Machine Learning Crash Course for Executives - by Deloitte
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: aac, 48000 Hz
Language: English | VTT | Size: 263 MB | Duration: 1h 52m
What you'll learn
Machine Learning
Artificial Intelligence
AI
Deep Learning
Decision Trees
Ensemble Learning
Anomaly Detection
Clustering
Recommender Systems
Neural Networks
NLP
Natural Language Processing
LSTM
GRU
Data Science
Python
Keras
Gradient Boosting
Bagging
Random Forest
XGBoost
Logistic Regression
Regression
Classification
gradient
Model
Model Parameters
Computer Vision
Neural Networks
GAN
Unsupervised Learning
Supervised Learning
Natural Language Processing
Variational Autoencoders
Requirements
No Prerequisites
Description
Deloitte's crash course on AI, Machine Learning and Deep Learning Programme is provides short, one stop learning opportunity for everybody that has an interest to understand AI, Machine Learning and Deep Learning beyond the buzzwords. After completing this course, participants will be able to prioritise, lead and manage AI initiatives. Deloitte developed this course according to following design principles:
One stop shop for AI, Machine Learning & Deep Learning
Short Learning Units - Microlearning
No prerequisites
Detailed Content of the Course:
MODULE 1: DEFINITIONS AND FUNDAMENTALS
LU 1.1: What is the difference between AI, Machine Learning and Deep Learning?
LU 1.2: What is the difference between Supervised, Unsupervised and Reinforcement Learning?
LU 1.3: How do machines learn?
LU 1.3.1: Loss Function and Mean Squared Error (MSE)
LU 1.3.2: Model Parameters
LU 1.3.3: Gradient Descent
LU 1.4: Regression and Classification
LU 1.4.1: Regression Case Study - Stock Price Prediction General Electric (GE)
LU 1.4.2: Classification
LU 1.4.2.1: Sigmoid Model & Logistic Regression
LU 1.4.2.2: How do we measure the performance of a classifier?
LU 1.4.2.3: Case Study Classification - Diabetes Prediction - Python, Pandas, Colab
LU 1.5: Machine Learning & Linear Algebra
LU 1.6: Underfitting, Overfitting and Regularization
MODULE 2: MACHINE LEARNING - CLASSIFIERS
LU 2.1: Decision Trees
LU 2.1.1: What are Decision Trees?
LU 2.1.2: Ensemble learning
LU 2.1.2.1: Bagging
LU 2.1.2.2: Boosting
LU 2.2: How to deploy a Machine Learning model in production
LU 2.2.1: Spam filter - Python & Visual Studio
MODULE 3: UNSUPERVISED LEARNING
LU 3.1: Anomaly Detection Systems
LU 3.1.1: Statistical Methods
LU 3.1.2: Density-based methods
LU 3.1.3: Isolation Forest
LU 3.2: Clustering
LU 3.3: Recommender Systems
MODULE 4: DEEP LEARNING
LU 4.1: History of Deep Learning
LU 4.1.1: The Perceptron
LU 4.1.2: From Perceptron to Neural Network
LU 4.1.3: Neural Networks and Deep Learning
LU 4.2: Anatomy of a Neural Network
LU 4.3: Backpropagation
LU 4.4: Regularization and Dropout
LU 4.5: Convolutional Neural Networks - Covnets - Computer Vision
LU 4.5.1: Why Covnets?
LU 4.5.2: What is Convolution?
LU 4.5.3: What is Pooling?
LU 4.5.4: What is Zero Padding?
LU 4.5.5: Covnet Architecture
LU 4.6: Dealing with text data
LU 4.6.1: One Hot Encoding
LU 4.6.2: Word Embeddings
LU 4.7: Recurrent Neural Networks
LU 4.7.1: LSTM and GRU - NLP - Natural Language Processing
LU 4.8: Generative Deep Learning - Everybody an Artist!
LU 4.8.1: Variational Autoencoders (VAEs)
LU 4.8.2: Generative Adversarial Networks (GANs)
Who this course is for:
Executives curious about Data Science, AI, Artificial Intelligence, Machine Learning and Deep Learning
Professionals curious about Data Science, AI, Artificial Intelligence, Machine Learning and Deep Learning
Anybody with an interest in AI, Artificial Intelligence, Machine Learning and Deep Learning
IT professionals curious about Data Science, AI, Artificial Intelligence, Machine Learning and Deep Learning
Directors curious about Data Science, AI, Artificial Intelligence, Machine Learning and Deep Learning
Business Leaders curious about Data Science, AI, Artificial Intelligence, Machine Learning and Deep Learning
CEO curious about Data Science, AI, Artificial Intelligence, Machine Learning and Deep Learning
Vice President curious about Data Science, AI, Artificial Intelligence, Machine Learning and Deep Learning
Managers curious about Data Science, AI, Artificial Intelligence, Machine Learning and Deep Learning
Data Analist curious about Data Science, AI, Artificial Intelligence, Machine Learning and Deep Learning
Beginner Python developers curious about Data Science, AI, Artificial Intelligence, Machine Learning and Deep Learning
Beginner Keras developers curious about Data Science, AI, Artificial Intelligence, Machine Learning and Deep Learning
