Artificial Intelligence, Machine and Deep Learning
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AI, Machine and Deep Learning
- Artificial intelligence, machine learning and deep learning are rapidly developing fields that offer many potential real-world applications.
- Our Effective MBA - Artificial Intelligence, Machine and Deep Learning specialization will teach you the most necessary skills and help you navigate in the areas of artificial intelligence programs, machine and deep learning principles and benefits of Python programming language in the context of AI and Machine learning.
- By leveraging the combined capabilities you can become AI, Machine and Deep Learning specialist or consultant to create innovative solutions for a better tomorrow.
- In addition, the Effective MBA - AI, Machine, and Deep Learning specialization addresses deep learning, applying these principles in practice and other vital areas.
- Part of this specialization's content are tips and information on steering approaches and work styles of successful company leaders, such as AirBnB, BuzzFeed, Microsoft, Adidas, Nike, Kraft Heinz Company, Tesla, JetBlue, Danone, Alphabet, SC Johnson, Infor, IBM, Reddit, YouTube, LinkedIn, Yum!, Carnival, Uber, Casper, Levi´s, Trivia and others.
AI, Machine and Deep Learning consists of 10 Modules
1. Artificial Intelligence and Machine Learning I.
Content includes: The history of AI, Machine learning, Technical approaches to AI, AI in robotics, Integrating AI with big data, Avoiding pitfalls, Describing how to work with data, Applying machine learning principles, Distinguish different types of machine learning, Identifying problems that use machine learning, Create decision trees, Explain how to select the best algorithm, What is machine learning (ML), ML vs. deep learning vs. AI, Handling common challenges in ML, Plotting continuous features, Continuous and categorical data cleaning, Measuring success, Overfitting, and underfitting, Tuning hyperparameters, Evaluating a model, Models vs. Algorithms, Cleaning continuous and categorical variables, Tuning hyperparameters, Pros and cons of logistic regression, Fitting a support vector machines model, When to consider using a multilayer perceptron model, Using the random forest algorithm, Fitting a basic boosting model, etc.
2. Artificial Intelligence and Machine Learning II.
Content includes: What are XAI and IML, Why Isolating a Varieable´s Contribution is Difficult, Black Box Model 101, KNIME for XAI and IML XAI Techniques: Global Explanations, Techniques for Local Explanations, IML TEchniques, Experimental Design and Statistical Controls, Conditional Probability and Bayes´ Theorem, Prediction and Proof with Bayesian statistics, Causal Modeling with Structural Equation Modeling (SEM), Causal Modeling with Bayesian Networks, Explain the significance of a p-value for hypothesis testing, Define causation, explain the difference between correlation and causation, and illustrate how to demonstrate causation, Explain how to detect multicollinearity and describe a strategy for dealing with it, Define induction, deduction, falsification, and counterfactual, and then illustrate their significance in model Evaluation, Explain the diminishing utility of p-values with an increasing number of model parameters, Explain how to test model performance in data mining, Describe the conflicting goals and philosophy of statistics and data mining, Reinforcement Learning, Reinforcement Learning Algorithms, Monta Carlo Method, Temporal Difference Methods, Modified Forms of Reinforcement, etc.
3. Artificial Intelligence and Machine Learning III.
Content includes: Review of the challenges of AI, Applying narrow AI to a decision, Defining two effective approaches used when dealing with AI, Examining supervised and unsupervised learning, Explaining harassment by AI, Identifying three concepts that distributive justice is based on, What is XAI, XAI benefits and limitations, Humans vs. Computers, XAI business examples, Investing in XAI, Transformers in NLP, Training Transformers and Their Architecture, Large Language Models, Introducing Decision Trees, Introducing the C5.0 Algorithm, Introducing Classification Trees, Introducing Regression Trees, NPL and Transformers, BERT and Transfer Learning, Transformer Architecture and BERT, Text Classification, etc
4. GDPR and Data Privacy
Content includes: Understanding and Prioritizing Data Privacy, Creating a Culture of Privacy, Learning GDPR, GDPR Compliance: Essential Training, Achieving GDPR Compliance with Microsoft Technologies
5. Information privacy within the US context
Content includes: CIPP/US Cert Prep: The Basics (2020), CIPP/US Cert Prep: 1 U.S. Privacy Environment, CIPP/US Cert Prep: 2 Private Sector Privacy, CIPP/US Cert Prep: 3 Government and Court Access to Information, CIPP/US Cert Prep: 4 Workplace Privacy, CIPP/US Cert Prep: 5 State Privacy Laws, Privacy in the New World of Work, California Consumer Privacy Act (CCPA) Essential Training
6. AI and ML in practical applications
Content includes: Power BI, Analyzing a Single Variable, Measuring Relationships between Variables, Utilizing AI Visuals to Ask What-If Questions, Analyzing Time Series Data, Creating and Sharing Analysis, Chatbots with Azure, Introductions to Chatbots, Chatbot Terminology and Architecture, Design a Chatbot, Enhancing Your Chatbot Actions, Chatbots via Google Dialogflow, Dialogflow building blocks, Setting up a Dialogflow account, Creating intents, Importing and exporting an agent, Creating entities and parameters, Adding follow-up intents, Input, and output context, Creating a fulfillment, Integrating a chatbot with your website, Machine Learning with Scikit-Learn, Why use Scikit-learn, Supervised vs. unsupervised learning, Linear and logistic regression, Decision trees and random forests, K-means clustering, Principal component analysis (PCA), etc.
7. Python I.
Content includes: Machine Learning with Python, Machine Learning, Collecting Data for Machine Learning, Understanding Data for Machine Learning, Preparation Data for Machine Learning, Types of Machine Learning Models, Decision Trees, Working with Classification Trees, Working with Regression Trees, Understanding K-Means Clustering, Segmenting Data with K-Means Clustering, Association Rules, Discovering Patterns with Association Rules, Neural Networks in Python, Choosing a Neural Network, The Building Blocks of Neural Networks, Building Your Network, Training Your Network, Make a Segment Display Classifier, etc.
8. Python II.
Content includes: Regression, Logistic Regression, Classifying Data with Logistic Regression, Advanced NLP with Python for Machine Learning, Review NLP Basics, word2vec, doc2vec, Recurrent Neural Networks, Compare Advance NLP Techniques on an ML Problem, NLP with Python for Machine Learning Essential Training, NLP Basic, Supplemental Data Cleaning, Vectorizing Raw Data, Feature Engineering, Building Machine Learning Classifiers, etc.
9. Deep learning and neural networks
Content includes: Deep Learning, Introduction to Deep Learning, Neural Network Architecture, Training a Neural Network, Deep Learning Examples, Model Optimization, and Tuning, Introduction to Deep Learning Optimization, Tuning the Deep Learning Network, Tuning Back Propagation, Overfitting Management, Model Tuning Exercise, Building Deep Learning Applications with TensorFlow, What's TensorFlow, Hardware, software, and language requirements, Creating a TensorFlow model, Training a deep learning model with TensorFlow, Visualizing the computational graph, Adding custom visualizations to TensorBoard, Exporting models for use with Google Cloud, Neural Networks, Differentiate between perceptrons and sigmold neurons, Describe the three types of layers of a neural network, Identify the purpose of weights, Recognize the steps for initializing a neural network, Explain how backpropagation improves accuracy, Evaluate the effectiveness of supervised and unsupervised learning methods in a given situation, Recurrent Neural Networks, Introduction to RNNs, RNN Concepts, An RNN Example, RNN Architectures, An LSTM Example, Word Embeddings, Spam Detection with Word Embeddings, Neural Networks and Convolutional Neural Networks Essential Training, Neurons and artificial neurons, Components of neural networks, Neural network visualization, Neural network implementation in Keras, Compiling and training a neural network model, Accuracy and evaluation of a neural network model, Convolutional neural networks in Keras, Enhancements to convolutional neural networks, Working with VGG16, etc.
Content includes: Cybersecurity Foundations, Cybersecurity at Work, Cybersecurity: Key Policies and Resources, Cybersecurity Awareness: Phishing and Whaling, Cybersecurity Awareness: Malware Explained
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"My industry-related management experience has been given further exposure and a solid foundation in marketing, finance, business innovation, and managerial accounting, thereby empowering an engineer like me to start a business with confidence."
"EDU Effective MBA is one of the best affordable online MBA available currently. This Effective MBA course is comprehensive and designed to meet the requirements of working professionals, managers, and people aspiring to become managers. The modules and topics covered under this course are worth the value."
"Efficient, effective and good way of learning. We can study anytime and anywhere with high-quality materials and an excellent e-learning platform. I got many things from this program: academic knowledge, theory, and practical approaches obtained from experienced lecturers."