Machine Learning Masterclass with Python, TensorFlow, GCP

Machine Learning Masterclass with Python, TensorFlow, GCP, available at $59.99, has an average rating of 5, with 74 lectures, based on 2 reviews, and has 59 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone who wants to build a solid career in artificial intelligence, machine learning, data science. It is particularly useful for Anyone who wants to build a solid career in artificial intelligence, machine learning, data science.

Enroll now: Machine Learning Masterclass with Python, TensorFlow, GCP

Summary

Title: Machine Learning Masterclass with Python, TensorFlow, GCP

Price: $59.99

Average Rating: 5

Number of Lectures: 74

Number of Published Lectures: 74

Number of Curriculum Items: 74

Number of Published Curriculum Objects: 74

Original Price: ₹7,900

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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

Course Curriculum

Chapter 1: Introduction To AI, Machine Learning And Deep Learning

Lecture 1: Introduction To Artificial Intelligence

Lecture 2: Introduction To Machine Learning

Lecture 3: Introduction To Deep Learning

Lecture 4: Introduction To Artificial Neural Network

Lecture 5: Growing-up With AI

Chapter 2: Application Of Artificial Intelligence In Space Exploration

Lecture 1: AI In Space

Lecture 2: Machine Learning In Space

Chapter 3: Application Of AI In Cyber-security

Lecture 1: Cyber-security And AI

Chapter 4: Environment Setup For Python

Lecture 1: Environment Setup For Machine Learning And Deep Learning

Chapter 5: Understand Your Data

Lecture 1: Basic Statistics – Measuring Central Tendency

Lecture 2: Basic Statistics – Measuring Skewness and Kurtosis

Lecture 3: Basic Statistics And Data Visualization Using Python

Lecture 4: Missing Data Imputation – Part 1

Lecture 5: Missing Data Imputation – Part 2

Chapter 6: Different Types Of Machine Learning

Lecture 1: Supervised And Unsupervised Learning

Lecture 2: Semi-supervised Learning

Lecture 3: Reinforcement Learning

Chapter 7: Supervised Learning

Lecture 1: Linear Regression Part 1 – Introduction

Lecture 2: Linear Regression Part 2 – Fit Linear Regression Model to Data

Lecture 3: Linear Regression Part 3 – Model Complexity and Bias-Variance Trade-off

Lecture 4: Linear Regression Part 4 – Variable Selection

Lecture 5: Linear Regression Part 5 – Statistical Inference

Lecture 6: Linear Regression Part 6 – Multicollinearity

Lecture 7: Linear Regression Part 7 – Measures of Accuracy

Lecture 8: Linear Regression with Python

Lecture 9: Logistic Regression Part 1: Introduction

Lecture 10: Logistic Regression Part 2: Likelihood Estimation

Lecture 11: Logistic Regression Part 3: Statistical Inference

Lecture 12: Logistic Regression Part 4: Measure of Accuracy

Lecture 13: Logistic Regression With Python

Lecture 14: Decision Tree Basics – Part 1

Lecture 15: Decision Tree Basics – Part 2

Lecture 16: Decision Tree – Impurity Gain Ratio

Lecture 17: Decision Tree, Numerical Attributes – Part 1

Lecture 18: Decision Tree, Numerical Attributes – Part 2

Lecture 19: Decision Tree Hands-on Part 1

Lecture 20: Decision Tree Hands-on Part 2

Lecture 21: Decision Tree Hands-on Part 3

Lecture 22: Decision Tree Hands-on Part 4

Lecture 23: Decision Tree Hands-on Part 5

Lecture 24: Regression Trees

Lecture 25: Regression Tree Hands-on

Chapter 8: Unsupervised Learning

Lecture 1: Introduction to Cluster Analysis

Lecture 2: Features of Cluster Analysis

Lecture 3: K-means Clustering

Lecture 4: K-Means Clustering With Python

Lecture 5: Hierarchical Clustering – Part 1

Lecture 6: Hierarchical Clustering Case Studies

Lecture 7: Hierarchical Clustering – Part 2

Lecture 8: Hierarchical Clustering – Part 3

Chapter 9: Case Study

Lecture 1: Predicting diabetes using Decision Tree based predictive model

Chapter 10: Machine Learning On Google Cloud

Lecture 1: Machine Learning on Google Cloud – Part 1

Lecture 2: Machine Learning on Google Cloud – Part 2

Lecture 3: Machine Learning on Google Cloud – Part 3

Lecture 4: Machine Learning on Google Cloud – Part 4

Lecture 5: Machine Learning on Google Cloud – Part 5

Lecture 6: Machine Learning on Google Cloud – Part 6

Lecture 7: Machine Learning on Google Cloud – Part 7

Chapter 11: Worked-out Project – BigQuery

Lecture 1: Building A Machine Learning Model Using BigQuery – Part 1

Lecture 2: Building A Machine Learning Model Using BigQuery – Part 2

Lecture 3: Building A Machine Learning Model Using BigQuery – Part 3

Lecture 4: Building A Machine Learning Model Using BigQuery – Part 4

Lecture 5: Building A Machine Learning Model Using BigQuery – Part 5

Lecture 6: Building A Machine Learning Model Using BigQuery – Part 6

Lecture 7: Building A Machine Learning Model Using BigQuery – Part 7

Lecture 8: Building A Machine Learning Model Using BigQuery – Part 8

Chapter 12: Worked-out Project – GCP And TensorBoard

Lecture 1: Building A Machine Learning Model Using GCP And TensorBoard – Part 1

Lecture 2: Building A Machine Learning Model Using GCP And TensorBoard – Part 2

Lecture 3: Building A Machine Learning Model Using GCP And TensorBoard – Part 3

Lecture 4: Building A Machine Learning Model Using GCP And TensorBoard – Part 4

Lecture 5: Building A Machine Learning Model Using GCP And TensorBoard – Part 5

Lecture 6: Building A Machine Learning Model Using GCP And TensorBoard – Part 6

Lecture 7: Building A Machine Learning Model Using GCP And TensorBoard – Part 7

Lecture 8: Building A Machine Learning Model Using GCP And TensorBoard – Part 8

Instructors

Spotle Learn
The Career Platform – From Learning To Hiring

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5 stars: 2 votes

Frequently Asked Questions

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