05.08 Visualize Results-11

LEVEL 1 - Learn to Code in Python: Fundamentals

LEVEL 2 - Data Science with Python and NumPy

Machine Learning theory

Intro to Tensorflow

Review Sentiment Analysis

Learn to Graph Data with Python and Matplotlib

Complete Beginners Data Analysis with Pandas and Python

Beginners R Programming: Data Science and Machine Learning

R Programming: Practical Data Science and Modeling

LEVEL 3 - Introduction to Machine Learning - Machine Learning Theory for Business - 01 What is Machine Learning

02 What machine learning can and cannot do

01 What Machine Learning Can And Cannot Do (11:27)

03a Machine learning model examples

03b Deep Learning and Neural Networks

04 What are machine learning libraries

01 What Are Machine Learning Libraries (11:59)

Microsoft Certified Azure Data Scientist Associate Preparation - 00a Course Overview

01 (Prerequisite) Introduction to Machine Learning

02 Introduction to Cloud Computing for Machine Learning

03 Introduction to Azure Machine Learning

04 Build a cluster and pipeline in Azure Machine Learning

05 Build a dataset in Microsoft Azure ML Studio

06 Build a regression machine learning model with Azure Machine Learning

Google Cloud Professional Machine Learning Engineer Certification - 00a Course Overview

Course Preview (4:02)

02a Introduction to Cloud Computing for Machine Learning

02b Image classification with AutoML and Vertex AI in Google Cloud

02c image datasetclassification Cloud Storage

02d Train an AutoML image classifier machine learning model

03 Build a streaming data pipeline in Google Cloud with BigQuery

04 Build data streaming Dataflow Pipeline with Google Cloud API

05 Query and visualize data with BigQuery SQL

Machine Learning Fundamentals - 00b Course Overview

01 Code Python on the Web

03 Probability and Statistics for Machine Learning

04 Distributions in Machine Learning

05 Machine Learning Optimization

Data Engineering and Machine Learning Masterclass - 00b Course Overview

03 Load, clean and encode data

04 Data engineering for machine learning

05 Build regression and discretizer models

06 Data transformation and feature selection for ridge regression

Build Machine Learning Models - 00b Image Recognition with MNIST

01 Build Beginner Models in TensorFlow 2.0

02 AI Uninformed Search Algorithms

03 AI Informed Search Algorithms

04 How Machine Learning Works

05 Logistic Regression

06 Gradient Boosted Classification

07 Gradient Boosted Regression

08 Supervised Learning Introduction

LEVEL 4 - Deep Learning Neural Networks - Creative Machine Learning - Draw and Paint with 3 Neural Network Projects - Overview

02 Collect and Process Data

03 Build a Generative Neural Network

03a Generative Neural Network Fundamentals

04 Build a Discriminator Neural Network

05 Evaluate the Model's Performance

06 Train the Model to Draw

07 Test the Model's Drawing Ability

08 Build an Image Style Transfer Project

09 Build an Image Approximation Project

The Deep Learning Masterclass - Convert Sketch to Photo - Overview

02 Data Processing

03a Generative Neural Network Fundamentals

03b Build Neural Networks to Convert a Sketch to a Photograph

04a Discriminator Neural Network Fundamentals

04b Train the Model

05 Test the Model

Machine Learning and Deep Learning for Biology with Python and TensorFlow - 01 Course Overview

04 Regression Fundamentals - Theory Behind the Code

05 Build a K Nearest neighbors regression model to predict diabetes

06 Build Regression Machine Learning Models to Detect Diabetes

07 Data analysis and transformation on blood cell data

08 Cluster blood cells based on fluorescent intensities

09 Preprocess a malignant vs benign cancer mass dataset

10 Build an SVM model to classify malignant vs benign cancer mass

11 Build a logistic regression model to classify malignant vs benign cancer mass

12 Improve model accuracy with tuning methods

13 Prepare heart disease data for machine learning

14 Predict heart disease with machine learning

16 Build a neural network to find malaria in cells

Text to Speech with Python Machine Learning, Deep Learning and Neural Networks - Overview

03 Super simple text to speech with Google Text to Speech

01 Convert Text To Speech With GTTS (5:45)

04 Text to speech with PyTorch, Tacotron 2 and WaveGlow

05 Text to speech with pyttsx3

LEVEL 5 - Computer Vision and Deep Learning with OpenCV and Python - Build 15 Projects

03 Analyze Images with OpenCV

04 Restore Images with Computational Photography

01 Restore A Damaged Image (18:57)

05 Detect objects in images

06 Machine learning and neural networks introduction

07 Read text in an image with OCR and Tesseract

08 Build your first neural network with OpenCV

09 Object detection with OpenCV Deep Learning

10 Outline objects in a video

11 Detect faces in video

12 Track a color in videos

13 Detect lanes for autonomous vehicle computer vision

14 Build a motion alert video monitoring system

15 Detect emotion in a video

Detect Emotion In A Video (12:11)

16 Swap faces with machine learning

LEVEL 6 - ML App Development

CoreML SwiftUI Masterclass - Machine Learning App Development - Overview

00b (Prerequisite) Introduction to Xcode

00c (Prerequisite) Swift Language Basics - 01. Variable and Constants

02. Collection Types

03. Control flow

04. Functions

05. Classes, Struct and Enums

00d (Prerequisite) Introduction to SwiftUI - 01 SwiftUI Overview

02 Build an App with Basic View Objects

03 Build Layout Objects

04 Build Events

01 Sentiment analysis with Natural Language CoreML

02 Tabular Classification with CreateML

04 Classify images with SwiftUI app and MobileNetV2 neural network

05 Object detection with Swift Playgrounds and YOLOv3

Python and Android TensorFlow Lite - Machine Learning for App Development - Overview

01 (Prerequisite) Introduction to Machine Learning

02 Introduction to Python (Prerequisite)

03 (Prerequisite) Introduction to Android Studio

04a (Prerequisite) Introduction to Kotlin

04b Linear regression from scratch

05 Build a linear regression model with Python for TF Lite

06 Build an Android app with TensorFlow machine learning model

07 Load and use model with Kotlin

08 Classification

09 Build a logistic regression model with TensorFlow Keras

10 Build an Android app with TensorFlow machine learning model

11 Load and use model with Kotlin

Beginners Machine Learning Masterclass with Tensorflow JS - 00a Course Overview

01a Build Your First Tensors

01b Visualize Data

01c Train a Simple Model

01d Generate and Visualize Data

02 Build a Linear Regression Model

02b Visualize Linear Regression with User Input

03 Visualize Polynomial Regression with User Input

04 Polynomial Regression

05 K Nearest Neighbors Image Classification with Tensorflow JS

Beginners Guide to Neural Networks in Tensorflow JS - 01 Course Overview

03 TensorFlow JS Fundamentals

04 Build Neural Network Components

05 Build a Simple Neural Network

06 Build a Neural Network with Cross Validation

07 Image Classification with a Neural Network

08 Build a Neural Network for the XOR Algorithm

09 Use Recurrent Neural Networks with Tensorflow JS

10 Detect Objects in Images with a Neural Network

11 Build a Deep Neural Network with Backpropagation

12 Build a Neural Network with Gradient Descent

13 Build a Deep Neural Network with Backpropagation

02 Train The Deep Neural Network With Gradient Descent--1 (15:24)

00a (Prerequisite) Introduction to CSS

00a (Prerequisite) Introduction to HTML

00a (Prerequisite) Introduction to JavaScript

02 Identify Text Toxicity Scores

03 Build a Speech Recognition Drawing Site

04 Manage TensorFlow Memory

05 Build a Housing Linear Regression Project

06 Build a Model on a Large Dataset

07 Build a Logistic Regression Model

08 Visualize Fast Fourier Transform

09 Visualize Principal Component Analysis

Advanced Neural Networks with TensorFlow.js - 03 Build a Neural Network to Detect Lines in Images

04 Build an LSTM Recurrent Neural Network

05 Build a Model to Classify Iris Species

06 Build a Positive vs Negative Text Classifier

07 Build a Neural Network to Recognize Handwriting

LEVEL 7 - Blockchain Machine Learning - Python SQL Ethereum Data Science with Google BigQuery - 00a Course overview

05 Simple BigQuery Python SQL queries

06 Simple BigQuery Ethereum queries

07 Calculate transaction ratios

Build 12 Models, Decentralized Federated Learning and More - 00a Course Overview

00b What is Blockchain

01 What is Machine Learning

03 Regression Machine Learning with Blockchain API

04 Clustering Machine Learning on Cryptocurrencies

05a Build a K Nearest Neighbors Model

05b Build a Radius Neighbors Regression Model

06a Build a CatBoost Model

06b Build an XGBoost Regression Model

07c Build a Recurrent Neural Network with TensorFlow

07a Neural Network Fundamentals

07b Build a Neural Network Classifier

08 Build a Bagging Classifier Model

09 Build a Light Gradient Boosted Regression Ensemble

10 Build a Nested Cross Validation Mode

11 Differential Privacy Project

12 Deep Learning Differential Privacy Project

13 Build a Federated Learning Model

Python Crypto Trading Machine Learning

02 Overview of Regression Models (Prerequisite)

03 Support Vector Machine Machine Learning (Prerequisite)

04 Compare regression techniques for crypto machine learning

05 Decision Tree Machine Learning (Prerequisite)

06 Trees - Compare regression techniques for crypto machine learning

07 Build an AdaBoost machine learning model for stock prediction

08 Overview of Classification Models

09 Compare classification models for crypto machine learning

10 Build a Support Vector Machine to Predict Trading