Packt Publishing – Introduction To AI – Machine Learning And Deep Learning 2026 BOOKWARE-LERNSTUF
Packt Publishing – Introduction To AI – Machine Learning And Deep Learning 2026 BOOKWARE-LERNSTUF | Apps-Tutorials | MKV | 760.27 MiB
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Packt Publishing
Introduction To AI - Machine Learning And Deep Learning
2026
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.. - - - /- --- _- [ R E L E A S E D E T A i L S ]- . ./\ . - __./---'
Release Date : 2026-07-16
Publisher : Packt Publishing
Title : Introduction To AI - Machine Learning And Deep Learning
Year : 2026
Month : 06
Category : Unknown
Language : English
Section : BOOKWARE
Files : 03 x 300 MB
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https://learning.oreilly.com/videos/-/9781808340376/
In this 3-hour course, you will build a strong foundation in Artificial
Intelligence, Machine Learning, and Deep Learning through clear explanations,
essential math refreshers, and practical algorithm overviews. The course
covers data concepts, regression, decision trees, neural networks, K-Means
clustering, performance estimation, and advanced algorithms to help you begin
your AI learning journey with confidence. What I will be able to do after this
course Understand core AI, Machine Learning, and Deep Learning concepts
Explain how regression, decision trees, clustering, and neural networks work
Apply Machine Learning concepts to practical business problem scenarios
Evaluate model performance using common assessment techniques Prepare for
further AI learning, projects, or certification pathways Course Instructor(s)
Varad G. Varadarajan is the Founder and CEO of ConnectedWorld Technology
Solutions, specializing in AI, ML, and cloud technologies. A former AWS
Solutions Architect, he has led multimillion-dollar projects and advised CXOs
on digital transformation. He is an author, blogger, and certified AI/ML
professional with advanced degrees in engineering, computer science, and
business. Who is it for This course is for students aspiring to begin a career
in AI, IT professionals, managers, and curious learners who want to understand
AI fundamentals. It is suitable for beginners with basic undergraduate-level
knowledge of mathematics, including linear algebra, probability, statistics,
and calculus. Programming experience is helpful but not mandatory.
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iF YOU HAVE ANYTHiNG TO OFFER
CONTACT US ViA THE SCENE, OR EMAiL ADDRESS BELOW
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GREETiNGS TO ALL THE PAST AND PRESENT SCENE GROUPS AND SCENERS WHO MADE
STUF AVAiLABLE FOR THOSE THAT MAY NOT OTHERWISE HAD THE PRiViLEGE
_ _ _
.. -_- -/_- __--\__ [ C O N T A C T D E T A i L S ] --- -___-- - \ - .
lernstuf [at] proton [dot] me
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File List:
174M 001-Chapter_1_Introduction 13M 001-Chapter_1_Introduction/001-about_this_course.mkv 4.7M 001-Chapter_1_Introduction/002-installation_of_software_and_course_materials.mkv 16M 001-Chapter_1_Introduction/003-installation_of_software_mac___optional.mkv 20M 001-Chapter_1_Introduction/004-python_jupyter_notebook_refresher___optional.mkv 17M 001-Chapter_1_Introduction/005-python_refresher___optional.mkv 6.8M 001-Chapter_1_Introduction/006-numpy_pandas_matplotlib_refresher___optional.mkv 98M 001-Chapter_1_Introduction/007-introduction_to_machine_learning_and_deep_learning.mkv 132M 002-Chapter_2_Math_Refresher 73M 002-Chapter_2_Math_Refresher/008-math_refresher___statistics_and_probability.mkv 48M 002-Chapter_2_Math_Refresher/009-math_refresher___functions_and_calculus.mkv 12M 002-Chapter_2_Math_Refresher/010-math_refresher_functions___optional.mkv 62M 003-Chapter_3_Data_Concepts 62M 003-Chapter_3_Data_Concepts/011-data_concepts.mkv 135M 004-Chapter_4_Machine_Learning_And_Deep_Learning 76M 004-Chapter_4_Machine_Learning_And_Deep_Learning/012-machine_learning_terminology.mkv 49M 004-Chapter_4_Machine_Learning_And_Deep_Learning/013-machine_learning_overview.mkv 11M 004-Chapter_4_Machine_Learning_And_Deep_Learning/014-gradient_descent_using_first_principles___optional.mkv 49M 005-Chapter_5_Regression 32M 005-Chapter_5_Regression/015-regression.mkv 5.1M 005-Chapter_5_Regression/016-sklearn_linear_regression___optional.mkv 3.4M 005-Chapter_5_Regression/017-sklearn_logistic_regression___optional.mkv 9.0M 005-Chapter_5_Regression/018-sklearn_multi_variate_regression___optional.mkv 41M 006-Chapter_6_Decision_Trees 25M 006-Chapter_6_Decision_Trees/019-decision_trees.mkv 11M 006-Chapter_6_Decision_Trees/020-sklearn_decision_trees___optional.mkv 3.7M 006-Chapter_6_Decision_Trees/021-sklearn_decision_trees_iris___optional.mkv 1.7M 006-Chapter_6_Decision_Trees/022-xgboost_decision_tree_iris___optional.mkv 83M 007-Chapter_7_Neural_Networks 69M 007-Chapter_7_Neural_Networks/023-neural_networks.mkv 15M 007-Chapter_7_Neural_Networks/024-keras___optional.mkv 8.7M 008-Chapter_8_K_Means 6.0M 008-Chapter_8_K_Means/025-k_means.mkv 2.8M 008-Chapter_8_K_Means/026-sklearn_kmeans___optional.mkv 44M 009-Chapter_9_Estimating_Performance 44M 009-Chapter_9_Estimating_Performance/027-estimating_performance.mkv 33M 010-Chapter_10_Advanced_Algorithms 33M 010-Chapter_10_Advanced_Algorithms/028-advanced_algorithms.mkv 3.4M 011-Chapter_11_Closing_Thoughts 3.4M 011-Chapter_11_Closing_Thoughts/029-bonus_lecture.mkv 652K LERNSTUF 648K LERNSTUF/lernstuf_introduction_2025.rar 761M total
File: 001-about_this_course.mkv Size: 12740004 bytes (12.15 MiB), duration: 00:04:12, avg.bitrate: 404 kb/s Audio: aac, 44100 Hz, stereo Video: h264, yuv420p, 1920x1080, 30.00 fps(r) Subtitles: eng
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Keywords: Packt, Publishing, 8211, Introduction, 8211, Machine, Learning, And, Deep, Learning, 2026, BOOKWARE, LERNSTUFDownload from Nitroflare
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