Certification in Machine Learning and Deep Learning

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?