In this practical, hands-on course, our main objective is to give you the foundational educations of Machine Learning with Python. Understandably, a theory is important to build a solid foundation. However, that theory alone isn’t going to get the job done, so that’s why this course is packed with practical hands-on examples that you can follow step by step. This section gives you a full introduction to Machine Learning, including Supervised & Unsupervised ML with hands-on, step-by-step training.
- Access 77 lectures & 12 hours of content 24/7
- Introduction to Machine learning
- Understand data processing
- Learn about linear regression & logistic regression
- Know what decision trees, ensemble learning, K-nearest neighbors & others are all about
- Gain insights on support vector machines, PCA & K-means clustering
The Machine Learning & Data Science Developer Certification Program provides a comprehensive set of knowledge and skills in data science, machine learning, and deep learning. This immersive training curriculum covers all the key technologies, techniques, principles, and practices you need to play a key role in your data science development team and distinguish yourself professionally. This program moves progressively and rapidly to cover the foundational components of machine learning, beginning with foundational principles and concepts used in data science and machine learning.
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- Access 64 lectures & 11 hours of content 24/7
- Develop to real-world machine learning problems
- Explain & discuss the essential concepts of machine learning and, in particular, deep learning
- Implement supervised & unsupervised learning models for tasks such as forecasting, predicting and outlier detection
- Apply & use advanced machine learning applications, including recommendation systems and natural language processing
- Evaluate & apply deep learning concepts and software applications
- Identify, source & prepare raw data for analysis and modelling
- Work with open source tools such as Python, Scikit-learn, Keras and Tensorflow
This is a straightforward course for Python Programming Language and Machine Learning. In the course, you will have down-to-earth way explanations with projects. With this course, you will learn Machine Learning step-by-step. It comes with easy exercises, challenges, and lots of real-life examples. Open your door to the Data Science and Machine Learning world. You will learn the fundamentals of Machine Learning and its beautiful libraries such as Scikit Learn. Throughout the course, Your will learn how to use Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms.
- Access 45 lectures & 8 hours of content 24/7
- Introduce yourself to Machine Learning
- Familiarize with Evaluation Metrics
- Linear Regression
- What is Classification vs Regression?
- Evaluating Performance-Classification Error Metrics
- Evaluating Performance-Regression Error Metrics
- Supervised Learning
- Cross-Validation and Bias Variance Trade-Off
- Use Matplotlib and seaborn for data visualizations
- Machine Learning with SciKit Learn
- Logistic Regression
In this course, you will learn some fundamental stuff about Python and the Numpy library. Then Machine Learning history, concepts, workflow, models, and algorithms. Also, learn what is neural network concept is. Then learn Artificial Neural networks and enter the Keras world, then we exit the Tensorflow world. Then understand the Convolutional Neural Network concept. Then learn about Recurrent Neural Networks and LTSM. After a while, you will learn the Transfer Learning concept. Finally, Projects. Here you’ll make some interesting machine learning models with the information you’ve learned along the course.
- Access 59 lectures & 10 hours of content 24/7
- Fundamental stuff of Python and its library Numpy
- What are the AI, Machine Learning, and Deep Learning
- History of Machine Learning
- Turing Machine and Turing Test
- Convolutional Neural Network
- Recurrent Neural Network and LTSM
- Transfer Learning
This course is your complete guide to practical machine and deep learning using the Tensorflow and Keras frameworks in Python. In the age of Big Data, companies across the globe use Python to sift through the avalanche of information at their disposal, and the advent of Tensorflow and Keras is revolutionizing deep learning. This course will help you break into this booming field.
- Access 61 lectures & 5 hours of content 24/7
- Get a full introduction to Python Data Science
- Get started w/ Jupyter notebooks for implementing data science techniques in Python
- Learn about Tensorflow & Keras installation
- Understand the workings of Pandas & Numpy
- Cover the basics of the Tensorflow syntax & graphing environment and Keras syntax
- Discover how to create artificial neural networks & deep learning structures w/ Tensorflow & Keras
Note: Software not included
In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in machine learning, neural networks, and deep learning via a powerful framework, H2O in R, you can give your company a competitive edge and boost your career to the next level. This course covers the main aspects of the H2O package for data science in R. If you take this course, you can do away with taking other courses or buying books on R-based data science as you will have the keys to a very powerful R supported data science framework.
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- Access 6 lectures & 0.5 hour of content 24/7
- Be familiar with powerful R-based deep learning packages such as H2O
- Learn the important concepts of machine learning without the jargon
- Implement both supervised & unsupervised algorithms using H2O
- Do Artificial Neural Networks (ANN) & Deep Neural Networks (DNN)
- Work with real data within the framework
In this short course, you’re going to create an application with Python that will detect objects inside of images. You’ll use a busy downtown intersection, a cat, and a bike as examples of multiple object detection, living object detection, and how things don’t always turn out how you expect. You do not need to know math or how to code to take this course! You do not need to know Python or machine learning for this course. It’ll walk you through each of the steps to get set up and how to modify the code so you can perform object detection on any image.
- Access 12 lectures & 1 hour of content 24/7
- Create an Image Detection App from scratch
- Learn to install Phyton
- Familiarize yourself with its environment
- Install packages
- Use a custom model
- Detect images
The concept of Artificial Intelligence and Machine Learning can be a little bit intimidating for beginners, and specifically for people without a substantial background in complex math and programming. This training is a soft starting point to walk you through the fundamental theoretical concepts. In this course, you’re going to open the mysterious AI/ML black box, take a look inside, get more familiar with the terms used in the industry. It is going to be a super interesting story. It is important to mention that there are no specific prerequisites for starting this training, and it is designed for absolute beginners.
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- Access 28 lectures & 2 hours of content 24/7
- Understand the difference between Applied & Generalized AI
- Learn the process of training a model
- Learn more about Machine Learning & Deep Learning
- Understand clustering & dimension reduction
In this practical, hands-on course, our main objective is to give you the foundational education on implementing Python Data Analysis & Visualization. And we understand that theory is important to build a solid foundation. We understand that theory alone isn’t going to get the job done, so that’s why this course is packed with practical hands-on examples that you can follow step by step.
- Access 11 lectures & 2 hours of content 24/7
- NumPy data analysis
- Pandas data analysis
- Phyton data visualization
From Netflix’s recommendation system to Tesla’s self-driving cars, machine learning is all around us, and more companies are getting on board with what this technology can offer. Serving as your machine learning primer, this course offers a comprehensive look at machine learning, the algorithms that power it, and how you can implement them with the R programming language. You’ll dive into what makes today’s AI innovations tick, explore key tools like TensorFlow, and get hands-on training as you explore neural networks, decisions trees, and more.
- Access 36 lectures & 5 hours of content 24/7
- Explore implementing machine learning algorithms w/ the R language
- Walk through creating neural networks & implementing them in R
- Familiarize yourself w/ TensorFlow & H2O
In this practical, hands-on course, our main objective is to give you the foundational educations of Machine Learning with Python. Understandably, a theory is important to build a solid foundation. However, that theory alone isn’t going to get the job done, so that’s why this course is packed with practical hands-on examples that you can follow step by step. This section gives you a full introduction to Machine Learning, including Supervised & Unsupervised ML with hands-on, step-by-step training.
- Access 77 lectures & 12 hours of content 24/7
- Introduction to Machine learning
- Understand data processing
- Learn about linear regression & logistic regression
- Know what decision trees, ensemble learning, K-nearest neighbors & others are all about
- Gain insights on support vector machines, PCA & K-means clustering
Python is the number one programming language choice for machine learning, data science, and artificial intelligence. However, to get those high-paying programming jobs, you need the skills and knowledge of becoming an expert Python Programmer, and that’s exactly what you’ll learn in this course. In this practical, hands-on course, the main objective is to educate you on the ins and outs of Python Programming. Blending practical work with solid theoretical training, we take you from the basics of Python Programming to mastery, giving you the training you need not just to create software programs, scrape websites, and build automation but also the foundational understanding of data science and visualization so you can become a well-rounded Python Programmer.
- Access 20 lectures & 2 hours of content 24/7
- Learn the ins & outs of Python programming
- Create software programs, scrape websites, & build automations
- Understand data science & visualization
- Become a well-rounded Python Programmer
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