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MANNING | Machine Learning For Mere Mortals [Video] [FCO]

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File	Size
0. Websites you may like/0. (1Hack.Us) Premium Tutorials-Guides-Articles _ Community based Forum.url	377.00 B
0. Websites you may like/1. (FreeCoursesOnline.Me) Download Udacity, Masterclass, Lynda, PHLearn, Pluralsight Free.url	286.00 B
0. Websites you may like/3. (FTUApps.com) Download Cracked Developers Applications For Free.url	239.00 B
0. Websites you may like/How you can help our Group!.txt	204.00 B
U01M01 The basics.mp4	34.68 MB
U01M02 Machine Learning versus Artificial Intelligence.mp4	33.01 MB
U01M03 Supervised learning.mp4	43.06 MB
U01M04 Unsupervised learning.mp4	16.22 MB
U01M05 Reinforcement learning.mp4	28.40 MB
U01M06 A quick math refresher.mp4	11.02 MB
U01M07 Slope of a line.mp4	45.10 MB
U01M08 Scalars, vectors, and tensors.mp4	26.47 MB
U01M09 Matrices and matrix arithmetic.mp4	13.44 MB
U01M10 Set up your computing environment.mp4	1.83 MB
U01M11 Install Python tools.mp4	11.18 MB
U01M12 Create virtualenv environment.mp4	5.88 MB
U01M13 Install Tensorflow.mp4	18.31 MB
U01M14 The projects.mp4	10.06 MB
U02M01 Supervised learning.mp4	22.32 MB
U02M02 Trend lines.mp4	4.62 MB
U02M03 Cost functions.mp4	3.80 MB
U02M04 Minimizing cost functions.mp4	8.78 MB
U02M05 Visualizing data.mp4	34.97 MB
U02M06 Using linear regression to predict values.mp4	22.84 MB
U02M07 More complicated functions.mp4	1.71 MB
U02M08 Working with matrices.mp4	4.61 MB
U02M09 Letting Tensorflow do the hard work.mp4	14.17 MB
U03M01 More supervised learning.mp4	4.72 MB
U03M02 What are features_.mp4	5.63 MB
U03M03 What makes a good feature_.mp4	15.53 MB
U03M04 Decision trees.mp4	10.58 MB
U03M05 K-nearest neighbor.mp4	9.92 MB
U03M06 Linear classification.mp4	7.89 MB
U03M07 Making it work in Tensorflow.mp4	21.22 MB
U03M08 Creating a spam filter.mp4	13.66 MB
U03M09 Tools and data for email classification.mp4	36.00 MB
U03M10 Classifying emails.mp4	9.15 MB
U04M01 How clustering works.mp4	82.12 MB
U04M02 Clustering algorithms.mp4	56.82 MB
U04M03 Introducing k-means.mp4	56.87 MB
U04M06 Assigning Points to a Centroid in K-means).mp4	34.33 MB
U05M01 What are neural networks, and how do they work.mp4	68.70 MB
U05M02 The Tensorflow Playground interface.mp4	14.04 MB
U05M03 Adding nodes to use multiple models in the TensorFlow Playground.mp4	12.81 MB
U05M04 What hidden layers are, and how to use them with TensorFlow Playground.mp4	43.92 MB
U05M05 What is the activation function in a neural network_.mp4	35.82 MB
U06M01 Using Neural Networks.mp4	56.56 MB
U06M02 How encoding non-numeric data works.mp4	64.84 MB
U06M03 One hot encoding.mp4	84.50 MB
U06M04 How image recognition relates to a neural network.mp4	95.14 MB
U07M01 Encoding and Representation.mp4	21.27 MB
U07M02 Numeric representation of data.mp4	40.61 MB
U07M03 Text representation of data.mp4	31.82 MB
U07M04 Representation of image data.mp4	34.06 MB
U07M05 Representation of audio data.mp4	28.39 MB
U07M06 Analytics, stock prices, and other time series data.mp4	43.63 MB
U07M07 Preparing data_ finding the data set.mp4	31.31 MB
U07M08 Preparing data_ Features engineering.mp4	61.38 MB
U07M09 Principal Component Analysis_ The mathematical way to determine features.mp4	23.11 MB
U07M10 Feature selection.mp4	34.54 MB
U07M11 Geometry of the data space and the curse of dimensionality.mp4	58.61 MB
U08M01 The difference between an algorithm and a model.mp4	50.27 MB
U08M02 Chaining together models.mp4	146.62 MB
U09M01 Improving performance in machine learning routines.mp4	89.81 MB
U09M02 Using parallelization.mp4	57.25 MB
U09M03 Outliers.mp4	204.64 MB
U09M05 What should we do with outliers_.mp4	119.10 MB
U09M06 Robustness and noise.mp4	40.25 MB
U09M07 Overfitting.mp4	104.22 MB
U09M08 Regularization.mp4	272.46 MB

Description

Lynda and other Courses >>> https://www.freecoursesonline.me/
Forum for discussion >>> https://1hack.us/

Author: Nicholas Chase
Publisher: Manning Publications
Released: 2021
Duration: 3h 34m
Language: English
Course Source: https://www.manning.com/livevideo/machine-learning-for-mere-mortals

Video Description

What can you do with machine learning?

• Use social media data to put the right ads in front of your users
• Predict which customers are going to leave in time to stop them
• Real-time product pricing that reacts to competition and demand
• Create smart SPAM filters
• Read text and numbers from images

If you're a halfway decent Python programmer, you can learn to do all these things and much more! Machine Learning for Mere Mortals is a practical video course that takes you from clueless beginner into the incredible world of machine learning with Python and Tensorflow!

About the subject

With machine learning (ML), you can predict outcomes, identify trends, and make on-point recommendations that take the guesswork out of marketing, pricing, and other key business activities. And a quick look through the job boards will tell you that machine learning has become one of the hottest job skills out there. You can't afford to miss out!

About the video

What do we mean by mere mortals? It's simple! We don't expect you to know any specialist mathematics or highbrow computer programming. If you passed college stats and you know the basics of Python programming, you're set. In this course, you'll start by learning what machine learning is, along with a quick refresher on the math you'll need, including key ML terms like scalars, vectors, and matrices. Next, you'll start working with Google's amazing TensorFlow machine learning library as you take your first steps. In your first major project, you'll build a smart spam filter. As you explore practical lessons in supervised and unsupervised machine learning, you'll learn how to fine-tune it to catch exactly what it needs to, every time.

One of the hottest ML topics is Deep Learning with neural networks. That's where this course goes next, but DON'T PANIC! You'll find examples and explanations that make this extraordinarily cool topic easy to understand. You'll build your first network, discover what makes it tick, and apply it to recognize handwriting. Along the way, you'll start to think like a machine learning developer as you learn how to choose and optimize algorithms and explore other tools you can use beyond TensorFlow.

Expert author Nick Chase brings his experience writing hundreds of articles and tutorials to the world of video, as he carefully guides you through each aspect of machine learning you need to know. He breaks down key concepts and terms so you can discuss this topic with other people in the ML biz using their own language. With this video course and Nick by your side, you'll be more than ready to develop your own machine learning applications and get real, actionable insight from your data!

Prerequisites

All you need is basic programming skills and basic math knowledge.

What you will learn

• Common machine learning algorithms
• Working with TensorFlow
• Using neural networks
• Making predictions
• Recognizing patterns in big data
• A tour of the most used machine learning APIs

About the instructor

Nick Chase is the author or co-author of close to a dozen programming books, including Active Server Pages 3.0 From Scratch, Java and XML From Scratch, and Beginning XML. He is also an IBM Developerworks Master Author, having written more than 400 articles and tutorials on various technical topics.