Machine Learning: Keras 2.4 expert
Technology:
Expertise:
 9 Courses  6h 5m 49s
 10 Books  57h 10m
 2 Courses  2h 27m 8s
 4 Courses  3h 12m 33s
 8 Books  34h 5m
 1 Course  1h 54m 50s
 4 Books  9h 45m
 3 Courses  1h 46m 5s
 6 Books  36h 44m
 2 Courses  1h 38m 46s
 19 Courses  20h 9m 16s
 8 Books  32h 53m
 Includes Lab
 5 Courses  5h 52m 8s
 7 Books  20h 28m
 5 Courses  6h 1m 20s
 4 Books  13h 57m
 2 Courses  2h 30m 22s
 3 Courses  2h 38m 59s
 6 Books  51h 57m
 2 Courses  1h 37m 40s
 18 Courses  14h 25m 5s
 1 Book  2h 25m
 8 Courses  9h 17m 2s
 4 Books  17h 18m
 28 Courses  28h 31m 21s
 8 Books  32h 30m
 Includes Lab
 1 Course  1h 15m 30s
 1 Book  6h 56m
 2 Courses  1h 39m 53s
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
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
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
Using BigML: Building Supervised Learning Models

2m 38s

8m 56s
GETTING STARTED
Bayesian Methods: Bayesian Concepts & Core Components

1m 38s

7m 30s
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
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
Assessment
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
Assessment
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
Assessment
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
Assessment
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
Assessment
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
Assessment
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 10video course, explore techniques such as standardizing, nomalizing, and onehot 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 scikitlearn'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 scikitlearn'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 onehot 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
Assessment
Badge
Building ML Training Sets: Preprocessing Datasets for Linear Regression
This 7video course helps learners discover how to implement machine learning scaling techniques such as standardizing and minmax scaling on continuous data and onehot 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 scikitlearn to predict the sale price of a house and evaluate this model by using metrics such as mean squared error and rsquare. Next, learners will examine the application of minmax scaling on continuous fields and onehot 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 scikitlearn's StandardScaler on a data set's continuous features and compare its effects with that of minmax scaling. The concluding exercise involves preprocessing data for regression.
7 videos 
50m
Assessment
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 6video 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 scikitlearn; 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
Assessment
Badge
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FREE ACCESS
COURSES INCLUDED
Using BigML: An Introduction to Machine Learning & BigML
From selfdriving 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
Assessment
Badge
Using BigML: Getting Handson with BigML
BigML not only provides easeofuse, but it also offers flexibility in how you work with your data. This course serves as a handson 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
Assessment
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 7video 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, scikitlearn, 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
Assessment
Badge
Machine & Deep Learning Algorithms: Regression & Clustering
In this 8video 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
Assessment
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 10video course, learners can explore their application in Pandas ML. First, examine how to load data from a CSV (commaseparated values) file into a Pandas data frame and prepare the data for training a classification model. Then use the scikitlearn 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 scikitlearn. Learn how to build a linear regression model by using Pandas ML. Then evaluate a regression model by using metrics such as rsquare 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 KMeans algorithm, analyzing data clusters to determine unique characteristics. Finally, complete an exercise on regression, classification, and clustering.
10 videos 
1h 3m
Assessment
Badge
Automation Design & Robotics
In this 12video 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 HumanComputer 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
Assessment
Badge
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FREE ACCESS
COURSES INCLUDED
Deep Learning with Keras
In this 19video course, learners explore deep learning with Keras, including how to create and use neural networks with Keras for machine learning solutions. Begin with an overview of what neural networks are and their main components, followed by an introduction to Keras and its guiding principles. Observe how to configure Microsoft Cognitive Toolkit (CNTK) as your Keras backend; install and configure Keras; identify and work with both types of models available in Keras; and recognize features of commonlyused Keras layers and when to use them. Use Keras to make regression classifications and image classifications; Keras metrics to judge a model's performance; and Jupyter Notebooks with Keras. Next, download and load a data set from MNIST or CIFAR10; explore data sets in Keras; prepare your data in Keras by defining input and target tensors, and compile the model in Keras. Then train and test your neural network; evaluate and score the performance of neural networks in Keras, and make predictions using your data set in Keras. The closing exercise involves using a neural network to make predictions.
19 videos 
1h 54m
Assessment
Badge
COURSES INCLUDED
Research Topics in ML & DL
This course explores research being done in machine learning and deep learning. Topics covered include neural networks and deep neural networks. First, learners examine how to prevent neural networks from overfitting. You will explore research on multilabel learning algorithms, multilabel classification, and multipleoutput classifications, which are variants of the standard classification problem. Then examine deep learning algorithms, the enhanced performance of deeper neural networks that are more adept at automatic feature extraction. Next, ut facial alignment, regression tree ensembles, and deep features for scene recognition. Review ELM (Extreme Learning Machine), and how it is used to perform regression and multiclass classification.
13 videos 
41m
Assessment
Badge
Reinforcement Learning: Essentials
Explore machine learning reinforcement learning, along with the essential components of reinforcement learning that will assist in the development of critical algorithms for decisionmaking, in this 10video course. You will examine how to achieve continuous improvement in performance of machines or programs over time, along with key differences between reinforcement learning and machine learning paradigm. Learners will observe how to depict the flow of reinforcement learning by using agent, action, and environment. Next, you will examine different scenarios of state changes and transition processes applied in reinforcement learning. Then examine the reward hypothesis, and learn to recognize the role of rewards in reinforcement learning. You will learn that all goals can be described by maximization of the expected cumulative rewards. Continue by learning the essential steps applied by agents in reinforcement learning to make decisions. You will explore the types of reinforcement learning environments, including deterministic, observable, discrete or continuous, and singleagent or multiagent. Finally, you will learn how to install OpenAI Gym and OpenAl Universe.
10 videos 
29m
Assessment
Badge
Reinforcement Learning: Tools & Frameworks
This 9video course explores how to implement machine learning reinforcement learning by examining the terminology, including agents, the environment, state, and policy. This course demonstrates how to implement reinforcement learning by using Keras and Python; how to ensure that you can build a model; and how to launch and use Ubuntu, and VI editor to do score calculations. First, learn the role of the Markov decision process in which the agent observes the environment, with output consisting of a reward and the next state, and then acts upon it. You will explore Qlearning, a modelfree reinforcement learning technique, an asynchronous dynamic programming approach, and will learn about the Qlearning rule, and Deep Qlearning. Next, learn the steps to install TensorFlow for reinforcement learning, as well as framework, which is used for reinforcement learning provided by OpenAI. Then learn how to implement TensorFlow for reinforcement learning. Finally, you will learn to implement Qlearning using Python, and then utilize capabilities of OpenAl Gym and FrozenLake.
9 videos 
34m
Assessment
Badge
COURSES INCLUDED
Advanced Functionality of Microsoft Cognitive Toolkit (CNTK)
Microsoft Cognitive Toolkit provides powerful machine learning and deep learning algorithms for developing AI. Knowing which problems are easier to solve using Microsoft CNTK over other frameworks helps AI practitioners decide on the best software stack for a given application. In this course, you'll explore advanced techniques for working with Microsoft CNTK and identify which cases benefit most from MS CNTK. You'll examine how to load and use external data using CNTK and how to use its imperative and declarative APIs. You'll recognize how to carry out common AI development tasks using CNTK, such as working with epochs and batch sizes, model serialization, model visualization, feedforward neural networks, and machine learning model evaluation. Finally, you'll implement a series of practical AI projects using Python and MS CNTK.
15 videos 
47m
Assessment
Badge
Working With Microsoft Cognitive Toolkit (CNTK)
Microsoft Cognitive Toolkit (CNTK) is an open source framework for distributed deep learning suitable for commercial applications. It's primarily used to develop neural networks but can also be used for machine learning and cognitive computing. It supports multiple languages and can easily be used in the cloud. These factors make CNTK a good fit for various AI projects. In this course, you'll explore the basic concepts required to work with Microsoft CNTK. You'll compare other frameworks with CNTK, examine the process of creating machine learning and deep learning models with CNTK, and learn how it can be used with several cloud services. You'll move on to learn where to access CNTK documentation, community, and installation guidelines. Finally, you'll use CNTK to predict diabetes using retina scans.
16 videos 
51m
Assessment
Badge
COURSES INCLUDED
Fundamentals of Sequence Model: Artificial Neural Network & Sequence Modeling
Explore artificial neural networks (ANNs), their essential components, tools, and frameworks for their implementation in machine learning solutions. In this 9video course, you will discover recurrent neural networks (RNNs) and how they are implemented. Key concepts covered here include perceptrons and the computational role they play in ANNs; learning features and characteristics of ANNs and how components are used to build a model; and learning prominent tools and frameworks used to implement sequence models and ANNs. Next, you will learn about sequence modeling as it pertains to language models; RNNs and their capabilities and components; and how to specify RNN types and their implementation features. Learners will then explore the concept of linear and nonlinear functions and classify how they are used with perceptrons; explore the concept of backpropagation and usage of backpropagation algorithm in neural networks; and examine the concept of activation functions and how linear and nonlinear activations are utilized in neural networks. Finally, you will see how to implement perceptrons with Python, and how to use modeling tools and architectures and applications of sequence models.
9 videos 
36m
Assessment
Badge
Fundamentals of Sequence Model: Language Model & Modeling Algorithms
In this 7video course, learners can explore the concepts of language modeling, natural language processing (NLP), and sequence generation for NLP. Prominent machine learning modeling algorithms such as vanishing gradient problem, gated recurrent units (GRUs), and long shortterm memory (LSTM) network are also covered. Key concepts studied in this course include language models, one of the most important parts of NLP. and how to implement NLP along with its essential components; learning the process and approach of generating sequence for NLP; and vanishing gradient problem implementation approaches to overcome the problem of taking longer times to achieve convergence. Then, learn about features and characteristics of GRUs used to resolve issues with vanishing gradient problems, and learn the problems and drawbacks of implementing shortterm memory and LSTM as modeling solutions. In the concluding exercise, learners will review the essential components and prominent applications of language modeling and specify some of the solutions for vanishing gradient problems.
7 videos 
18m
Assessment
Badge
Build & Train RNNs: Neural Network Components
Explore the concept of artificial neural networks (ANNs) and components of neural networks, and examine the concept of learning and training samples used in supervised, unsupervised, and reinforcement learning in this 10video course. Other topics covered in this course include network topologies, neuron activation mechanism, training sets, pattern recognition, and the need for gradient optimization procedure for machine learning. You will begin the course with an overview of ANN and its components, then examine the artificial network topologies that implement feedforward, recurrent, and linked networks. Take a look at the activation mechanism for neural networks, and the prominent learning samples that can be applied in neural networks. Next, compare supervised learning samples, unsupervised learning samples, and reinforcement learning samples, and then view training samples and the approaches to building them. Explore training sets and pattern recognition and, in the final tutorial, examine the need for gradient optimization in neural networks. The exercise involves listing neural network components, activation functions, learning samples, and gradient descent optimization algorithms.
10 videos 
36m
Assessment
Badge
Build & Train RNNs: Implementing Recurrent Neural Networks
Learners will examine the concepts of perception, layers of perception, and backpropagation, and discover how to implement recurrent neural network by using Python, TensorFlow, and Caffe2 in this 10video course. Begin by taking a look at the essential features and processes of implementing perception and backpropagation in machine learning neural networks. Next, you will compare singlelayer perception and multilayer perception and describe the need for layer management. You will learn about the steps involved in building recurrent neural network models; building recurrent neural networks with Python and TensorFlow; implementing long shortterm memory (LSTM) by using TensorFlow, and building recurrent neural networks with Caffe2. Caffe is a deep learning framework. Building deep learning language models using Kerasan open source neural network librarywill be explored in the final tutorial of the course. The concluding exercise entails implementing recurrent neural networks by using TensorFlow and Caffe2 and building deep learning language models by using Keras.
10 videos 
48m
Assessment
Badge
Convolutional Neural Networks: Fundamentals
Learners can explore the concepts of convolutional neural network (CNN); the underlying architecture, principles, and methods needed to build a CNN; and its implementation in a deep neural network. In this 12video course, you will examine visual perception, and the ability to interpret the surrounding environment by using light in the visible spectrum. First, learn about CNN architecture; how to analyze the essential layers; and the impact of an initial choice of layers. Next, you will learn about nonlinearity in the first layer, and the need for several pooling techniques. Then learn how to implement a convolutional layer and sparse interaction. Examine the hidden layers of CNN, which are convolutional layers, ReLU (rectified linear unit) layers, or activation functions, the pooling layers, the fully connected layer, and the normalization layer. You will examine machine learning semantic segmentation to understand an image at the pixel level, and its implementation using Texton Forest and a random based classifier. Finally, this course examines Gradient Descent and its variants.
12 videos 
45m
Assessment
Badge
Convolutional Neural Networks: Implementing & Training
This course explores machine learning convolutional neural networks (CNNs), which are popular for implementation in image and audio processing. Learners explore AI (artificial intelligence), and the issues surrounding implementation, how to approach organizational talent and strategy, and how to prepare for AI architecture in this 8video course. You will learn to use the Google Colab tool, and to implement image recognition classifier by using CNN, Keras, and TensorFlow. Next, learn to install and implement a model, and use it for image classification. You will examine the artificial neural network ResNet (residual neural network), and how it builds on constructs known from pyramidal cells and cerebral cortex. You will also study PyTorch, an opensource machine learning library that enables fast, flexible experimentation, and efficient production through a hybrid front end, and learn to use the PyTorch ecosystem tool to develop and implement neural networks. Finally, this course demonstrates how to create a data set by using Training CNN by using PyTorch to categorize garments.
8 videos 
30m
Assessment
Badge
Getting Started with Neural Networks: Biological & Artificial Neural Networks
Learners can explore fundamental concepts of biological and artificial neural networks, computational models that can be implemented with neural networks, and how to implement neural networks with Python, in this 12video course. Begin with a look at characteristics of machine learning biological neural networks that inspired artificial neural networks. Then explore components of biological neural networks and the signal processing mechanism. Next, take a look at the essential components of the structure of artificial neural networks; learn to recognize the layered architecture of neural networks; and observe how to classify various computational models that can be implemented by using neural networks paradigm. Examine neurons connectivity, by describing the interconnection between neurons involving weights and fixed weights. This leads on to threshold functions in neural networks and the basic logic gates of AND, OR, and XNOR. Implement neural networks by using Python and the core libraries provided by Python for neural networks; create a neural network model using Python, Keras, and TensorFlow, and finally, view prominent neural network use cases. The concluding exercise involves implementing neural networks.
12 videos 
58m
Assessment
Badge
Getting Started with Neural Networks: Perceptrons & Neural Network Algorithms
Discover the basics of perceptrons, including single layer and multilayer, and the roles of linear and nonlinear functions in this 10video course. Learners will explore how to implement perceptrons and perceptron classifiers by using Python for machine learning solutions. Key concepts covered in this course include perceptrons, singlelayer and multilayer perceptrons, and the computational role they play in artificial neural networks; learning the algorithms that can be used to implement singlelayer perceptron training models; and exploring multilayer perceptrons and illustrating the algorithmic difference from singlelayer perceptrons. Next, you will learn to classify the role of linear and nonlinear functions in perceptrons; learn how to implement perceptrons by using Python; and learn approaches and benefits of using the backpropagation algorithm in neural networks. Then learn the uses of linear and nonlinear activation functions in artificial neural networks; learn to implement a simple perceptron classifier using Python; and learn the benefits of using the backpropagation algorithm in neural networks and implement perceptrons and perceptron classifiers by using Python.
10 videos 
44m
Assessment
Badge
Training Neural Networks: Implementing the Learning Process
In this 13video course, learners can explore how to work with machine learning frameworks and Python to implement training algorithms for neural networks. You will learn the concept and characteristics of perceptrons, a single layer neural network that aggregates the weighted sum of inputs, and returns either zero or one, and neural networks. You will then explore some of the prominent learning rules that to apply in neural networks, and the concept of supervised and unsupervised learning. Learn several types of neural network algorithms, and several training methods. Next, you will learn how to prepare and curate data by using Amazon SageMaker, and how to implement an artificial neural network training process using Python, and other prominent and essential learning algorithms to train neural networks. You will learn to use Python to train artificial neural networks, and how to use Backpropagation in Keras to implement multilayer perceptrons or neural networks. Finally, this course demonstrates how to implement regularization in multilayer perceptrons by using Keras.
13 videos 
1h 38m
Assessment
Badge
Training Neural Networks: Advanced Learning Algorithms
This 15video course explores how to design advanced machine learning algorithms by using training patterns, pattern association, the Hebbian learning rule, and competitive learning. First, learners examine the concepts and characteristics of online and offline training techniques in implementing artificial neural networks, and different training patterns in teaching inputs that are used in implementing artificial neural networks. You will learn to manage training samples, and how to use Google Colab to implement overfitting and underfitting scenarios by using baseline models. You will examine regularization techniques to use in training artificial neural networks. This course then demonstrates how to train previouslybuilt neural network models using Python, and the prominent training algorithms to implement pattern associations. Next, learn the architecture and algorithm associated with learning vector quantization; the essential phases involved in implementing Hebbian learning; how to implement Hebbian learning rule using Python; and the steps involved in implementing competitive learning. Finally, you will examine prominent techniques to use to optimize neural networks, and how to debug neural networks.
15 videos 
1h 40m
Assessment
Badge
Building Neural Networks: Development Principles
Explore essential machine learning components used to learn, train, and build neural networks and prominent clustering and classification algorithms in this 12video course. The use of hyperparameters and perceptrons in artificial neuron networks (ANNs) is also covered. Learners begin by studying essential ANN components required to process data, and also different paradigms of learning used in ANN. Examine essential clustering techniques that can be applied on ANN, and the roles of the essential components that are used in building neural networks. Next, recall the approach of generating deep neural networks from perceptrons; learn how to classify differences between models and hyperparameters and specify the approach of tuning hyperparameters. You will discover types of classification algorithm that can be used in neural networks, and features of essential deep learning frameworks for building neural networks. Explore how to choose the right neural network framework for neural network implementations from the perspective of usage scenarios and fitment model, and define computational models that can be used to build neural network models. The concluding exercise concerns ANN training and classification.
12 videos 
1h 20m
Assessment
Badge
Building Neural Networks: Artificial Neural Networks Using Frameworks
This 13video course helps learners discover how to implement various neural networks scenarios by using Python, Keras, and TensorFlow for machine learning. Learn how to optimize, tune, and speed up the processes of artificial neural networks (ANN) and how to implement predictions with ANN is also covered. You will begin with a look at prominent building blocks involved in building a neural network, then recalling the concept and characteristics of evolutionary algorithms, gradient descent, and genetic algorithms. Learn how to build neural networks with Python and Keras for classification with Tensorflow as the backend. Discover how to build neural networks by using PyTorch; implement object image classification using neural network algorithms; and define and illustrate the use of learning rates to optimize deep learning. Examine various parameters and approaches of optimizing neural network speed; learn how to select hyperparameters and tune for dense networks by using Hyperas; and build linear models with estimators by using the capabilities of TensorFlow. Explore predicting with neural networks, temporal prediction optimization, and heterogenous prediction optimization. The concluding exercise involves building neural networks.
13 videos 
1h 54m
Assessment
Badge
Convo Nets for Visual Recognition: Filters and Feature Mapping in CNN
In this 13video course, you will explore the capabilities and features of convolutional networks for machine learning that make it a recommended choice for visual recognition implementation. Begin by examining the architecture and the various layers of convolutional networks, including pooling layer, convo layer, normalization layer, and fully connected layer, and defining the concept and types of filters in convolutional networks along with their usage scenarios. Learn about the approach to maximizing filter activation with Keras; define the concept of feature map in convolutional networks and illustrate the approach of visualizing feature maps; and plot the map of the first convo layer for given images, then visualize the feature map output from every block in the visual geometry group (VGG) model. Look at optimization parameters for convolutional networks, and hyperparameters for tuning and optimizing convolutional networks. Learn about applying functions on pooling layer; pooling layer operations; implementing pooling layer with Python, and implementing convo layer with Python. The concluding exercise involves plotting feature maps.
13 videos 
1h 6m
Assessment
Badge
Convo Nets for Visual Recognition: Computer Vision & CNN Architectures
Learners can explore the machine learning concept and classification of activation functions, the limitations of Tanh and the limitations of Sigmoid, and how these limitations can be resolved using the rectified linear unit, or ReLU, along with the significant benefits afforded by ReLU, in this 10video course. You will observe how to implement ReLU activation function in convolutional networks using Python. Next, discover the core tasks used in implementing computer vision, and developing CNN models from scratch for object image classification by using Python and Keras. Examine the concept of the fullyconnected layer and its role in convolutional networks, and also the CNN training process workflow and essential elements that you need to specify during the CNN training process. The final tutorial in this course involves listing and comparing the various convolutional neural network architectures. In the concluding exercise you will recall the benefits of applying ReLU in CNNs, list the prominent CNN architectures, and implement ReLU function in convolutional networks using Python.
10 videos 
48m
Assessment
Badge
ConvNets: Introduction to Convolutional Neural Networks
Explore convolutional neural networks, their different types, and prominent use cases for machine learning, in this 10video course. Learners will study the different layers and parameters of convolutional neural networks and their roles in implementing and addressing image recognition and classification problems. Key concepts covered in this course include the working mechanisms of convolutional neural networks, and the different types of convolutional neural networks that we can implement; and problems associated with computer vision, along with the prominent techniques to manage them. Next, you will learn about the role of neural networks and convolutional neural networks in implementing and addressing image recognition and classification problems; observe the prominent layers and parameters of convolutional neural networks for image classification; and learn to see the convolutional layer from a mathematical perspective, while recognizing the mathematical elements that enter into the convolution operations. Finally, learners will be shown how to build a convolutional neural network for image classification by using Python.
10 videos 
1h
Assessment
Badge
ConvNets: Working with Convolutional Neural Networks
Learners can explore the prominent machine learning elements that are used for computation in artificial neural networks, the concept of edge detection, and common algorithms, as well as convolution and pooling operations, and essential rules of filters and channel detection, in this 10video course. Key concepts covered here include the architecture of neural networks, along with essential elements used for computations by focusing on Softmax classifier; how to work with ConvNetJS as a Javascript library and train deep learning models; and learning about the edge detection method, including common algorithms that are used for edge detection. Next, you will examine the series of convolution and pooling operations used to detect features; learn the involvement of math in convolutional neural networks and essential rules that are applied on filters and channel detection; and learn principles of convolutional layer, activation function, pooling layer, and fullyconnected layer. Learners will observe the need for activation layers in convolutional neural networks and compare prominent activation functions for deep neural networks; and learn different approaches to improve convolution neural networks and machine learning systems.
10 videos 
42m
Assessment
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Improving Neural Networks: Neural Network Performance Management
In this 12video course, learners can explore machine learning problems that can be addressed with hyperparameters, and prominent hyperparameter tuning methods, along with problems associated with hyperparameter optimization. Key concepts covered here include the iterative workflow for machine learning problems, with a focus on essential measures and evaluation protocols; steps to improve performance of neural networks, along with impacts of data set sizes on neural network models and performance estimates; and impact of the size of training data sets on quality of mapping function and estimated performance of a fit neural network model. Next, you will learn the approaches of identifying overfitting scenarios and preventing overfitting by using regularization techniques; learn the impact of bias and variances on machine learning algorithms, and recall the approaches of fixing high bias and high variance in data sets; and see how to trade off bias variance by building and deriving an ideal learning curve by using Python. Finally, learners will observe how to test multiple models and select the right model by using Scikitlearn.
12 videos 
1h 56m
Assessment
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Improving Neural Networks: Loss Function & Optimization
Learners can explore the concept of loss function, the different types of Loss function and their impact on neural networks, and the causes of optimization problems, in this 10video course. Examine alternatives to optimization, the prominent optimizer algorithms and their associated properties, and the concept of learning rates in neural networks for machine learning solutions. Key concepts in this course include learning loss function and listing various types of loss function; recognizing impacts of the different types of loss function on neural networks models; and learning how to calculate loss function and score by using Python. Next, learners will learn to recognize critical causes of optimization problems and essential alternatives to optimization; recall prominent optimizer algorithms, along with their properties that can be applied for optimization; and how to perform comparative optimizer analysis using Keras. Finally, discover the relevance of learning rates in optimization and various approaches of improving learning rates; and learn the approach of finding learning rate by using RMSProp optimizer.
10 videos 
1h 3m
Assessment
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Improving Neural Networks: Data Scaling & Regularization
Explore how to create and optimize machine learning neural network models, scaling data, batch normalization, and internal covariate shift. Learners will discover the learning rate adaptation schedule, batch normalization, and using L1 and L2 regularization to manage overfitting problems. Key concepts covered in this 10video course include the approach of creating deep learning network models, along with steps involved in optimizing networks, including deciding size and budget; how to implement the learning rate adaptation schedule in Keras by using SGD and specifying learning rate, epoch, and decay using Google Colab; and scaling data and the prominent data scaling methods, including data normalization and data standardization. Next, you will learn the concept of batch normalization and internal covariate shift; how to implement batch normalization using Python and TensorFlow; and the steps to implement L1 and L2 regularization to manage overfitting problems. Finally, observe how to implement gradient descent by using Python and the steps related to library import and data creation.
10 videos 
1h 37m
Assessment
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Machine Learning & Data Analytics
Explore critical machine learning (ML) and deep learning concepts and the various categorizations of algorithms and their implementations using Python.
10 videos 
1h 3m
Assessment
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Supervised, Unsupervised & Deep Learning
Discover how to implement various supervised and unsupervised algorithms of machine learning using Python, with the primary focus of clustering and classification.
10 videos 
1h 30m
Assessment
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Deep Learning & Neural Network Implementation
Discover how to implement neural network with data sampling and workflow models using scikitlearn, and explore the pre and post model approaches of implementing machine learning workflows.
10 videos 
1h 2m
Assessment
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Implementing ML Algorithm Using scikitlearn
Discover how to implement data classification using various techniques, including Bayesian, and learn to apply various search implementations with Python and scikitlearn.
10 videos 
1h 13m
Assessment
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Implementing Robotic Process Automation
Discover how to implement Robotic Process Automation (RPA) using Python, and explore various RPA frameworks with the practical implementation of UiPath.
10 videos 
1h 2m
Assessment
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AI Fundamentals
Discover the fundamental concepts of the technologies driving artificial Intelligence (AI).
10 videos 
1h 3m
Assessment
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Machine Learning Implementation
Explore the various machine learning techniques and implementations using Java libraries, and learn to identify certain scenarios where you can implement algorithms.
12 videos 
1h 26m
Assessment
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Neural Network & Neuroph Framework
Discover the essential features and capabilities of Neuroph framework and Neural Networks, and also how to work with and implement Neural Networks using Neuroph framework.
16 videos 
1h 47m
Assessment
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Neural Network & NLP Implementation
Discover how to implement advanced neural network using DL4j and explore the concept of NLP and its implementation using OpenNLP Java library.
11 videos 
56m
Assessment
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Expert Systems & Reinforcement Learning
Explore the concepts of expert system along with its Implementation using Java based frameworks, and examine the implementation and usages of ND4J and Arbiter to facilitate optimization.
12 videos 
47m
Assessment
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Using BigML: Building Supervised Learning Models
The versatility of BigML allows you to build supervised learning models without much complexity. In this course, you'll practice constructing a selection of supervised learning models using BigML. You'll start by building an ensemble of decision trees to perform binary classification. Next, you'll build a linear regression model to predict the values of homes in a particular region. You'll then train and evaluate a logistic regression model to illustrate how it can be used to solve similar problems to those solved using ensemble methods. Another BigML capability you'll explore is building a time series plot to make various forecasts. In each demonstration, you'll delve into some optional configurations for the model being trained. Lastly, you'll use the OptiML feature to find the optimal model for your data.
14 videos 
1h
Assessment
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Using BigML: Unsupervised Learning
BigML includes various unsupervised learning models used to gain insights into your data. These insights can help make pivotal business decisions or act as a starting point to build supervised learning models. In this course, you'll build several unsupervised learning models and analyze the results they produce. You'll start by creating clusters from a dataset and examining how data points within a cluster share similarities. You'll move on to uncover associations in a dataset about items purchased on an ecommerce platform. Next, you'll apply topic modeling to extract the topics discussed in a collection of texts. Following this, you'll transform a dataset containing multiple fields into a handful of principal components using Principal Component Analysis, or PCA. Finally, you'll explore the detection of anomalies in your dataset.
9 videos 
1h
Assessment
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COURSES INCLUDED
Bayesian Methods: Bayesian Concepts & Core Components
This 11video course explores the machine learning concepts of Bayesian methods and the implementation of Bayes' theorem and methods in machine learning. Learners can examine Bayesian statistics and analysis with a focus on probability distribution and prior knowledge distribution. Begin with a look at the concept of Bayesian probability and statistical inference, then move on to the concept of Bayesian theorem and its implementation in machine learning. Next, learn about the role of probability and statistics in Bayesian analysis from the perspective of frequentist probability and subjective probability paradigms. You will examine standard probability, continuous distribution, and discrete distribution, and recall the essential elements of Bayesian statistics including prior distribution, likelihood function, and posterior inference. Recognize the implementation of prominent Bayesian methods including inference, statistical modeling, influence of prior belief, and statistical graphics. Describe prior knowledge and compare the differences between noninformative prior distribution and informative prior distribution. The steps involved in Bayesian analysis, including modeling data, deciding prior distribution, likelihood construction, and posterior distribution are also covered. The concluding exercise focuses on Bayesian statistics and analysis.
11 videos 
1h
Assessment
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Implementing Bayesian Model and Computation with PyMC
Learners can examine the concept of Bayesian learning and the different types of Bayesian models in this 12video course. Discover how to implement Bayesian models and computations by using different approaches and PyMC for your machine learning solutions. Learners start by exploring critical features of and difficulties associated with Bayesian learning methods, and then take a look at defining the Bayesian model and classifying singleparameter, multiparameter, and hierarchical Bayesian models. Examine the features of probabilistic programming and learn to list the popular probabilistic programming languages. You will look at defining Bayesian models with PyMC and arbitrary deterministic function and generating posterior samples with PyMC models. Next, learners recall the fundamental activities involved in the PyMC Bayesian data analysis process, including model checking, evaluation, comparison, and model expansion. Delve into the computation methods of Bayesian, including numerical integration, distributional approximation, and direct simulation. Also, look at computing with Markov chain simulation, and the prominent algorithms that can be used to find posterior modes based on the distribution approximation. The concluding exercise focuses on Bayesian modeling with PyMC.
12 videos 
47m
Assessment
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Bayesian Methods: Advanced Bayesian Computation Model
This 11video course explores advanced Bayesian computation models, as well as how to implement Bayesian modeling with linear regression, nonlinear, probabilistic, and mixture models. In addition, learners discover how to implement Bayesian inference models with PyMC3. First, learn how to build and implement Bayesian linear regression models by using Python for machine learning solutions. Examine prominent hierarchical linear models from the perspective of regression coefficients. Then view the concept of probability models and use of Bayesian methods for problems with missing data. You will discover how to build probability models by using Python, and examine coefficient shrinkage with nonlinear models, nonparametric models, and multivariate regression from nonlinear models. Examine fundamental concepts of Gaussian process models; the approaches of classification with mixture models and regression with mixture models; and essential properties of Dirichlet process models. Finally, learn how to implement Bayesian inference models in Python with PyMC3. The concluding exercise recalls hierarchical linear models from the perspective of regression coefficients, and asks learners to describe the approach of working with generalized linear models, and implement Bayesian inference by using PyMC3.
11 videos 
51m
Assessment
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COURSES INCLUDED
Keras  a Neural Network Framework
Keras is a deep learning package suitable for beginners. Although it is applied in multiple standard deep learning use cases, it is also used by commercialgrade products. To facilitate this, Keras provides additional, flexible options on top of the wellknown Sequential API, which allow you to customize and create various neural networks. To utilize this, however, requires a more indepth knowledge of the Keras framework. In this course, you'll develop the core skills needed to work with the Keras framework. You'll explore the advantages and disadvantages of using Keras over other frameworks, and examine how Keras can be used with TensorFlow. You'll move on to recognize how Keras is used for machine learning and deep learning. Finally, you'll implement two deep learning projects using the Keras framework.
15 videos 
47m
Assessment
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Working With the Keras Framework
Keras provides a quick way to implement, train, and evaluate robust neural networks in Python. Using Keras for AI development for prototyping AI is standard practice and AI practitioners need to know why and how to use Keras for particular AI implementations. In this course, you'll explore advanced techniques for working with the Keras framework. You'll recognize how Keras is different from other AI frameworks and identify cases in which it is advantageous to use Keras. You'll examine the functionality of the Keras Sequential model and Functional API and the role of multiple deep learning layers present in Keras. Finally, you will work with practical AI projects developed using Keras and troubleshoot common problems related to model training and evaluation.
16 videos 
50m
Assessment
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COURSES INCLUDED
AWS Certified Machine Learning: Data Engineering, Machine Learning, & AWS
Machine learning (ML) has become indispensable across all industries. With staggering amounts of data generated globally every second, it's impossible to make sense of it without using such advanced data analytics. The AWS Certified Machine Learning  Specialty certification is one of the most coveted yet challenging certs a data engineer or scientist can get. To pass the associated exam, candidates must demonstrate knowledge of various machine learning concepts and the ability to solve realworld business challenges. Use this course to prepare for acquiring this valuable certification. Get to grips with key data engineering and machine learning terminology, concepts, tools, tasks, and workflows. Then, dive into how the AWS Machine Learning platform is used for realworld applications. Upon completing this course, you'll recognize key ML concepts and how to prepare datasets, develop ML models, and optimize models for improved predictive accuracy.
12 videos 
35m
Assessment
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AWS Certified Machine Learning: Amazon S3 Simple Storage Service
Amazon Simple Storage Service (S3) is widely used for many machine learning applications. Using Amazon S3, you can quickly and easily run machine learning algorithms on large databases using remote machines. In this course, you'll explore the various data formats Amazon S3 uses for machine learning pi