Teach on Udemy

Turn what you know into an opportunity and reach millions around the world.

Learn More

Your cart is empty.

Keep shopping

Certification in Machine Learning and Deep Learning

Name: Certification in Machine Learning and Deep Learning
Rating: 4.3 (29 reviews)

Learn Data Cleaning and Preprocessing, Regression, Clustering, DL Techniques, Deployment & Model Management, Ethical AI

Created byHuman and Emotion: CHRMI

Last updated 6/2024

English

What you'll 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

Coding Exercises

This course includes our updated coding exercises so you can practice your skills as you learn.

Course content

11 sections • 131 lectures • 12h 8m total length

Introduction7:51
1.1.1 Overview of Machine Learning2:28
1.1.2 Types of Machine Learning4:06
1.1.3 Continuation of types of machine learning4:30
1.1.4 Steps in a typical machine learning workflow3:49
1.1.5 Application of machine learning3:41
1.2.1 Data types and structures.2:04
1.2.2 Control Flow and structures1:59
1.2.3 Libraries for Machine learning3:51
1.2.4 Loading and preparing data3:56
1.2.4 Loading and preparing data 22:21
Learn to load and prepare data using Python and pandas, reading CSV data and other formats, with basic pre-processing. Use scikit-learn for model building, evaluation, train_test_split, and linear regression.
1.2.5 Model Deployment4:38
1.2.6 Numpy4:29
1.2.7 Indexing and Slicing7:04
1.2.8 Pandas4:47
Learn pandas, a numpy-based tool for handling structured, tabular data. Create data frames from dictionaries or lists, perform manipulation, view data with head and tail, and manage missing values.
1.2.9 Indexing and Selection3:55
Master indexing and selection in pandas, including slicing, boolean indexing, and iloc/loc for rows and columns. Manipulate data frames by adding or removing columns, merging, joining, and applying column operations.
1.2.10 Handling missing data5:20

2.1.1 Data Cleaning and Preprocessing4:35
2.1.2 Handling Duplicates3:45
2.1.2 Handling Missing Values3:30
2.1.4 Data Splitting5:04
Split data into training, validation, and test sets to train models, tune hyperparameters, and evaluate unseen performance, while addressing imbalance with resampling and synthetic data, and applying PCA or t-SNE.
2.1.5 Data Transformation5:32
2.1.6 Iterative Process4:05
2.2.1 Exploratory Data Analysis3:39
2.2.2 Visualization Libraries5:13
2.2.3 Advanced Visualization Techniques15:00
2.2.4 Interactive Visualization6:39
Explore interactive visualizations with Dash, Boki, and Altar to build Python web apps and dashboards that render interactive plots from pandas data frames using Vega-Lite.

3.1.1 Regression3:11
3.1.2 Types of Regression7:24
3.1.3 Lasso Regression8:30
3.1.4 Steps in Regression Analysis13:43
3.1.4 Continuation3:29
Deploy trained regression models to production or integrate into applications for predictions on new unseen data in real time, with preprocessing, scalability, monitoring, versioning, and periodic retraining from production feedback.
3.1.5 Best Practices7:33
3.2.1 Regression Analysis2:42
3.2.2 Classification4:19
Master classification by assigning input data to predefined classes using labeled data and features, with algorithms like decision trees and SVM, evaluated by accuracy and F1.
3.2.3 Types of Classification6:16
3.2.3 Steps in Classification Analysis4:48
3.2.3 Steps in Classification Analysis Continuation10:09
3.2.4 Best Practices6:46
3.2.5 Classification Analysis3:07
3.3.1 Model Evaluation and Hyperparameter tuning5:02
3.3.2 Evaluation Metrics4:48
3.3.3 Hyperparameter Tuning4:26
3.3.4 Continuations of Hyperparameter tuning7:47
3.3.5 Best Practices6:19

4.1.2 Clustering4:12
Explore unsupervised learning through clustering, a method that groups data points by similarity without labeled data, using distance metrics and discovering patterns for market and image segmentation and anomaly detection.
4.1.2 Types of Clustering Algorithms6:10
Explore types of clustering algorithms, including k-means, hierarchical, and DBSCAN, and learn how each partitions data, uses centroids or dendrograms, and handles noise.
4.1.2 Continuations Types of Clustering Algorithms3:37
4.1.3 Steps in Clustering Analysis5:32
4.1.4 Continuations Steps in Clustering Analysis5:01
4.1.5 Evaluation of Clustering Results8:10
4.1.5 Application of Clustering7:29
4.1.6 Clustering Analysis2:53
4.2.1 Dimensionality Reduction9:32
4.2.1 Continuation of Dimensionality Reduction3:25
4.2.2 Principal component Analysis(PCA)6:55
Learn principal component analysis, a linear dimensionality reduction method that standardizes data, computes the covariance matrix, and projects data onto top principal components to preserve variance.
4.2.3 t Distributed Stochastic Neighbor Embedding2:48
4.2.4 Application of Dimensionality Reduction3:35
4.2.4 Continuation of Application of Dimensionality Reduction6:28
Dimensionality reduction reduces noise and focuses on informative features to boost signal to noise ratio, improve model robustness, and enable efficient analysis, visualization, and modeling of high dimensional data.

5.1.1 Introduction to Deep Learning7:31
5.1.2 Feedforward Propagation2:57
5.1.3 Backpropagation7:02
5.1.4 Recurrent Neural Networks(RNN)7:21
5.1.5 Training Techniques4:43
5.1.6 Model Evaluation8:25
5.2.1 Introduction to TensorFlow and Keras7:58
Explore TensorFlow and Keras, open source frameworks for building and training neural networks. Learn their ecosystem, data flow graphs, automatic differentiation, and high level APIs for scalable deployment.
5.2.1 Continuation of Introduction to TensorFlow and Keras10:41
5.2.3 Workflow6:31
Define the computational graph to represent the model architecture and data flow. Compile, train, evaluate, and deploy the model using loss functions, optimizers, evaluation metrics, and production deployment.
5.2.4 Keras5:09
5.2.4 Keras 22:04
5.2.5 Integration7:21
TensorFlow and Keras integrate seamlessly, enabling high-level API access, distributed training, custom operations, and production deployment via TensorFlow serving.

6.1.1 Deep learning Techniques3:02
6.1.1 Continuation of Deep learning Techniques7:20
6.1.2 key Components5:20
6.1.3 Training8:27
6.1.4 Application3:43
6.1.4 Continuation of Application5:17
Leverage pre-trained convolutional neural networks such as VGG, ResNet, and MobileNet to enable transfer learning on smaller datasets and fine-tune higher-level features for image classification, object detection, and semantic segmentation.
6.2.1 Recurrent Neural Networks5:32
6.2.1 Continuation of Recurrent Neural Networks3:21
Understand how recurrent neural networks update the hidden state at each step by combining the current input with the previous state to capture memory, and when to output.
6.2.2 Training3:25
6.2.3 Variants4:10
6.2.4 Application4:54
6.2.5 RNN4:39
6.3.1 Transfer LEARNING AND FINE TUNING4:44
6.3.1 Transfer LEARNING AND FINE TUNING 27:24
6.3.2 Fine Tuning5:02
6.3.2 Fine Tuning Continuation3:42
6.3.3 Best Practices4:42
6.3.4 Transfer LEARNING and fine tuning are powerful technique3:39

7.1.1 Advance Deep Learning4:46
7.1.2 Architecture6:33
7.1.3 Training3:36
7.1.4 Training Process3:30
Training process randomly initializes generator and discriminator, trains them in mini-batches, updates via real and adversarial losses, and mitigates model collapse and vanishing gradients with mini-batch discrimination and feature matching.
7.1.5 Application6:24
7.1.6 Generative Adversarial Network Have demonstrated2:41
7.2.1 Reinforcement Learning4:48
7.2.2 Reward Signal and Deep Reinforcement Learning3:41
7.2.3 Techniques in Deep Reinforcement Learning4:40
7.2.4 Application of Deep Reinforcement Learning5:42
7.2.5 Deep Reinforcement Learning has demonstrated3:49
Deep reinforcement learning demonstrates remarkable capabilities in learning complex sequential decisions from raw sensor input by combining deep neural networks with reinforcement learning to learn human-level policies.

8.1.1 Deployment and Model Management4:00
8.1.2 Flask for Web APIs5:16
8.1.3 Example7:55
Build a Flask web api that serves a machine learning model, handling post requests with json input and returning predictions, and explore deployment with Nginx, Gunicorn, and Heroku.
8.1.4 Dockerization7:55
8.1.5 Example Dockerfile10:26
8.1.6 Flask and Docker provide a powerful Combination4:06
Learn how Flask and Docker deploy machine learning models as scalable, reliable web APIs in production, using containerization to ensure consistent RESTful endpoints across environments.
8.2.1 Model Management and Monitoring14:44
8.2.1 Continuation of Model Management and Monitoring3:54
Package train models into deployable units with docker containers, including metadata, dependencies, and documentation to enable automated deployment pipelines with testing, monitoring, and rollback and roll-forward strategies.
8.2.2 Model Monitoring8:06
Define metrics like accuracy, precision, recall, and F1; set thresholds and monitor data distribution for drift. Compare predictions to ground truth to detect model drift and trigger updates.
8.2.2 Continuation of Model Monitoring5:55
8.2.3 Tools and Platforms4:39
8.2.4 By implementing effecting model management4:15
Implement effective model management and monitoring to ensure robustness, reliability, and performance of deployed ML models in production. Continuous monitoring and predictive maintenance address issues early and sustain model quality.

9.1.1 Ethical and Responsible AI4:10
9.1.2 Understanding Bias9:55
Promote fairness in machine learning by measuring bias across race, gender, and age, applying disparate impact metrics, and using data preprocessing and algorithmic fairness to ensure equitable predictions.
9.1.3 Promotion Fairness6:49
Promote fairness in machine learning by defining group, individual, and intersection fairness; evaluate performance across demographics with disparate impact, equal opportunity, and demographic parity metrics; apply bias mitigation.
9.1.4 Module Ethical Considerations6:58
9.1.5 Tools and Resources6:18
Explore fairness tools such as AI fairness 360 and fair learn to evaluate and mitigate bias in ML models using metrics and visualizations, with diverse data and teams.
9.2.1 Privacy and security in ML5:38
9.2.2 Privacy Considerations7:03
9.2.3 Security Considerations9:59
Secure model training with access control, encryption, and secure communication; conduct regular audits. Boost robustness via adversarial training, input sanitization, and robust optimization within a governance framework.
9.2.3 Continuation of security Consideration7:20
Secure deployment of ML models via containerization, access control, network segmentation, and encryption; audit data access and enforce privacy regulations like GDPR and CCPA, while upholding fairness and accountability in AI.
9.2.4 Education and Awareness7:25

10.1.1 Capstone Project8:30
Design a capstone project that applies privacy-preserving machine learning to healthcare data, using deep learning techniques to extract insights while protecting patients' privacy and ensuring data security.
10.1.2 Project Tasks4:11
10.1.3 Model Evaluation and performance Metrics7:20
10.1.4 Privacy-Preserving Deployment and Monitoring7:41
Explore privacy-preserving deployment and real-time monitoring of healthcare model inference, ensuring data security, encryption, access control, and compliance with HIPAA and GDPR.
10.1.5 Learning Outcome5:49
10.1.6 Additional Resources and Practice4:00
Explore privacy preserving machine learning and deep learning through recommended resources and coding exercises. Engage with differential privacy concepts and real-world applications in healthcare, finance, e-commerce, and autonomous system.

Requirements

You should have an interest in Machine learning and Deep learning.
An interest in learning about overview of machine learning , supervised learning, unsupervised learning and re-enforcement learning.
Be interested in getting the knowledge of Data Preprocessing and Visualization, Introduction to Deep Learning, Deep Learning Techniques, Advanced Deep Learning.
Have an interest in understanding the Deployment and Model Management, Ethical and Responsible AI, Capstone Project

Description

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 growth and 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?

Who this course is for:

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

Certification in Machine Learning and Deep Learning

What you'll learn

Explore related topics

Coding Exercises

Course content

Introduction and Overview of Machine Learning17 lectures • 1hr 11min

Data Cleaning and Preprocessing10 lectures • 57min

Regression18 lectures • 1hr 50min

Clustering14 lectures • 1hr 16min

Introduction to Deep Learning12 lectures • 1hr 18min

Deep learning Techniques18 lectures • 1hr 28min

Advance Deep Learning11 lectures • 50min

Deployment and Model Management12 lectures • 1hr 21min

Ethical and Responsible AI10 lectures • 1hr 12min

Capstone Project6 lectures • 38min

Requirements

Description

Who this course is for: