Overview
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
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
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
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?
Human and Emotion: CHRMI
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