CBTNuggets - Introduction to Machine Learning

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Free Download CBTNuggets - Introduction to Machine Learning
Released 3/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 420 Lessons ( 48h ) | Size: 29 GB
This entry-level training in machine learning and artificial intelligence prepares learners to convert vast datasets into not only meaningful information but also actionable insights, predictions, and forward-looking trends.

The impact of machine learning on today's technological landscape is simply immeasurable. This course serves as an introduction to the groundbreaking power of machine learning, and aims to illuminate the exciting possibilities of solving real-world problems with machine learning. It's up to you to harness these insights and skills to solve specific problems in your organization or professional work.
Fortunately, this course goes beyond the concepts of machine learning by offering hands-on opportunities to build models with scikit-learn, PyTorch, TensorFlow, and even a crash course in LLM development with OpenAI, LangChain, and HuggingFace.
Once you complete this Introduction to Machine Learning training, you'll be adept at employing algorithms to uncover hidden insights, leverage statistical analysis, and generate data-driven predictive outcomes - all by using machine learning.
For leaders of IT teams, this machine learning course offers an amazing transformative value: ideal for new junior data scientists transitioning into machine learning, integrating personalized training sessions, or simply a comprehensive reference for data science, machine learning, and artificial intelligence (AI) concepts and best practices.
Introduction to Machine Learning: What You Need to Know
This machine learning training features videos that cover essential data science, machine learning, and AI topics including
Exploring machine learning fundamentals and the latest best practices
Making sense of algorithms such as gradient descent and backpropagation
Implementing classification and regression models to uncover patterns in data
Diving into the perceptron and neural networks with powerful AI modeling concepts
Hands-on introduction to PyTorch, and TensorFlow model building
Distilling Large Language Models (LLMs) with ChatGPT, LangChain, and HuggingFace
Who Should Take Introductory Machine Learning Training?
The introduction to machine learning training is presented as associate-level data science training, which means it was designed for junior data scientists and aspiring machine learning engineers. This machine learning skills course offers significant value to both emerging IT professionals with at least a year of experience and seasoned data scientists looking to validate their data science skills in an ever advancing field.
New or aspiring junior data scientists. If you're a brand new data scientist, you don't want to start your first job without a familiarity with machine learning. Whether you're looking for your first job or you're still a student, take this introduction to machine learning and bring all the capabilities and opportunities of machine learning with you to your first job from day one.
Experienced junior data scientists. If you've navigated working as a data scientist for several years without delving into machine learning, congrats on your achievement! This introductory machine learning course will further broaden your wheelhouse of skills, empowering you to work with precision, efficiency, and alignment to the latest best practices and tools. Not to mention staying at the forefront of data science but also opening up profitable opportunities and advancement in your career.
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Introduction to Machine Learning
Duration: 14h 31m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 6.48 GB​

Genre: eLearning | Language: English [/center]

Linear and Logistic Regression and Neural Networks Using Python

What you'll learn
Introduction to Machine Learning: math, algorithms, and Python coding for Linear and Logistic Regression and Neural Networks

Requirements
Linear Algebra (matrix multiplication), Multivariable Calculus (gradients), Python programming

Description
Goal:

Provide an introduction to machine learning focusing on linear and logistic regression and neural network approaches

Course Covers:

Underlying mathematics and algorithms in detail

Development in Python of a machine learning framework emphasizing how algorithms translate into code

Approaches for improving performance of machine learning systems

Application to regression, binary and multi-class classification problems

Case studies: house price prediction, spam classification, digits identification

Tensorflow examples

Course Approach

Lecture videos with many examples to illustrate the theory

Jupyter Notebook demos to complement lectures

Walk through of development of machine learning framework and running of programs

50+ exercises (with solutions) including math problems, Jupyter notebook based exercises, and programming problems

Github site with all course materials (course framework code, Jupyter notebook demos, exercises & solutions, pdf of presentations)

Who this course is for:
Students without any previous experience with machine learning
Students with previous experience who would like to revisit the math, algorithms and coding for machine learning in detail

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