Machine Learning Masterclass with Python, TensorFlow, GCP

Name: Machine Learning Masterclass with Python, TensorFlow, GCP
Rating: 5.0 (2 reviews)

This course by industry experts and academic leaders is designed for the people who want to build careers in this field

Created bySpotle Learn

Last updated 1/2021

English

What you'll learn

Data Science
Artificial Intelligence
Machine Learning
Deep Learning
Types Of Machine Learning
Supervised Learning
Unsupervised Learning
Artificial Neural Networks
Machine Learning On Google Cloud
Building a Machine Learning Model Using BigQuery
Building a Machine Learning Model Using GCP and Tensorboard
Predicting diabetes using Decision Tree based predictive model

Course content

12 sections • 74 lectures • 10h 4m total length

Introduction To Artificial Intelligence9:03
Hi. Welcome to Spotle Certificate Masterclass. #BeCareerReady. In this video we are going to talk about Artificial Intelligence which is commonly called AI. The aim of this session today is to help you build a fundamental understanding before we go into more details in the next modules.
Introduction To Machine Learning6:13
In this video we are going to learn about a very interesting field of artificial intelligence; machine learning. The aim of this session today is to help you build a fundamental understanding of machine learning, what it means, where they are used etc.
Introduction To Deep Learning6:06
In this video we are going to learn about one of the most recent developments in artificial intelligence, deep learning. The aim of this session today is to help you build a fundamental understanding of deep learning, what it means, its applications etc.
Introduction To Artificial Neural Network6:52
In this video I am going to introduce you to Artificial Neural Network. It’s probably one of the deciding technologies that has the power to shape our future.
Growing-up With AI3:27

AI In Space6:50
Space is fascinating. There is hardly anyone who hasn’t looked at the night sky and imagined traveling through the space. There are hardly few rivalries that benefited mankind. One of them is the cold war between Russia and USA. That opened up the opportunity in space exploration. When one of these countries tried to shine over the other one, mankind shined with knowledge about space. And from there to today’s SPACEX age we have come across a long way. Starting from manually calculating the trajectory for moon landing to modern day usage of artificial intelligence in space exploration we have come a long way and yet there are many things to be done. By the way, did you know, Katherine Johnston, an African-American female physicist was the first person to do the calculations for the first actual moon landing in 1969.
In this video we will learn about the use of artificial intelligence in space exploration.
Machine Learning In Space4:58
Outer Space Data Transmission is a method of sending and receiving data over long distance in space. And machine learning is in it today.
Machine learning algorithms are used to determine optimal ways to transfer data over long distances in space. For example, the MEXAR2 (Mars Express AI Tool) uses historic data to determine the best download schedule and optimizes the transmission of data to prevent the loss of data.
There’s a lot to explore. In this video we will see how machine learning is enhancing several aspects of space exploration.

Linear Regression Part 1 - Introduction12:56
Linear Regression Part 2 - Fit Linear Regression Model to Data9:39
Linear Regression Part 3 - Model Complexity and Bias-Variance Trade-off15:01
Linear Regression Part 4 - Variable Selection9:49
Linear Regression Part 5 - Statistical Inference10:58
Linear Regression Part 6 - Multicollinearity15:36
Linear Regression Part 7 - Measures of Accuracy9:35
Linear Regression with Python13:16
Logistic Regression Part 1: Introduction9:52
Logistic Regression Part 2: Likelihood Estimation12:44
Logistic Regression Part 3: Statistical Inference9:18
Logistic Regression Part 4: Measure of Accuracy11:31
Logistic Regression With Python13:23
Decision Tree Basics - Part 19:02
Decision Tree Basics - Part 29:17
Decision Tree - Impurity Gain Ratio5:56
Decision Tree, Numerical Attributes - Part 14:57
Decision Tree, Numerical Attributes - Part 26:26
Decision Tree Hands-on Part 14:49
Decision Tree Hands-on Part 27:07
Decision Tree Hands-on Part 34:51
Decision Tree Hands-on Part 45:17
Decision Tree Hands-on Part 55:21
Regression Trees6:20
Regression Tree Hands-on5:48

Requirements

You will need to have a computer or a mobile handset with an internet connection
Knowledge of basic statistics will help

Description

Machine Learning, BigQuery, TensorBoard, Google Cloud, TensorFlow, Deep Learning have become key industry drivers in the global job and opportunity market. This course with mix of lectures from industry experts and Ivy League academics will help engineers, MBA students and young managers learn the fundamentals of big data and data science and their applications in business scenarios.

In this course you will learn

1. Data Science

2. Machine Learning

3. BigQuery

4. TensorBoard

5. Google Cloud Machine Learning

6. AI, Machine Learning, Deep Learning Fundamentals

7. Analyzing Data

8. Supervised and Unsupervised Learning

9. Building a Machine Learning Model Using BigQuery

10. Building a Machine Learning Model Using GCP and Tensorboard

11. Building your own model for predicting diabetes using Decision Tree

Who this course is for:

Anyone who wants to build a solid career in artificial intelligence, machine learning, data science.

Machine Learning Masterclass with Python, TensorFlow, GCP

What you'll learn

Explore related topics

Course content

Introduction To AI, Machine Learning And Deep Learning5 lectures • 32min

Application Of Artificial Intelligence In Space Exploration2 lectures • 12min

Application Of AI In Cyber-security1 lecture • 7min

Environment Setup For Python1 lecture • 14min

Understand Your Data5 lectures • 1hr 8min

Different Types Of Machine Learning3 lectures • 24min

Supervised Learning25 lectures • 3hr 49min

Unsupervised Learning8 lectures • 54min

Case Study1 lecture • 6min

Machine Learning On Google Cloud7 lectures • 41min

Requirements

Description

Who this course is for: