# Machine Learning Courses Online

Machine Learning Live Instructor Led Online Training Machine Learning courses is delivered using an interactive remote desktop.

During the Machine Learning courses each participant will be able to perform Machine Learning exercises on their remote desktop provided by Qwikcourse.

## Hands On Machine Learning With C++

Implement supervised and unsupervised machine learning algorithms using C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib with the help of real-world examples and datasets

#### About

C++ can make your machine learning models run faster and more efficiently. This handy guide will help you learn the fundamentals of machine learning (ML), showing you how to use C++ libraries to get the most out of your data. this course makes machine learning with C++ for beginners easy with its example-based approach, demonstrating how to implement supervised and unsupervised ML algorithms through real-world examples.

this course will get you hands-on with tuning and optimizing a model for different use cases, assisting you with model selection and the measurement of performance. You’ll cover techniques such as product recommendations, ensemble learning, and anomaly detection using modern C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib. Next, you’ll explore neural networks and deep learning using examples such as image classification and sentiment analysis, which will help you solve various problems. Later, you’ll learn how to handle production and deployment challenges on mobile and cloud platforms, before discovering how to export and import models using the ONNX format.

By the end of this C++ book, you will have real-world machine learning and C++ knowledge, as well as the skills to use C++ to build powerful ML systems.

#### Content

- Explore how to load and preprocess various data types to suitable C++ data structures
- Employ key machine learning algorithms with various C++ libraries
- Understand the grid-search approach to find the best parameters for a machine learning model
- Implement an algorithm for filtering anomalies in user data using Gaussian distribution
- Improve collaborative filtering to deal with dynamic user preferences
- Use C++ libraries and APIs to manage model structures and parameters
- Implement a C++ program to solve image classification tasks with LeNet architecture

#### Features

- Become familiar with data processing, performance measuring, and model selection using various C++ libraries
- Implement practical machine learning and deep learning techniques to build smart models
- Deploy machine learning models to work on mobile and embedded devices

## Hands On Music Generation With Magenta

Design and use machine learning models for music generation using Magenta and make them interact with existing music creation tools

#### About

The importance of machine learning (ML) in art is growing at a rapid pace due to recent advancements in the field, and Magenta is at the forefront of this innovation. With this course, you’ll follow a hands-on approach to using ML models for music generation, learning how to integrate them into an existing music production workflow. Complete with practical examples and explanations of the theoretical background required to understand the underlying technologies, this course is the perfect starting point to begin exploring music generation.

the course will help you learn how to use the models in Magenta for generating percussion sequences, monophonic and polyphonic melodies in MIDI, and instrument sounds in raw audio. Through practical examples and in-depth explanations, you’ll understand ML models such as RNNs, VAEs, and GANs. Using this knowledge, you’ll create and train your own models for advanced music generation use cases, along with preparing new datasets. Finally, you’ll get to grips with integrating Magenta with other technologies, such as digital audio workstations (DAWs), and using Magenta.js to distribute music generation apps in the browser.

By the end of this course, you'll be well-versed with Magenta and have developed the skills you need to use ML models for music generation in your own style.

#### Content

- Use RNN models in Magenta to generate MIDI percussion, and monophonic and polyphonic sequences
- Use WaveNet and GAN models to generate instrument notes in the form of raw audio
- Employ Variational Autoencoder models like MusicVAE and GrooVAE to sample, interpolate, and humanize existing sequences
- Prepare and create your dataset on specific styles and instruments
- Train your network on your personal datasets and fix problems when training networks
- Apply MIDI to synchronize Magenta with existing music production tools like DAWs

#### Features

- Learn how machine learning, deep learning, and reinforcement learning are used in music generation
- Generate new content by manipulating the source data using Magenta utilities, and train machine learning models with it
- Explore various Magenta projects such as Magenta Studio, MusicVAE, and NSynth

## Machine Learning With Real World Projects

Go from Beginner to Super Advance Level in Machine Learning Algorithms using Python and Mathematical Insights

#### About

Want to become a good Data Scientist? Then this is a right course for you.

This course has been designed by IIT professionals who have mastered in Mathematics and Data Science. We will be covering complex theory, algorithms and coding libraries in a very simple way which can be easily grasped by any beginner as well.

We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science from beginner to advance level.

We have solved few Kaggle problems during this course and provided complete solutions so that students can easily compete in real world competition websites.

All the codes and supporting files for this course are available at: https://github.com/PacktPublishing/Machine-Learning-with-Real-World-Projects

#### Content

- Master Machine Learning in Python
- Learn to use MatplotLib for Python Plotting
- Learn to use Numpy and Pandas for Data Analysis
- Learn to use Seaborn for Statistical Plots
- Learn All the Mathematics Required to understand Machine Learning Algorithms
- Implement Machine Learning Algorithms along with Mathematic intuitions
- Projects of Kaggle Level are included with Complete Solutions
- Learning End to End Data Science Solutions
- All Advanced Level Machine Learning Algorithms and Techniques like Regularisations, Boosting, Bagging and many more included
- Learn All Statistical concepts To Make You Ninza in Machine Learning
- Real-World Case Studies
- Model Performance Metrics
- Deep Learning
- Model Selection

#### Features

- Learn Machine Learning with real-world case studies
- Learn complex theory, algorithms and coding libraries in a very simple way

## MATLAB For Machine Learning

Extract patterns and knowledge from your data in easy way using MATLAB

#### About

MATLAB is the language of choice for many researchers and mathematics experts for machine learning. this course will help you build a foundation in machine learning using MATLAB for beginners.

You’ll start by getting your system ready with t he MATLAB environment for machine learning and you’ll see how to easily interact with the Matlab workspace. We’ll then move on to data cleansing, mining and analyzing various data types in machine learning and you’ll see how to display data values on a plot. Next, you’ll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions.

You’ll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you’ll explore feature selection and extraction techniques for dimensionality reduction for performance improvement.

At the end of the course, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB.

#### Content

- Learn the introductory concepts of machine learning.
- Discover different ways to transform data using SAS XPORT, import and export tools,
- Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data.
- Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment.
- Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures.
- Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox.
- Learn feature selection and extraction for dimensionality reduction leading to improved performance.

#### Features

- Get your first steps into machine learning with the help of this easy-to-follow guide
- Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB
- Understand how your data works and identify hidden layers in the data with the power of machine learning.

## Hands On Neural Networks With TensorFlow 2.0

A comprehensive guide to developing neural network-based solutions using TensorFlow 2.0

#### About

TensorFlow, the most popular and widely used machine learning framework, has made it possible for almost anyone to develop machine learning solutions with ease. With TensorFlow (TF) 2.0, you'll explore a revamped framework structure, offering a wide variety of new features aimed at improving productivity and ease of use for developers.

this course covers machine learning with a focus on developing neural network-based solutions. You'll start by getting familiar with the concepts and techniques required to build solutions to deep learning problems. As you advance, you’ll learn how to create classifiers, build object detection and semantic segmentation networks, train generative models, and speed up the development process using TF 2.0 tools such as TensorFlow Datasets and TensorFlow Hub.

By the end of this TensorFlow book, you'll be ready to solve any machine learning problem by developing solutions using TF 2.0 and putting them into production.

#### Content

- Grasp machine learning and neural network techniques to solve challenging tasks
- Apply the new features of TF 2.0 to speed up development
- Use TensorFlow Datasets (tfds) and the tf.data API to build high-efficiency data input pipelines
- Perform transfer learning and fine-tuning with TensorFlow Hub
- Define and train networks to solve object detection and semantic segmentation problems
- Train Generative Adversarial Networks (GANs) to generate images and data distributions
- Use the SavedModel file format to put a model, or a generic computational graph, into production

#### Features

- Understand the basics of machine learning and discover the power of neural networks and deep learning
- Explore the structure of the TensorFlow framework and understand how to transition to TF 2.0
- Solve any deep learning problem by developing neural network-based solutions using TF 2.0

## Penn Ml Benchmarks

#### Penn Machine Learning Benchmarks

this course contains the code and data for a large, curated set of benchmark datasets for evaluating and comparing supervised machine learning algorithms. These data sets cover a broad range of applications, and include binary/multi-class classification problems and regression problems, as well as combinations of categorical, ordinal, and continuous features. Please go to our home page to interactively browse the datasets, vignette, and contribution guide!

#### Breaking changes in PMLB 1.0

*this course has been restructured, and several dataset names have been changed!* If you have an older version of PMLB, we highly recommend you upgrade it to v1.0 for updated URLs and names of datasets: pip install pmlb --upgrade

#### Datasets

Datasets are tracked with Git Large File Storage (LFS). If you would like to clone the entire repository, please install and set up Git LFS for your user account. Alternatively, you can download the `.zip`

file from GitHub. All data sets are stored in a common format:

- First row is the column names
- Each following row corresponds to one row of the data
- The target column is named
`target`

- All columns are tab (
`\t`

) separated - All files are compressed with
`gzip`

to conserve space#### Python wrapper

For easy access to the benchmark data sets, we have provided a Python wrapper named

`pmlb`

. The wrapper can be installed on Python via`pip`

: pip install pmlb and used in Python scripts as follows: from pmlb import fetch_data#### Returns a pandas DataFrame

adult_data = fetch_data('adult') print(adult_data.describe()) The

`fetch_data`

function has two additional parameters: `return_X_y`

(True/False): Whether to return the data in scikit-learn format, with the features and labels stored in separate NumPy arrays.`local_cache_dir`

(string): The directory on your local machine to store the data files so you don't have to fetch them over the web again. By default, the wrapper does not use a local cache directory. For example: from pmlb import fetch_data#### Returns NumPy arrays

adult_X, adult_y = fetch_data('adult', return_X_y=True, local_cache_dir='./') print(adult_X) print(adult_y) You can also list all of the available data sets as follows: from pmlb import dataset_names print(dataset_names) Or if you only want a list of available classification or regression datasets: from pmlb import classification_dataset_names, regression_dataset_names print(classification_dataset_names) print('') print(regression_dataset_names)

#### Example usage: Compare two classification algorithms with PMLB

PMLB is designed to make it easy to benchmark machine learning algorithms against each other. Below is a Python code snippet showing the most basic way to use PMLB to compare two algorithms. from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt import seaborn as sb from pmlb import fetch_data, classification_dataset_names logit_test_scores = [] gnb_test_scores = [] for classification_dataset in classification_dataset_names: X, y = fetch_data(classification_dataset, return_X_y=True) train_X, test_X, train_y, test_y = train_test_split(X, y) logit = LogisticRegression() gnb = GaussianNB() logit.fit(train_X, train_y) gnb.fit(train_X, train_y) logit_test_scores.append(logit.score(test_X, test_y)) gnb_test_scores.append(gnb.score(test_X, test_y)) sb.boxplot(data=[logit_test_scores, gnb_test_scores], notch=True) plt.xticks([0, 1], ['LogisticRegression', 'GaussianNB']) plt.ylabel('Test Accuracy')

#### Citing PMLB

If you use PMLB in a scientific publication, please consider citing the following paper: Randal S. Olson, William La Cava, Patryk Orzechowski, Ryan J. Urbanowicz, and Jason H. Moore (2017). PMLB: a large benchmark suite for machine learning evaluation and comparison.

*BioData Mining***10**, page 36. BibTeX entry: @article{Olson2017PMLB, author="Olson, Randal S. and La Cava, William and Orzechowski, Patryk and Urbanowicz, Ryan J. and Moore, Jason H.", title="PMLB: a large benchmark suite for machine learning evaluation and comparison", journal="BioData Mining", year="2017", month="Dec", day="11", volume="10", number="1", pages="36", issn="1756-0381", doi="10.1186/s13040-017-0154-4", url="https://doi.org/10.1186/s13040-017-0154-4" }#### Support for PMLB

PMLB was developed in the Computational Genetics Lab at the University of Pennsylvania with funding from the NIH under grant AI117694, LM010098 and LM012601. We are incredibly grateful for the support of the NIH and the University of Pennsylvania during the development of this project.

## Statistics For Machine Learning

Build Machine Learning models with a sound statistical understanding.

Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. this course will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more.

By the end of the course, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem.

#### Content

- Understand the Statistical and Machine Learning fundamentals necessary to build models
- Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems
- Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages
- Analyze the results and tune the model appropriately to your own predictive goals
- Understand the concepts of required statistics for Machine Learning
- Introduce yourself to necessary fundamentals required for building supervised & unsupervised deep learning models
- Learn reinforcement learning and its application in the field of artificial intelligence domain

#### Features

- Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics.
- Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering.
- Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python.

## Machine Learning With Scikit Learn Python 3.x

#### Machine-Learning-with-Scikit-Learn-Python-3.x

**Defination:** Machine learning is the scientific study of `algorithms`

and `statistical models`

that `computer systems`

use in order to perform a `specific task`

effectively `without using explicit instructions`

, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. When applying machine learning to real-world data, there are a lot of steps involved in the process -- starting with collecting the data and ending with generating predictions.

## Data Stream Development With Apache Spark, Kafka, And Spring Boot

Handle high volumes of data at high speed. Architect and implement an end-to-end data streaming pipeline

#### About

Today, organizations have a difficult time working with huge numbers of datasets. In addition, data processing and analyzing need to be done in real time to gain insights. This is where data streaming comes in. As big data is no longer a niche topic, having the skillset to architect and develop robust data streaming pipelines is a must for all developers. In addition, they also need to think of the entire pipeline, including the trade-offs for every tier.

This course starts by explaining the blueprint architecture for developing a completely functional data streaming pipeline and installing the technologies used. With the help of live coding sessions, you will get hands-on with architecting every tier of the pipeline. You will also handle specific issues encountered working with streaming data. You will input a live data stream of Meetup RSVPs that will be analyzed and displayed via Google Maps.

By the end of the course, you will have built an efficient data streaming pipeline and will be able to analyze its various tiers, ensuring a continuous flow of data.

All the code and supporting files for this course are available at https://github.com/PacktPublishing/-Data-Stream-Development-with-Apache-Spark-Kafka-and-Spring-Boot

#### Style and Approach

This course is a combination of text, a lot of images (diagrams), and meaningful live coding sessions. Each topic covered follows a three-step structure: first, we have some headlines (facts); second, we continue with images (diagrams) meant to provide more details; and finally we convert the text and images into code written in the proper technology.

#### Content

- Attain a solid foundation in the most powerful and versatile technologies involved in data streaming: Apache Spark and Apache Kafka
- Form a robust and clean architecture for a data streaming pipeline
- Implement the correct tools to bring your data streaming architecture to life
- Isolate the most problematic tradeoff for each tier involved in a data streaming pipeline
- Query, analyze, and apply machine learning algorithms to collected data
- Display analyzed pipeline data via Google Maps on your web browser
- Discover and resolve difficulties in scaling and securing data streaming applications

#### Features

- From blueprint architecture to complete code solution, this course treats every important aspect involved in architecting and developing a data streaming pipeline
- Select the right tools and frameworks and follow the best approaches to designing your data streaming framework
- Build an end-to-end data streaming pipeline from a real data stream (Meetup RSVPs) and expose the analyzed data in browsers via Google Maps

## Kaggler

#### Kaggler

Kaggler is a Python package for lightweight online machine learning algorithms and utility functions for ETL and data analysis. It is distributed under the MIT License. Its online learning algorithms are inspired by Kaggle user tinrtgu's code. It uses the sparse input format that handles large sparse data efficiently. Core code is optimized for speed by using Cython.

## The Complete Machine Learning Course With Python

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More!

#### About

Do you ever want to be a data scientist and build Machine Learning projects that can solve real-life problems? If yes, then this course is perfect for you.

You will train machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more!

Inside the course, you'll learn how to:

- Set up a Python development environment correctly
- Gain complete machine learning toolsets to tackle most real-world problems
- Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them.
- Combine multiple models with by bagging, boosting or stacking
- Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data
- Develop in Jupyter (IPython) notebook, Spyder and various IDE
- Communicate visually and effectively with Matplotlib and Seaborn
- Engineer new features to improve algorithm predictions
- Make use of train/test, K-fold and Stratified K-fold cross-validation to select the correct model and predict model perform with unseen data
- Use SVM for handwriting recognition, and classification problems in general
- Use decision trees to predict staff attrition
- Apply the association rule to retail shopping datasets
- And much more!

By the end of this course, you will have a Portfolio of 12 Machine Learning projects that will help you land your dream job or enable you to solve real-life problems in your business, job or personal life with Machine Learning algorithms.

#### Style and Approach

You'll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen.

#### Content

- Learn to Build Powerful Machine Learning Models to Solve Any Problem
- Learn to Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells & more

#### Features

- Solve any problem in your business or job with powerful Machine Learning models
- Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, and unsupervised Machine Learning etc.

## Swix

#### Swift Matrix and Machine Learning Library

Apple's Swift is a high level language that's *asking* for some numerical library to perform computation *fast* or at the very least *easily*. This is a bare-bones wrapper for that library.

A way to have iOS run high-level code similar to Python or Matlab is something I've been waiting for, and am incredibly excited to see the results. This will make porting complex signal processing algorithms to C *much* easier. Porting from Python/MATLAB to C was (and is) a pain in the butt, and this library aims to make the conversion between a Python/Matlab algorithm and a mobile app *simple.*

In most cases, this library calls [Accelerate][accel] or [OpenCV][opencv]. If you want to speed up some function or add add another feature in those libraries, feel free to file an issue or submit a pull request (preferred!). Currently, this library gives you

- operators and various functions (sin, etc) that operate on entire arrays
- helper function (reshape, reverse, delete, repeat, etc)
- easy initializers for 1D and 2D arrays
- complex math (dot product, matrix inversion, eigenvalues, etc)
- machine learning algorithms (SVM, kNN, SVD/PCA, more to come)
- one dimensional Fourier transforms
- speed optimization using Accelerate and OpenCV

## Xai

### XAI - An eXplainability toolbox for machine learning

XAI is a Machine Learning library that is designed with AI explainability in its core. XAI contains various tools that enable for analysis and evaluation of data and models. The XAI library is maintained by The Institute for Ethical AI & ML, and it was developed based on the 8 principles for Responsible Machine Learning. You can find the documentation at https://ethicalml.github.io/xai/index.html. You can also check out our talk at Tensorflow London where the idea was first conceived - the talk also contains an insight on the definitions and principles in this library.

#### What do we mean by eXplainable AI?

We see the challenge of explainability as more than just an algorithmic challenge, which requires a combination of data science best practices with domain-specific knowledge. The XAI library is designed to empower machine learning engineers and relevant domain experts to analyse the end-to-end solution and identify discrepancies that may result in sub-optimal performance relative to the objectives required. More broadly, the XAI library is designed using the 3-steps of explainable machine learning, which involve 1) data analysis, 2) model evaluation, and 3) production monitoring.

## The Deep Learning With Keras

Find out how to take advantage with Keras, the powerful and easy to use open source python library to develop and evaluate deep learning model

#### About

New experiences can be intimidating, but not this! This deep learning starter guide is here to help you explore the deep learning of the scratch with with Keras, and on your way to your first neural.

What sets Keras apart from other deep learning frameworks is its simplicity. With over two hundred thousand users, Keras has a stronger adoption in industry and the research community than any other deep learning framework.

Deep learning with Keras Workshop begins by presenting you the fundamental concepts of the machine learning using the package . After learning to perform the linear transformations needed for the construction of neural networks

At the end of this course, you have developed the skills you need to form entirely your own neural network models.

#### Content

- Generate information on the fundamentals of Neural
- understand the limitations of machine learning and how it differs from deep learning
- build image classifiers with convolutional neurons s
- Evaluate, take your attention and improve your model with techniques such as crossvalidation
- create a prediction model To detect data patterns and make predictions
- Improve model Precision with L1, L2 and the list of deco
#### Features

- Get to grips with various model evaluation metrics, including sensitivity, specificity, and AUC scores
- Explore advanced concepts such as sequential and sequential mnetworkory model Ing
- Strengthen your skills with real-world development, scenarios and knowledge references.

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