Free Download Coursera - AI and Machine Learning Essentials with Python Specialization
Released 8/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 93 Lessons ( 10h 53m ) | Size: 1.93 GB
Explore AI and Machine Learning. Go further with your Python skills while exploring the transformative fields of artificial intelligence, machine learning and deep learning.
What you'll learn
Understand the philosophical, scientific, and historical foundations of artificial intelligence and machine learning.
Apply Python programming concepts to implement AI algorithms, machine learning models, and deep learning architectures.
Use essential statistics concepts, like probability, linear algebra and statistical learning theory, to develop and analyze machine learning models.
Implement and compare machine learning algorithms, such as linear regression and logistic regression, for both regression and classification tasks.
Skills you'll gain
Artificial Intelligence
Python (Programming Language)
Search Algorithms
Logistic Regression
Linear Regression
Machine Learning Methods
Probability And Statistics
Mathematics
Confidence Intervals
Simple Random Sample
Point Estimation
This specialization will prepare learners to enter the exciting fields of artificial intelligence (AI) and machine learning. Across four courses, learners will familiarize themselves with AI, machine learning and deep learning essentials, while also gaining experience with statistics-the backbone of any machine learning problem. Learners will use Python to write programs that accomplish basic AI and machine learning tasks. By the end of this specialization, learners will be able to confidently use and discuss AI and machine learning tools and concepts, improve their Python skills, and be primed for more advanced study in these fields.
Applied Learning Project
Learners will complete Python coding assignments to solve common AI and Machine Learning tasks, such as uninformed/informed search and linear regression problems. In addition, learners will solve statistics assignments, building skills needed to address nearly every data science problem.
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