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  3. Foundations of Artificial Intelligence and Machine Learning

Foundations of Artificial Intelligence and Machine Learning

This course provides an introduction to the Python programming language essential for data manipulation, statistical analysis, and modeling techniques required for machine learning and artificial intelligence.
Minerva Singh
Minerva Singh
Data Science | core | 30 hours

    Discussions

Overview

1.4KSTUDENTS*
94%RECOMMEND*

This journey includes:

  • 30+ hours of on-demand video
  • 21 modules
  • Core level
  • Direct access/chat with the instructor
  • 100% self-paced online
  • Many downloadable resources
  • Shareable certificate of completion
Machine learning is one of the liveliest areas in artificial intelligence. Machine learning algorithms allow computers to learn new things without being programmed. They use statistics as a way to better understand the massive amounts of data that we create every day. These newer algorithms help machines classify images, sounds, and videos. They can answer our questions, discover new drugs, and even write songs.

Skills You Will Gain

AI/ML
Deep Learning
Python
visualizations

Learning Outcomes (At the end of this program you will be able to)

  • Develop solutions to real-world machine learning problems
  • Explain and discuss the essential concepts of machine learning and in particular deep learning
  • Implement supervised and unsupervised learning models for tasks such as forecasting, predicting, and outlier detection
  • Apply and use advanced machine learning applications, including recommendation systems and natural language processing
  • Evaluate and apply deep learning concepts and software applications
  • Identify, source, and prepare raw data for analysis and modeling
  • Work with open source tools such as Python, Scikit-learn, Keras, and Tensorflow
  • Learn Python’s underlying object model, operators, and syntax.
  • Use Python and its libraries interactively through Jupyter Notebooks (IPython).
  • Manipulate data types in Python and in particular “container” types: those built into Python (str, tuple, list, dict) as well as those that are the basis of Numpy and Pandas (ndarray, Series, and DataFrames).
  • Practice the Python mechanisms needed to understand the thousands of data analysis examples available online: flow-of-control, function protocols, sequence unpacking, list comprehensions, and other functional programming tools.
  • Use functions to customize data cleaning and the behavior of data transformations.
  • Visualization and machine learning algorithms framework.

Who Should Attend

  • Professionals looking to develop a solid understanding of Python and its use in AI/ML & data science field

Curriculum

Instructors

Frequently Asked Questions

How much do the courses at Starweaver cost?

We offer flexible payment options to make learning accessible for everyone. With our Pay-As-You-Go plan, you can pay for each course individually. Alternatively, our Subscription-Based plan provides you with unlimited access to all courses for a monthly or yearly fee.

Do you offer any certifications upon completion of a course at Starweaver?

Yes, we do offer a certification upon completion of our course to showcase your newly acquired skills and expertise.

Does Starweaver offer any free courses or trials?

No, we don't offer any free courses, but we do offer 5-day trial only on our subscriptions-based plans.

Are Starweaver's courses designed for beginners or advanced students?

Our course is designed with three levels to cater to your learning needs - Core, Intermediate, and Advanced. You can choose the level that best suits your knowledge and skillset to enhance your learning experience.

What payment options are available for Starweaver courses?

We accept various payment methods such as major credit cards, PayPal, wire transfer, and company purchase orders. For more information related to payments contact customer support.

Do you offer refunds?

Yes, we do offer a 100% refund guarantee for our courses within a specified time frame. If you are not satisfied with the course, contact our customer support team to request a refund with your order details. Some restrictions may apply.

*Where courses have been offered multiple times, the “# Students” includes all students who have enrolled. The “%Recommended” shown is also based on this data.
Practical Artificial Intelligence (AI) With the PyTorch Framework in Python

Practical Artificial Intelligence (AI) With the PyTorch Framework in Python

Part 1: Practical Artificial Intelligence (AI) With the PyTorch Framework in Python

Minerva Singh

Minerva Singh

Minerva Singh completed her PhD in Forest Ecology and Conservation from the University of Cambridge, UK. In her Ph.D., she used a combination of remote sensing data such as optical data, LiDAR, aerial imagery and radar data for quantifying the impact of forest cover change on carbon stocks and biodiversity in tropical Asia. She has worked extensively in different parts of tropical Asia- Malaysia, Laos, Cambodia and the Philippines. She used extensive statistical, machine learning, and image processing techniques for her research and has an intermediate level proficiency in R and Python programming languages. She is also proficient in the use of GIS softwares like QGIS and ArcGIS and remote sensing tools like ENVI. She has several peer-reviewed publications in well-regarded journals to her credit.
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