Certification in Machine Learning and Deep Learning

Certification in Machine Learning and Deep Learning, available at $44.99, has an average rating of 4.47, with 131 lectures, 2 quizzes, based on 17 reviews, and has 1025 subscribers.

You will learn about You will learn the key factors in Machine and Deep Learning. Overview of Machine Learning. Introduction to Machine Learning. Learn Definition and Importance of the Machine Learning which includes Types of Machine Learning, Basics of Python for Machine Learning Include Data types Learn Control Flow and Functions, NumPy and Pandas for Data Manipulation. Introduction to Data Preprocessing and Visualization. Which include : Data Cleaning and Preprocessing , Handling Missing Values and Feature Scaling Learn After that Data Visualization base on Matplotlib and Seaborn for Visualization and also Exploratory Data Analysis (EDA). You will be able to learn about Supervised Learning including Regression in Linear Regression and Polynomial Regression. Details about Regression is a type of supervised learning including Ridge Regression, Lasso Regression: Elastic Net Regression, Support Vector Regression (SVR) Model Evaluation and Hyperparameter Tuning include Cross-Validation, Grid Search. Unsupervised Learning, including K means clustering, Hierarchical Clustering Part of this Module Learn about Introduction to Deep Learning including Neural Networks Basics, Role of Perceptions and Activation Functions, Feedforward Neural Networks. Introduction to TensorFlow and Keras include : Basics of TensorFlow, Building Neural Networks with Keras. Deep Learning Techniques include Convolutional Neural Networks (CNNs) base on Architecture of CNNs and Image Classification with CNNs Recurrent Neural Networks (RNNs) base on Architecture of RNNs and Sequence Generation with RNNs Transfer Learning and Fine-Tuning base on Pretrained Models and : Fine-Tuning Models Advanced Deep Learning, Generative Adversarial Networks (GANs) , Understanding GANs Image Generation with GANs Reinforcement Learning, include Basics of Reinforcement Learning and Q-Learning and Deep Q-Networks (DQN). Learn about Deployment and Model Management, Model Deployment, Flask for Web APIs, Dock erization, Model Management and Monitoring Bias and Fairness in ML Models, Understanding Bias, Mitigating Bias ,privacy and security in Ml include Data Privacy, Model Security This course is ideal for individuals who are Professionals with Machine Learninng Engineer,Data Scientist,Data Analyst who wants to see themselves well established in the Data Science Domain. or New professionals who are looking to see them successful in Data related work playing with Structural unstructural Data. or Existing AI Architecture , Research Scientist who is looking to get more engagement and innovation from their teams and organizations It is particularly useful for Professionals with Machine Learninng Engineer,Data Scientist,Data Analyst who wants to see themselves well established in the Data Science Domain. or New professionals who are looking to see them successful in Data related work playing with Structural unstructural Data. or Existing AI Architecture , Research Scientist who is looking to get more engagement and innovation from their teams and organizations.

Enroll now: Certification in Machine Learning and Deep Learning

Summary

Title: Certification in Machine Learning and Deep Learning

Price: $44.99

Average Rating: 4.47

Number of Lectures: 131

Number of Quizzes: 2

Number of Published Lectures: 131

Number of Published Quizzes: 2

Number of Curriculum Items: 133

Number of Published Curriculum Objects: 133

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

You will learn the key factors in Machine and Deep Learning. Overview of Machine Learning. Introduction to Machine Learning.
Learn Definition and Importance of the Machine Learning which includes Types of Machine Learning, Basics of Python for Machine Learning Include Data types
Learn Control Flow and Functions, NumPy and Pandas for Data Manipulation.
Introduction to Data Preprocessing and Visualization. Which include : Data Cleaning and Preprocessing , Handling Missing Values and Feature Scaling
Learn After that Data Visualization base on Matplotlib and Seaborn for Visualization and also Exploratory Data Analysis (EDA).
You will be able to learn about Supervised Learning including Regression in Linear Regression and Polynomial Regression.
Details about Regression is a type of supervised learning including Ridge Regression, Lasso Regression: Elastic Net Regression, Support Vector Regression (SVR)
Model Evaluation and Hyperparameter Tuning include Cross-Validation, Grid Search.
Unsupervised Learning, including K means clustering, Hierarchical Clustering Part of this Module
Learn about Introduction to Deep Learning including Neural Networks Basics, Role of Perceptions and Activation Functions, Feedforward Neural Networks.
Introduction to TensorFlow and Keras include : Basics of TensorFlow, Building Neural Networks with Keras.
Deep Learning Techniques include Convolutional Neural Networks (CNNs) base on Architecture of CNNs and Image Classification with CNNs
Recurrent Neural Networks (RNNs) base on Architecture of RNNs and Sequence Generation with RNNs
Transfer Learning and Fine-Tuning base on Pretrained Models and : Fine-Tuning Models
Advanced Deep Learning, Generative Adversarial Networks (GANs) , Understanding GANs Image Generation with GANs
Reinforcement Learning, include Basics of Reinforcement Learning and Q-Learning and Deep Q-Networks (DQN).
Learn about Deployment and Model Management, Model Deployment, Flask for Web APIs, Dock erization, Model Management and Monitoring
Bias and Fairness in ML Models, Understanding Bias, Mitigating Bias ,privacy and security in Ml include Data Privacy, Model Security

Who Should Attend

Professionals with Machine Learninng Engineer,Data Scientist,Data Analyst who wants to see themselves well established in the Data Science Domain.
New professionals who are looking to see them successful in Data related work playing with Structural unstructural Data.
Existing AI Architecture , Research Scientist who is looking to get more engagement and innovation from their teams and organizations

Target Audiences

Professionals with Machine Learninng Engineer,Data Scientist,Data Analyst who wants to see themselves well established in the Data Science Domain.
New professionals who are looking to see them successful in Data related work playing with Structural unstructural Data.
Existing AI Architecture , Research Scientist who is looking to get more engagement and innovation from their teams and organizations

Description

Take the next step in your career!Whether you’re an up-and-coming professional, an experienced executive, Data Scientist Professional. This course is an opportunity to sharpen your Python and ML DL capabilities, increase your efficiency for professional growthand make a positive and lasting impact in the Data Related work.

With this course as your guide, you learn how to:

All the basic functions and skills required Python Machine Learning
Transform DATA related work Make better Statistical Analysis and better Predictive Model on unseen Data.
Get access to recommended templates and formats for the detail’s information related to Machine Learning And Deep Learning.
Learn useful case studies, understanding the Project for a given period of time. Supervised Learning, Unsupervised Learning , ANN,CNN,RNN with useful forms and frameworks
Invest in yourself today and reap the benefits for years to come

The Frameworks of the Course

Engaging video lectures, case studies, assessment, downloadable resources and interactive exercises. This course is created to Learn about Machine Learning and Deep Learning, its importance through various chapters/units. How to maintain the proper regulatory structures and understand the different types of Regression and Classification Task. Also to learn about the Deep Learning Techniques and the Pre Trained Model.

Data Preprocessing will help you to understand data insights and clean data in an organized manner, including responsibilities related to Feature Engineering and Encoding Techniques. Managing model performance and optimization will help you understand how these aspects should be maintained and managed according to the determinants and impacts of algorithm performance. This approach will also help you understand the details related to model evaluation, hyperparameter tuning, cross-validation techniques, and changes in model accuracy and robustness.

The course includes multiple case studies, resources like code examples, templates, worksheets, reading materials, quizzes, self-assessment, video tutorials, and assignments to nurture and upgrade your machine learning knowledge in detail.

In the first part of the course, you’ll learn the details of data preprocessing, encoding techniques, regression, classification, and the distinction between supervised and unsupervised learning.

In the middle part of the course, you’ll learn how to develop knowledge in Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Natural Language Processing (NLP), and Computer Vision.

In the final part of the course, you’ll develop knowledge related to Generative Adversarial Networks (GANs), Transformers, pretrained models, and the ethics of using medical data in projects. You will get full support, and all your queries will be answered within 48 hours, guaranteed.

Course Content:

Part 1

Introduction and Study Plan

· Introduction and know your Instructor

· Study Plan and Structure of the Course

Overview of Machine Learning

1.1.1 Overview of Machine Learning

1.1.2 Types of Machine Learning

1.1.3 continuation of types of machine learning

1.1.4 steps in a typical machine learning workflow

1.1.5 Application of machine learning

1.2.1 Data types and structures.

1.2.2 Control Flow and structures

1.2.3 Libraries for Machine learning

1.2.4 Loading and preparing data.

1.2.5 Model Deployment

1.2.6 Numpy

1.2.7 Indexing and Slicing

1.2.8 Pandas

1.2.9 Indexing and Selection

1.2.10 Handling missing data

Data Cleaning and Preprocessing

2.1.1 Data Cleaning and Preprocessing

2.1.2 Handling Duplicates

2.1.2 Handling Missing Values

2.1.3 Data Processing

2.1.4 Data Splitting

2.1.5 Data Transformation

2.1.6 Iterative Process

2.2.1 Exploratory Data Analysis

2.2.2 Visualization Libraries

2.2.3 Advanced Visualization Techniques

2.2.4 Interactive Visualization

Regression

3.1.1 Regression

3.1.2 Types of Regression

3.1.3 Lasso Regression

3.1.4 Steps in Regression Analysis

3.1.4 Continuation

3.1.5 Best Practices

3.2.1 Classification

3.2.2 Types of Classification

3.2.3 Steps in Classification Analysis

3.2.3 Steps in Classification Analysis Continuation

3.2.4 Best Practices

3.2.5 Classification Analysis

3.3.1 Model Evaluation and Hyperparameter tuning

3.3.2 Evaluation Metrics

3.3.3 Hyperparameter Tuning

3.3.4 Continuations of Hyperparameter tuning

3.3.5 Best Practices

Clustering

4.1.2 Types of Clustering Algorithms

4.1.2 Continuations Types of Clustering Algorithms

4.1.3 Steps in Clustering Analysis

4.1.4 Continuations Steps in Clustering Analysis

4.1.5 Evaluation of Clustering Results

4.1.5 Application of Clustering

4.1.6 Clustering Analysis

4.2.1 Dimensionality Reduction

4.2.1 Continuation of Dimensionality Reduction

4.2.2 Principal component Analysis(PCA)

4.2.3 t Distributed Stochastic Neighbor Embedding

4.2.4 Application of Dimensionality Reduction

4.2.4 Continuation of Application of Dimensionality Reduction

Introduction to Deep Learning

5.1.1 Introduction to Deep Learning

5.1.2 Feedforward Propagation

5.1.3 Backpropagation

5.1.4 Recurrent Neural Networks(RNN)

5.1.5 Training Techniques

5.1.6 Model Evaluation

5.2.1 Introduction to TensorFlow and Keras

5.2.1 Continuation of Introduction to TensorFlow and Keras

5.2.3 Workflow

5.2.4 Keras

5.2.4 Continuation of Keras

5.2.5 Integration

Deep learning Techniques

6.1.1 Deep learning Techniques

6.1.1 Continuation of Deep learning Techniques

6.1.2 key Components

6.1.3 Training

6.1.4 Application

6.1.4 Continuation of Application

6.2.1 Recurrent Neural Networks

6.2.1 Continuation of Recurrent Neural Networks

6.2.2 Training

6.2.3 Variants

6.2.4 Application

6.2.5 RNN

6.3.1 Transfer LEARNING AND FINE TUNING

6.3.1 Transfer LEARNING AND FINE TUNING Continuation

6.3.2 Fine Tuning

6.3.2 Fine Tuning Continuation

6.3.3 Best Practices

6.3.4 Transfer LEARNING and fine tuning are powerful technique

Advance Deep Learning

7.1.1 Advance Deep Learning

7.1.2 Architecture

7.1.3 Training

7.1.4 Training Process

7.1.5 Application

7.1.6 Generative Adversarial Network Have demonstrated

7.2.1 Reinforcement Learning

7.2.2 Reward Signal and Deep Reinforcement Learning

7.2.3 Techniques in Deep Reinforcement Learning

7.2.4 Application of Deep Reinforcement Learning

7.2.5 Deep Reinforcement Learning has demonstrated

Deployment and Model Management

8.1.1 Deployment and Model Management

8.1.2 Flask for Web APIs

8.1.3 Example

8.1.4 Dockerization

8.1.5 Example Dockerfile

8.1.6 Flask and Docker provide a powerful Combination

8.2.1 Model Management and Monitoring

8.2.1 Continuation of Model Management and Monitoring

8.2.2 Model Monitoring

8.2.2 Continuation of Model Monitoring

8.2.3 Tools and Platforms

8.2.4 By implementing effecting model management

Ethical and Responsible AI

9.1.2 Understanding Bias

9.1.3 Promotion Fairness

9.1.4 Module Ethical Considerations

9.1.5 Tools and Resources

9.2.1 Privacy and security in ML

9.2.2 Privacy Considerations

9.2.3 Security Considerations

9.2.3 Continuation of security Consideration

9.2.4 Education and Awareness

Capstone Project

10.1.1 Capstone Project

10.1.2 Project Tasks

10.1.3 Model Evaluation and performance Metrics

10.1.4 Privacy-Preserving Deployment and Monitoring

10.1.5 Learning Outcome

10.1.6 Additional Resources and Practice

Part 3

Assignments

· What is the difference between supervised and unsupervised learning? Note down the answer in your own words.

· What is Padding and staid in CNN?

· Define Transformer in your own words.. What do you mean by Pre trained Model?

Course Curriculum

Chapter 1: Introduction and Overview of Machine Learning

Lecture 1: Introduction

Lecture 2: 1.1.1 Overview of Machine Learning

Lecture 3: 1.1.2 Types of Machine Learning

Lecture 4: 1.1.3 Continuation of types of machine learning

Lecture 5: 1.1.4 Steps in a typical machine learning workflow

Lecture 6: 1.1.5 Application of machine learning

Lecture 7: 1.2.1 Data types and structures.

Lecture 8: 1.2.2 Control Flow and structures

Lecture 9: 1.2.3 Libraries for Machine learning

Lecture 10: 1.2.4 Loading and preparing data

Lecture 11: 1.2.4 Loading and preparing data 2

Lecture 12: 1.2.5 Model Deployment

Lecture 13: 1.2.6 Numpy

Lecture 14: 1.2.7 Indexing and Slicing

Lecture 15: 1.2.8 Pandas

Lecture 16: 1.2.9 Indexing and Selection

Lecture 17: 1.2.10 Handling missing data

Chapter 2: Data Cleaning and Preprocessing

Lecture 1: 2.1.1 Data Cleaning and Preprocessing

Lecture 2: 2.1.2 Handling Duplicates

Lecture 3: 2.1.2 Handling Missing Values

Lecture 4: 2.1.4 Data Splitting

Lecture 5: 2.1.5 Data Transformation

Lecture 6: 2.1.6 Iterative Process

Lecture 7: 2.2.1 Exploratory Data Analysis

Lecture 8: 2.2.2 Visualization Libraries

Lecture 9: 2.2.3 Advanced Visualization Techniques

Lecture 10: 2.2.4 Interactive Visualization

Chapter 3: Regression

Lecture 1: 3.1.1 Regression

Lecture 2: 3.1.2 Types of Regression

Lecture 3: 3.1.3 Lasso Regression

Lecture 4: 3.1.4 Steps in Regression Analysis

Lecture 5: 3.1.4 Continuation

Lecture 6: 3.1.5 Best Practices

Lecture 7: 3.2.1 Regression Analysis

Lecture 8: 3.2.2 Classification

Lecture 9: 3.2.3 Types of Classification

Lecture 10: 3.2.3 Steps in Classification Analysis

Lecture 11: 3.2.3 Steps in Classification Analysis Continuation

Lecture 12: 3.2.4 Best Practices

Lecture 13: 3.2.5 Classification Analysis

Lecture 14: 3.3.1 Model Evaluation and Hyperparameter tuning

Lecture 15: 3.3.2 Evaluation Metrics

Lecture 16: 3.3.3 Hyperparameter Tuning

Lecture 17: 3.3.4 Continuations of Hyperparameter tuning

Lecture 18: 3.3.5 Best Practices

Chapter 4: Clustering

Lecture 1: 4.1.2 Clustering

Lecture 2: 4.1.2 Types of Clustering Algorithms

Lecture 3: 4.1.2 Continuations Types of Clustering Algorithms

Lecture 4: 4.1.3 Steps in Clustering Analysis

Lecture 5: 4.1.4 Continuations Steps in Clustering Analysis

Lecture 6: 4.1.5 Evaluation of Clustering Results

Lecture 7: 4.1.5 Application of Clustering

Lecture 8: 4.1.6 Clustering Analysis

Lecture 9: 4.2.1 Dimensionality Reduction

Lecture 10: 4.2.1 Continuation of Dimensionality Reduction

Lecture 11: 4.2.2 Principal component Analysis(PCA)

Lecture 12: 4.2.3 t Distributed Stochastic Neighbor Embedding

Lecture 13: 4.2.4 Application of Dimensionality Reduction

Lecture 14: 4.2.4 Continuation of Application of Dimensionality Reduction

Chapter 5: Introduction to Deep Learning

Lecture 1: 5.1.1 Introduction to Deep Learning

Lecture 2: 5.1.2 Feedforward Propagation

Lecture 3: 5.1.3 Backpropagation

Lecture 4: 5.1.4 Recurrent Neural Networks(RNN)

Lecture 5: 5.1.5 Training Techniques

Lecture 6: 5.1.6 Model Evaluation

Lecture 7: 5.2.1 Introduction to TensorFlow and Keras

Lecture 8: 5.2.1 Continuation of Introduction to TensorFlow and Keras

Lecture 9: 5.2.3 Workflow

Lecture 10: 5.2.4 Keras

Lecture 11: 5.2.4 Keras 2

Lecture 12: 5.2.5 Integration

Chapter 6: Deep learning Techniques

Lecture 1: 6.1.1 Deep learning Techniques

Lecture 2: 6.1.1 Continuation of Deep learning Techniques

Lecture 3: 6.1.2 key Components

Lecture 4: 6.1.3 Training

Lecture 5: 6.1.4 Application

Lecture 6: 6.1.4 Continuation of Application

Lecture 7: 6.2.1 Recurrent Neural Networks

Lecture 8: 6.2.1 Continuation of Recurrent Neural Networks

Lecture 9: 6.2.2 Training

Lecture 10: 6.2.3 Variants

Lecture 11: 6.2.4 Application

Lecture 12: 6.2.5 RNN

Lecture 13: 6.3.1 Transfer LEARNING AND FINE TUNING

Lecture 14: 6.3.1 Transfer LEARNING AND FINE TUNING 2

Lecture 15: 6.3.2 Fine Tuning

Lecture 16: 6.3.2 Fine Tuning Continuation

Lecture 17: 6.3.3 Best Practices

Lecture 18: 6.3.4 Transfer LEARNING and fine tuning are powerful technique

Chapter 7: Advance Deep Learning

Lecture 1: 7.1.1 Advance Deep Learning

Lecture 2: 7.1.2 Architecture

Lecture 3: 7.1.3 Training

Lecture 4: 7.1.4 Training Process

Instructors

Human and Emotion: CHRMI
E Learning, Consulting, Leadership Development

Rating Distribution

1 stars: 1 votes
2 stars: 0 votes
3 stars: 1 votes
4 stars: 5 votes
5 stars: 10 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

Menu