave Espino walks you through his proven system for generating high ticket course sales (anywhere from $997 to $20,000 per client) and shows you his system for doing so.
From the very beginning, Dave shows you how to begin with your perfect customer avatar and then determine what your highest and best service can be for them.
Then, Dave will walk you through how to design and “engineer” the perfect high ticket course offer for that client, so that your offer is irresistible to them and you make the sale.
You’ll also learn 2 ways to use ChatGPT to create your core training and you’ll learn 2 ways to easily record your course.
Lastly, Dave will show you his proven system for marketing your course using low cost ads that generate a very high return on ad spend.
You get the entire system that Dave’s using to generate high ticket course sales in this power-packed, yet concise course.
This is easily a valuable resource for you if you are serious about creating and selling online courses.
What is Data Science
Who is a Data Scientist
Type of Questions that a Data Science Can Answer
Supervised and Unsupervised Learning in Machine Learning with Real life Examples
Applications of Data Science in Real Life
What is Data Engineering
Who is a Data Engineer
What is Machine Learning
Who is a Machine Learning Engineer
Skills Needed to become a Data scientist
How to Practice Data Science and Build your portfolio
Certifications in Data Science
Some Great Books in Data Science
Data science and machine learning is one of the hottest fields in the market and has a bright future
In the past ten years, many courses have appeared that explains the field in a more practical way than in theory
During my experience in counseling and mentoring, I faced many obstacles, the most important of which was the existence of educational gaps for the learner, and most of the gaps were in the theoretical field.
To fill this gap, I made this course, Thank God, this course helped many students to properly understand the field of data science.
If you have no idea what the field of data science is and are looking for a very quick introduction to data science, this course will help you become familiar with and understand some of the main concepts underlying data science.
If you are an expert in the field of data science, then attending this course will give you a general overview of the field
This short course will lay a strong foundation for understanding the most important concepts taught in advanced data science courses, and this course will be very suitable if you do not have any idea about the field of data science and want to start learning data science from scratch
Master Azure Machine Learning
Have a great intuition of many Azure Machine Learning models
Build an army of powerful Machine Learning models and know how to combine them to solve any problem
Know which Machine Learning model to choose for each type of problem
Make robust Machine Learning models
Are you passionate about Machine Learning and AI? Are you looking to find your first steps into Data Science. This course starts from scratch with Azure Machine Learning and lands in decision trees.
I will walk you through the Azure ML Studio, how to create expirements, how to add datasets, how to add algorithms and predict values.
This course does not cover any coding with R or Python, this will be published in a different course.
Understand Machine learning concepts.
Machine learning course comprises below lectures.
# Course Duration
3.1 Machine learning introduction 00:07:11
3.2 Machine learning algorithms 00:10:25
3.3 Machine learning softwares 00:14:43
5.1 AWS and Machine learning 00:08:51
5.8 TensorFlow – Open source Machine Learning framework 00:16:15
Machine Learning on AWS covers more details about concepts of TensorFlow, Amazon SageMaker and other AWS ML topics.
Course covering KDD, AI, BI, Deep learning, Neural Networks, ANN, Decision tree, Bayesian networks, TensorFlow and Knime
A specific idea of the next generation of AI tools integrating deep learning algorithms and blockchain technology
This course provides a conceptual overview and technical summary of the two top job growth areas worldwide: blockchain technology and deep learning. The course discusses how these technologies may be used together in deep learning chains. Some of the important application areas are autonomous driving, health care, energy, and finance.
In this project-based course, you are going to build an end-to-end machine learning pipeline in Azure ML Studio, all without writing a single line of code! This course uses the Adult Income Census data set to train a model to predict an individual’s income. It predicts whether an individual’s annual income is greater than or less than $50,000. The estimator used in this project is a Two-Class Boosted Decision Tree classifier. Some of the features used to train the model are age, education, occupation, etc. Once you have scored and evaluated the model on the test data, you will deploy the trained model as an Azure Machine Learning web service. In just under an hour, you will be able to send new data to the web service API and receive the resulting predictions.
This is the second course in this series on building machine learning applications using Azure Machine Learning Studio. I highly encourage you to take the first course before proceeding. It has instructions on how to set up your Azure ML account with $200 worth of free credit to get started with running your experiments! This course runs on Coursera’s hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed. Notes: – You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. – 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.Read more
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
A basic understanding of machine learning workflows.
