Machine Learning: Mlflow 2.3.2 intermediate

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Discover machine learning, where computers use algorithms to access data and learn to solve problems by themselves.

GETTING STARTED

Introduction to Machine Learning & Supervised Learning

  • 3m 13s
  • 2m 35s

GETTING STARTED

Artificial Intelligence and Machine Learning

  • 2m
  • 10m 9s

GETTING STARTED

Using BigML: An Introduction to Machine Learning & BigML

  • 2m 34s
  • 7m 11s

GETTING STARTED

Machine & Deep Learning Algorithms: Introduction

  • 1m 58s
  • 8m 39s

GETTING STARTED

Low-code ML with KNIME: Getting Started with the KNIME Analytics Platform

  • 1m 37s
  • 12m 19s

GETTING STARTED

No-code ML with RapidMiner: Getting Started with RapidMiner

  • 1m 38s
  • 11m 59s

GETTING STARTED

Machine Learning with BigQuery ML: Building Regression Models

  • 1m 47s
  • 12m 31s

GETTING STARTED

MLOps with MLflow: Getting Started

  • 1m 49s
  • 7m 16s

GETTING STARTED

Getting Started with MLOps

  • 1m 59s
  • 12m 3s

GETTING STARTED

Deep Learning with Keras

  • 1m 39s
  • 7m 59s

GETTING STARTED

Research Topics in ML & DL

  • 2m 31s
  • 2m 51s

GETTING STARTED

Advanced Functionality of Microsoft Cognitive Toolkit (CNTK)

  • 2m 26s
  • 4m

GETTING STARTED

Fundamentals of Sequence Model: Artificial Neural Network & Sequence Modeling

  • 1m 38s
  • 3m 52s

GETTING STARTED

Machine Learning & Data Analytics

  • 5m 34s
  • 5m 1s

GETTING STARTED

AI Fundamentals

  • 3m 50s
  • 3m 21s

GETTING STARTED

Using BigML: Building Supervised Learning Models

  • 2m 38s
  • 8m 56s

GETTING STARTED

Bayesian Methods: Bayesian Concepts & Core Components

  • 1m 38s
  • 7m 30s

GETTING STARTED

Keras - a Neural Network Framework

  • 1m 31s
  • 2m 28s

GETTING STARTED

AWS Certified Machine Learning: Data Engineering, Machine Learning, & AWS

  • 1m 8s
  • 5m 40s

GETTING STARTED

TensorFlow: Introduction to Machine Learning

  • 2m 9s
  • 8m 21s

GETTING STARTED

Model Management: Building Machine Learning Models & Pipelines

  • 1m 37s
  • 4m 17s

GETTING STARTED

ML & Dimensionality Reduction: Performing Principal Component Analysis

  • 2m 12s
  • 4m 45s

GETTING STARTED

Low-code ML with KNIME: Building Regression Models

  • 1m 32s
  • 4m 57s

GETTING STARTED

No-code ML with RapidMiner: Performing Regression Analysis

  • 2m 7s
  • 4m 52s

GETTING STARTED

Machine Learning with BigQuery ML: Building Classification Models

  • 1m 42s
  • 6m 52s

GETTING STARTED

MLOps with MLflow: Creating & Tracking ML Models

  • 1m 18s
  • 10m 5s

GETTING STARTED

MLOps with Data Version Control: Tracking & Serving Models with DVC & MLEM

  • 2m 9s
  • 9m 26s

GETTING STARTED

Implementing AI With Amazon ML

  • 1m 28s
  • 2m 4s

GETTING STARTED

MLOps with Data Version Control: Creating & Using DVC Pipelines

  • 1m 54s
  • 6m 43s

COURSES INCLUDED

Introduction to Machine Learning & Supervised Learning
Machine learning includes many different fields that focus on different problems. Explore what machine learning is and the fundamentals of supervised learning.
17 videos | 46m has Assessment available Badge
Supervised Learning Models
Supervised learning is one of the most popular techniques in machine learning. Explore supervised learning models and how to use them to solve problems.
13 videos | 33m has Assessment available Badge
Unsupervised Learning
Unsupervised learning can provide powerful insights on data without the need to annotate examples. Explore unsupervised learning, clustering, anomaly detection, and dimensional reduction.
12 videos | 25m has Assessment available Badge
Neural Networks
Due to recent advancements in processing, neural networks have become easier to train, which made them extremely popular. Explore neural networks and how to use them.
13 videos | 30m has Assessment available Badge
Convolutional and Recurrent Neural Networks
Some tasks aren't suitable for traditional neural networks and require specialized neural networks. Explore convolutional and recurrent neural networks and the types of problems they can solve.
13 videos | 33m has Assessment available Badge
Applying Machine Learning
Applying machine learning to problems can be a difficult task because of all the different models that are offered. Discover how to evaluate and select machine learning models and apply machine learning to a problem.
13 videos | 32m has Assessment available Badge
Building ML Training Sets: Introduction
There are numerous options available to scale and encode features and labels in data sets to get the best out of machine learning (ML) algorithms. In this 10-video course, explore techniques such as standardizing, nomalizing, and one-hot encoding. Learners begin by learning how to use Pandas library to load a data set in the form of a CSV file and perform exploratory analysis on its features. Then use scikit-learn's Binarizer to transform the continuous data in a series to binary values; apply the MiniMaxScaler on a data set to get two similar columns to have the same range of values; and standardize multiple columns in data sets with scikit-learn's StandardScaler. Examine differences between the Normalizer and other scaling techniques, and learn how to represent values in a column as a proportion of the maximum absolute value by using the MaxAbScaler. Finally, discover how to use Pandas library to one-hot encode one or more features of your data set and distinguish between this technique and label encoding. The concluding exercise involves building ML training sets.
10 videos | 1h 9m has Assessment available Badge
Building ML Training Sets: Preprocessing Datasets for Linear Regression
This 7-video course helps learners discover how to implement machine learning scaling techniques such as standardizing and min-max scaling on continuous data and one-hot encoding on categorical features to improve performance of linear regression models. In the first tutorial, you will use Pandas library to load a CSV file into a data frame and analyze its contents by using Pandas and Matplotlib. You will then learn how to create a linear regression model with scikit-learn to predict the sale price of a house and evaluate this model by using metrics such as mean squared error and r-square. Next, learners will examine the application of min-max scaling on continuous fields and one-hot encoding on the categorical columns of a data set. Then analyze effects of preprocessing by recognizing benefits of scaling and encoding data sets by evaluating the performance of a regression model built with preprocessed data. Also, learn how to use scikit-learn's StandardScaler on a data set's continuous features and compare its effects with that of min-max scaling. The concluding exercise involves preprocessing data for regression.
7 videos | 50m has Assessment available Badge
Building ML Training Sets: Preprocessing Datasets for Classification
In this course, learners can explore how to implement machine learning scaling techniques such as standardizing and normalizing on continuous data and label encoding on the target, in order to get the best out of machine learning algorithms. Examine dimensionality reduction by using Principal Component Analysis (PCA). Start this 6-video course by using Pandas library to load a CSV data set into a data frame and scale continuous features by using a standard scaler. You will then learn how to build and evaluate a support vector classifier in scikit-learn; use Pandas and Seaborn to generate a heat map; and spot the correlations between features in a data set. Discover how to apply the technique of PCA to reduce the number of dimensions in your input data and obtain the explained variance of each principal component. In the course's final tutorial, you will explore how to apply normalization and PCA on data sets and build a classification model with the principal components of scaled data. The concluding exercise involves processing data for classification.
6 videos | 43m has Assessment available Badge
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COURSES INCLUDED

Artificial Intelligence and Machine Learning
This course will demystify the world of artificial intelligence (AI) and machine learning (ML), taking you from foundational concepts to practical applications. You'll learn to distinguish AI and ML, explore how algorithms learn, and perform common tasks like classification and clustering. You will begin by learning to confidently distinguish between the broad umbrella of AI and the specific subset of ML, understanding how each contributes to the landscape of intelligent systems. Next, you'll explore the milestones that shaped AI. Then you will discover how to classify the diverse approaches of machine learning. Finally, you will explore the practical aspects of common machine learning problems. You'll learn the meaning of regression, classification, and clustering and how they're applied in real-world scenarios. Discover how to evaluate model performance and explore the workings of popular traditional models like linear regression and decision trees. You'll also be introduced to ensemble learning, where the "wisdom of the crowds" fuels even more accurate predictions.
11 videos | 1h 36m has Assessment available Badge
Deep Learning and Neural Networks
Deep learning and neural networks have revolutionized various fields by enabling computers to automatically learn complex patterns from data. This led to breakthroughs in areas such as image recognition, natural language processing (NLP), and autonomous driving. In this course, you will compare and contrast traditional machine learning (ML) and deep learning models. You will see how deep learning models excel in automated feature extraction from raw data, tackling complex tasks with the power of vast datasets. You will explore the fundamental unit of deep learning, the neuron, and understand how it works. Next, you will explore the diverse neural network architectures designed for specific data types. You will learn how convolutional neural networks (CNNs) extract features from images and how recurrent neural networks (RNNs) are able to extract relationships in time-series data. Finally, you will explore how neural networks handle natural language processing. You will learn how attention-based models help models focus on crucial parts of the input data for enhanced predictions and how generative adversarial networks (GANs) work. You will also explore reinforcement learning, a machine learning technique where agents navigate uncertain environments to maximize rewards.
11 videos | 1h 20m has Assessment available Badge

COURSES INCLUDED

Using BigML: An Introduction to Machine Learning & BigML
From self-driving cars to predicting stock prices, machine learning has an exciting range of applications. BigML, due to its ease of use, makes these algorithms widely accessible. This course outlines machine learning fundamentals and how these are applied in BigML. You'll start by examining various machine learning algorithm categories and the kinds of problems they're used to solve. You'll then investigate the classification problem and the process involved in training and evaluating such models. Next, you'll examine linear regression and how this can help predict a continuous value. Moving on, you'll explore the concept of unsupervised learning and its application in clustering, Principal Component Analysis (PCA), and generating associations. Finally, you'll recognize how all of this comes together when using BigML to significantly simplify the building and maintenance of your machine learning models.
11 videos | 1h 10m has Assessment available Badge
Using BigML: Getting Hands-on with BigML
BigML not only provides ease-of-use, but it also offers flexibility in how you work with your data. This course serves as a hands-on introduction to BigML and its vast array of features. You'll start by exploring the different ways data can be loaded into the platform and how these can be transformed into datasets to train and test a machine learning model. You'll gain practical experience with some of the tools available to help you better understand your data - from histograms and scatterplots to visualizations of value distribution. Moving on, you'll build a fundamental classification model, a decision tree, which takes employee details and predicts whether they'll stay or leave in the next year. Finally, you'll investigate some possible configurations for this model.
11 videos | 1h 16m has Assessment available Badge

COURSES INCLUDED

Machine & Deep Learning Algorithms: Introduction
Examine fundamentals of machine learning (ML) and how Pandas ML can be used to build ML models in this 7-video course. The working of Support Vector Machines to perform classification of data are also covered. Begin by learning about different kinds of machine learning algorithms, such as regression, classification, and clustering, as well as their specific applications. Then look at the process involved in learning relationships between input and output during the training phase of ML. This leads to an introduction to Pandas ML, and the benefits of combining Pandas, scikit-learn, and XGBoost into a single library to ease the task of building and evaluating ML models. You will learn about Support Vector Machines, which are a supervised machine learning algorithm, and how they are used to find a hyperplane to divide data points into categories. Learners then study the concept of overfitting in machine learning, and the problems associated with a model overfitted to training data. and how to mitigate the issue. The course concludes with an exercise in machine learning and classification.
7 videos | 45m has Assessment available Badge
Machine & Deep Learning Algorithms: Regression & Clustering
In this 8-video course, explore the fundamentals of regression and clustering and discover how to use a confusion matrix to evaluate classification models. Begin by examining application of a confusion matrix and how it can be used to measure the accuracy, precision, and recall of a classification model. Then study an introduction to regression and how it works. Next, take a look at the characteristics of regression such as simplicity and versatility, which have led to widespread adoption of this technique in a number of different fields. Learn to distinguish between supervised learning techniques such as regression and classifications, and unsupervised learning methods such as clustering. You will look at how clustering algorithms are able to find data points containing common attributes and thus create logical groupings of data. Recognize the need to reduce large data sets with many features into a handful of principal components with the PCA (Principal Component Analysis) technique. Finally, conclude the course with an exercise recalling concepts such as precision and recall, and use cases for unsupervised learning.
8 videos | 48m has Assessment available Badge
Machine & Deep Learning Algorithms: Data Preparation in Pandas ML
Classification, regression, and clustering are some of the most commonly used machine learning (ML) techniques and there are various algorithms available for these tasks. In this 10-video course, learners can explore their application in Pandas ML. First, examine how to load data from a CSV (comma-separated values) file into a Pandas data frame and prepare the data for training a classification model. Then use the scikit-learn library to build and train a LinearSVC classification model and evaluate its performance with available model evaluation functions. You will explore how to install Pandas ML and define and configure a ModelFrame, then compare training and evaluation in Pandas ML with equivalent tasks in scikit-learn. Learn how to build a linear regression model by using Pandas ML. Then evaluate a regression model by using metrics such as r-square and mean squared error, and visualize its performance with Matplotlib. Work with ModelFrames for feature extraction and encoding, and configure and build a clustering model with the K-Means algorithm, analyzing data clusters to determine unique characteristics. Finally, complete an exercise on regression, classification, and clustering.
10 videos | 1h 3m has Assessment available Badge
Automation Design & Robotics
In this 12-video course, you will examine the different uses of data science tools and the overall platform, as well as the benefits and challenges of machine learning deployment. The first tutorial explores what automation is and how it is implemented. This is followed by a look at the tasks and processes best suited for automation. This leads learners into exploring automation design, including what Display Status is, and also the Human-Computer Collaboration automation design principle. Next, you will examine the Human Intervention automation design principle; automated testing in software design and development; and also the role of task runners in software design and development. Task runners are used to automate repeatable tasks in the build process. Delve into DevOps and automated deployment in software design, development, and deployment. Finally, you will examine process automation using robotics, and in the last tutorial in the course, recognize how modern robotics and AI designs are applied. The concluding exercise involves recognizing automation and robotics design application.
13 videos | 34m has Assessment available Badge
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COURSES INCLUDED

Low-code ML with KNIME: Getting Started with the KNIME Analytics Platform
Organizations have been collecting data for analytics and predictive modeling for decades, however, in the past, this analysis has been restricted to engineers and analysts who can write code. The KNIME Analytics Platform makes machine learning and data analytics more accessible by allowing you to build complex workflows with little to no code. Through this course, learn how the KNIME platform works. Examine the role of the KNIME Analytics Platform and the KNIME Community Hub. Next, explore machine learning basics and how supervised and unsupervised learning techniques work. Finally, discover how to set up the KNIME Analytics Platform and get familiar with the KNIME user interface. Upon completion, you'll be able to handle building machine learning workflows using KNIME.
7 videos | 44m has Assessment available Badge

COURSES INCLUDED

No-code ML with RapidMiner: Getting Started with RapidMiner
The more organizations depend on data for decision making, the more important machine learning becomes in every business process. The RapidMiner data science platform allows users to build complex analytics workflows with little to no code. Through this course, learn how to get started with RapidMiner. Discover what support RapidMiner offers for the analytics and artificial intelligence workflow, as well as the various tools included with RapidMiner. Next, explore the basics of machine learning and compare supervised and unsupervised learning models. Finally, work with RapidMiner Studio, and learn about the tool's different panels. Upon completion, you'll be able to set up to build predictive models in RapidMiner.
7 videos | 45m has Assessment available Badge

COURSES INCLUDED

Machine Learning with BigQuery ML: Building Regression Models
BigQuery is a flagship product on the Google Cloud Platform which allows you to build and train machine learning (ML) models using simple SQL queries. BigQuery has support for a range of supervised and unsupervised machine learning models that can be trained on data stored in BigQuery. In this course, you will be introduced to BigQuery on the Google Cloud Platform and set up a GCP trial account that allows you to work with BigQuery to train ML models. You will then review some machine learning basics and dig a little deeper into regression models. Next, you will create datasets and tables in BigQuery and upload your data to the cloud. You will visualize and explore your data using Looker Studio and prepare and clean your data using DataPrep. Finally, you will train regression models using linear regression, gradient-boosted trees, and the random forest model and evaluate and compare the performance of these models on your test data.
14 videos | 2h 4m has Assessment available Badge

COURSES INCLUDED