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
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:
DescriptionTake 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 LearningTransform 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 frameworksInvest in yourself today and reap the benefits for years to comeThe Frameworks of the CourseEngaging 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 1Introduction and Study Plan· Introduction and know your Instructor· Study Plan and Structure of the CourseOverview of Machine Learning1.1.1 Overview of Machine Learning1.1.2 Types of Machine Learning1.1.3 continuation of types of machine learning1.1.4 steps in a typical machine learning workflow1.1.5 Application of machine learning1.2.1 Data types and structures.1.2.2 Control Flow and structures1.2.3 Libraries for Machine learning1.2.4 Loading and preparing data.1.2.5 Model Deployment1.2.6 Numpy1.2.7 Indexing and Slicing1.2.8 Pandas1.2.9 Indexing and Selection1.2.10 Handling missing dataData Cleaning and Preprocessing2.1.1 Data Cleaning and Preprocessing2.1.2 Handling Duplicates2.1.2 Handling Missing Values2.1.3 Data Processing2.1.4 Data Splitting2.1.5 Data Transformation2.1.6 Iterative Process2.2.1 Exploratory Data Analysis2.2.2 Visualization Libraries2.2.3 Advanced Visualization Techniques2.2.4 Interactive VisualizationRegression3.1.1 Regression3.1.2 Types of Regression3.1.3 Lasso Regression3.1.4 Steps in Regression Analysis3.1.4 Continuation3.1.5 Best Practices3.2.1 Classification3.2.2 Types of Classification3.2.3 Steps in Classification Analysis3.2.3 Steps in Classification Analysis Continuation3.2.4 Best Practices3.2.5 Classification Analysis3.3.1 Model Evaluation and Hyperparameter tuning3.3.2 Evaluation Metrics3.3.3 Hyperparameter Tuning3.3.4 Continuations of Hyperparameter tuning3.3.5 Best PracticesClustering4.1.2 Types of Clustering Algorithms4.1.2 Continuations Types of Clustering Algorithms4.1.3 Steps in Clustering Analysis4.1.4 Continuations Steps in Clustering Analysis4.1.5 Evaluation of Clustering Results4.1.5 Application of Clustering4.1.6 Clustering Analysis4.2.1 Dimensionality Reduction4.2.1 Continuation of Dimensionality Reduction4.2.2 Principal component Analysis(PCA)4.2.3 t Distributed Stochastic Neighbor Embedding4.2.4 Application of Dimensionality Reduction4.2.4 Continuation of Application of Dimensionality ReductionIntroduction to Deep Learning5.1.1 Introduction to Deep Learning5.1.2 Feedforward Propagation5.1.3 Backpropagation5.1.4 Recurrent Neural Networks(RNN)5.1.5 Training Techniques5.1.6 Model Evaluation5.2.1 Introduction to TensorFlow and Keras5.2.1 Continuation of Introduction to TensorFlow and Keras5.2.3 Workflow5.2.4 Keras5.2.4 Continuation of Keras5.2.5 IntegrationDeep learning Techniques6.1.1 Deep learning Techniques6.1.1 Continuation of Deep learning Techniques6.1.2 key Components6.1.3 Training6.1.4 Application6.1.4 Continuation of Application6.2.1 Recurrent Neural Networks6.2.1 Continuation of Recurrent Neural Networks6.2.2 Training6.2.3 Variants6.2.4 Application6.2.5 RNN6.3.1 Transfer LEARNING AND FINE TUNING6.3.1 Transfer LEARNING AND FINE TUNING Continuation6.3.2 Fine Tuning6.3.2 Fine Tuning Continuation6.3.3 Best Practices6.3.4 Transfer LEARNING and fine tuning are powerful techniqueAdvance Deep Learning7.1.1 Advance Deep Learning7.1.2 Architecture7.1.3 Training7.1.4 Training Process7.1.5 Application7.1.6 Generative Adversarial Network Have demonstrated7.2.1 Reinforcement Learning7.2.2 Reward Signal and Deep Reinforcement Learning7.2.3 Techniques in Deep Reinforcement Learning7.2.4 Application of Deep Reinforcement Learning7.2.5 Deep Reinforcement Learning has demonstratedDeployment and Model Management8.1.1 Deployment and Model Management8.1.2 Flask for Web APIs8.1.3 Example8.1.4 Dockerization8.1.5 Example Dockerfile8.1.6 Flask and Docker provide a powerful Combination8.2.1 Model Management and Monitoring8.2.1 Continuation of Model Management and Monitoring8.2.2 Model Monitoring8.2.2 Continuation of Model Monitoring8.2.3 Tools and Platforms8.2.4 By implementing effecting model managementEthical and Responsible AI9.1.2 Understanding Bias9.1.3 Promotion Fairness9.1.4 Module Ethical Considerations9.1.5 Tools and Resources9.2.1 Privacy and security in ML9.2.2 Privacy Considerations9.2.3 Security Considerations9.2.3 Continuation of security Consideration9.2.4 Education and AwarenessCapstone Project10.1.1 Capstone Project10.1.2 Project Tasks10.1.3 Model Evaluation and performance Metrics10.1.4 Privacy-Preserving Deployment and Monitoring10.1.5 Learning Outcome10.1.6 Additional Resources and PracticePart 3Assignments· 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

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