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

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

Bner Python developers curious about Data Science, AI, Artificial Intelligence, Machine Learning and Deep Learning

Bner Keras developers curious about Data Science, AI, Artificial Intelligence, Machine Learning and Deep Learning



DOWNLOAD
uploadgig


rapidgator


nitroflare