Machine Learning and Deep Learning

Machine Learning and Deep Learning, available at $59.99, has an average rating of 4.2, with 113 lectures, 2 quizzes, based on 214 reviews, and has 16704 subscribers.

You will learn about You will learn the core concepts in Machine learning and Deep Learning How to code and access data stored in a cloud environment You will learn the core algorithms in ML: Linear Regression, Logistic Regression, Decision Tree, Random Forest You will also learn about unsupervised learning What is Explainer AI and why its important You will master deep learning concepts and algorithms What is a tensor and how it is helpful in deep learning What are the linear algebra concepts relevant to Machine Learning and Deep Learning How to go about a ML project Python programming (for those who dont know python) What is AutoML and how to use Vertex AI to deploy Machine learning algorithms Unsupervised deep learning algorithms This course is ideal for individuals who are Professionals wanting to shift to ML roles or Students or ML professionals who are looking for a refresher It is particularly useful for Professionals wanting to shift to ML roles or Students or ML professionals who are looking for a refresher.

Enroll now: Machine Learning and Deep Learning

Summary

Title: Machine Learning and Deep Learning

Price: $59.99

Average Rating: 4.2

Number of Lectures: 113

Number of Quizzes: 2

Number of Published Lectures: 77

Number of Published Quizzes: 1

Number of Curriculum Items: 124

Number of Published Curriculum Objects: 87

Number of Practice Tests: 1

Original Price: $34.99

Quality Status: approved

Status: Live

What You Will Learn

You will learn the core concepts in Machine learning and Deep Learning

How to code and access data stored in a cloud environment

You will learn the core algorithms in ML: Linear Regression, Logistic Regression, Decision Tree, Random Forest

You will also learn about unsupervised learning

What is Explainer AI and why its important

You will master deep learning concepts and algorithms

What is a tensor and how it is helpful in deep learning

What are the linear algebra concepts relevant to Machine Learning and Deep Learning

How to go about a ML project

Python programming (for those who dont know python)

What is AutoML and how to use Vertex AI to deploy Machine learning algorithms

Unsupervised deep learning algorithms

Who Should Attend

Professionals wanting to shift to ML roles

Students

ML professionals who are looking for a refresher

Target Audiences

Professionals wanting to shift to ML roles

Students

ML professionals who are looking for a refresher

Course Description

Welcome to our comprehensive course on Machine Learning and Deep Learning. This course is designed to provide you with a robust foundation in both fields, starting from the basics and advancing to more complex topics. Whether you are a beginner or looking to deepen your knowledge, this course will equip you with the essential skills and understanding needed to excel in these rapidly evolving areas.

This course covers the conceptsof machine learning and deep learning as well as the applicationof these concepts using case studies and examples, along with a walk through of the python codes.

A) Machine Learning

Simple and multiple linear regression

Logistic regression

Decision tree, Random forest and XG boost

Unsupervised algorithms

Principal Component Analysis (PCA)

Exploratory data analysis (EDA)

B) Linear Algebra in Machine Learning

C) Deep Learning

Tensors

Activation function

Convex Optimization

Neural Networks

Unsupervised Deep learning algorithms like GAN (Generative Adversarial Networks_

D) Explainable AI

E) AutoML using Google Vertex

F) Machine Learning Interview Prep

Python programming is also covered for the benefit of those who are new to python and those who want to refresh some of the topics in python.

This course is taught by an industry veteran, who brings his vast experiences and practical perspectives into the program.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Day 1: What gets measured gets improved

Lecture 1: What Gets Measured Gets Improved

Lecture 2: Test your understanding 1

Chapter 3: Day 2: Python Refresher | Python for Linear Algebra

Lecture 1: Python Refresher

Chapter 4: Day 3: First ML Algorithm

Lecture 1: Simple Linear Regression

Chapter 5: Day 4: Test Vs Train in Machine Learning

Lecture 1: Test Vs Train

Lecture 2: Linear Algebra in Machine Learning

Chapter 6: Day 5: Multiple Linear Regression

Lecture 1: Multiple Linear Regression

Chapter 7: Day 6: Logistic Regression, Gradient Descent

Lecture 1: Logistic Regression

Lecture 2: Math behind Gradient Descent

Chapter 8: Day 7: Are independent variables truly independent?

Lecture 1: Are independent variables truly independent?

Chapter 9: Day 8: Decision Tree, Random Forest & XG Boost

Lecture 1: Decision Tree, Random Forest, XG Boost

Chapter 10: Day 9: Principal Component Analysis

Lecture 1: Principal Component Analysis

Chapter 11: Day 10: Unsupervised Machine Learning

Lecture 1: Unsupervised Learning | Clustering

Chapter 12: Day 11: Tensor Intro

Lecture 1: Tensor Intro

Lecture 2: Tensor Computations

Chapter 13: Day 12: Understanding Deep Learning

Lecture 1: Understanding Deep Learning in Simple Terms

Lecture 2: Activation Function

Lecture 3: Convex Optimization

Lecture 4: ANN

Chapter 14: Day 13: Convolution

Lecture 1: Convolution in CNN

Lecture 2: Deploying a CNN Model

Chapter 15: Day 14: RNN

Lecture 1: Why RNN

Lecture 2: Math behind RNN

Lecture 3: LSTM

Lecture 4: Spam Detection – RNN & LSTM

Lecture 5: ANN Vs CNN Vs RNN

Chapter 16: Day 15: Unsupervised Deep Learning

Lecture 1: Generative Adversarial Network

Lecture 2: Restricted Boltzmann Machines and Deep Belief Networks

Lecture 3: Auto Encoder

Lecture 4: Building The Models

Chapter 17: Day 16: Explainer AI

Lecture 1: Churn prediction model

Lecture 2: Anomaly Detection – Insurance Fraud

Chapter 18: Day 17: Auto ML Using Vertex Part 1

Lecture 1: What is AutoML

Lecture 2: Introduction to Google Cloud Vertex AI

Lecture 3: Multiple Linear Regression in Vertex

Chapter 19: Day 18: AutoML Using Vertex Part 2

Lecture 1: Classification in Vertex

Lecture 2: NLP in Vertex

Chapter 20: Day 19: Machine Learning Interview Prep

Lecture 1: Getting Started – The Mind Game

Lecture 2: Clarity of thinking and Initiative

Lecture 3: A linear algebra question

Lecture 4: What if you do not know the answer

Lecture 5: An important skill

Chapter 21: Day 20: ML Interview Prep – Descriptive Technical Questions

Lecture 1: Descriptive technical questions – part 1

Lecture 2: Descriptive technical questions – part 2

Lecture 3: Descriptive technical questions – part 3

Lecture 4: Descriptive technical questions – part 4

Lecture 5: Descriptive technical questions – part 5

Lecture 6: Descriptive technical questions – part 6

Chapter 22: Day 21: ML Interview Prep – Non Technical Questions

Lecture 1: Tell me about yourself

Lecture 2: Do you have any questions

Lecture 3: Why are you looking for a change

Lecture 4: Why should I hire you

Lecture 5: Strengths and Weaknesses

Lecture 6: Tips to handle difficult situations

Chapter 23: Python Programming using google colab

Lecture 1: Introduction to Colab: Google Cloud Development Environment

Lecture 2: Getting Started with Python

Lecture 3: Variables

Lecture 4: Operators

Lecture 5: Conditions

Lecture 6: Loops

Lecture 7: Functions

Lecture 8: Arrays

Lecture 9: List

Lecture 10: Tuple

Lecture 11: Set

Lecture 12: Dictionary

Lecture 13: Getting Started with NumPy

Instructors

SeaportAi .
Artificial Intelligence and Business Transformation Experts

Rating Distribution

1 stars: 3 votes

2 stars: 2 votes

3 stars: 29 votes

4 stars: 73 votes

5 stars: 107 votes

Frequently Asked Questions

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