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Machine Learning for Interviews & Research and DL basics

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Dabeeruddin Syed

4:39:02

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  • 1. Types of Machine Learning.mp4
    04:55
  • 2. Parametric Models.mp4
    03:42
  • 3. Non-parametric Models.mp4
    03:59
  • 4. Central Limit Theorem. Gaussian Distribution. ML framework.mp4
    10:18
  • 5. Dimensionality Reducing Principle Component Analysis - Eigen Decomposition.mp4
    13:30
  • 6. Quiz on Statistics and PCA.html
  • 1. Supervised Machine Learning.mp4
    09:27
  • 2. Regression.mp4
    03:41
  • 3. Classification.mp4
    04:28
  • 4. Linear Regression.mp4
    05:00
  • 5. Gradient Descent.mp4
    05:35
  • 6. Tips for Gradient Descent.mp4
    06:06
  • 7. Normal Equations.mp4
    04:09
  • 8. Non-parametric method - Locally Weighted Linear Regression.mp4
    04:43
  • 9. Ridge Regression.mp4
    03:01
  • 10. Lasso Regression.mp4
    01:33
  • 11. Classification Models in sklearn.mp4
    03:47
  • 12. Classification Model - Logistic Regression.mp4
    05:29
  • 13. Mapping non-linear functions using linear techniques.mp4
    05:08
  • 14. Overfitting and Regularization.mp4
    03:55
  • 15. Support Vector Machines.mp4
    07:36
  • 16. Decision Trees.mp4
    15:47
  • 17. Quiz - Section 2.html
  • 1. Neural Networks Forward Propagation Backward Propagation GDstochasticMinibatch.mp4
    15:16
  • 2. Tuning Hyperparameters in Neural Network.mp4
    05:17
  • 1. Deep Learning - Requirements.mp4
    05:49
  • 2. Common Tricks for building a Deep NN & Improving accuracy performance.mp4
    07:28
  • 3. Overfitting - Regularization - Dropout.mp4
    05:43
  • 4. Batch Normalization.mp4
    03:25
  • 5. Is it possible for deeper networks to be faster than shallow networks ResNet.mp4
    07:46
  • 6. Convolutional Neural Networks.mp4
    15:20
  • 7. Maximum Pooling Layers.mp4
    05:05
  • 8. Recurrent Neural Networks.mp4
    05:50
  • 9. LSTM Units.mp4
    05:51
  • 10. GRU Units.mp4
    03:44
  • 1. Clustering.mp4
    09:07
  • 1. Getting started with Python and Machine Learning.mp4
    12:22
  • 2. Case Study - Using Keras - Digits Classification.mp4
    10:53
  • 3. Case Study - Load Forecasting.mp4
    13:06
  • 4. Case Study - Multiple Linear Regression.mp4
    21:11
  • Description


    Machine Learning, Linear Regression, PCA, Neural Networks, Hyperparameters, Deep Learning, Keras, Clustering, Case Study

    What You'll Learn?


    • Fundamentals of machine learning and deep learning with respect to big data applications.
    • Machine learning and deep learning concepts required to give data science interviews.
    • Suite of tools for exploratory data analysis and machine learning modeling.
    • Coding-based case studies

    Who is this for?


  • Machine learning enthusiasts, scholars or anyone seeking to hone the data science skills necessary
  • Beginner and intermediate developers interested in data science.
  • More details


    Description

    Interested in Machine Learning, and Deep Learning and preparing for your interviews or research? Then, this course is for you!

    The course is designed to provide the fundamentals of machine learning and deep learning. It is targeted toward newbies, scholars, students preparing for interviews, or anyone seeking to hone the data science skills necessary. In this course, we will cover the basics of machine learning, and deep learning and cover a few case studies.


    This short course provides a broad introduction to machine learning, and deep learning. We will present a suite of tools for exploratory data analysis and machine learning modeling. We will get started with python and machine learning and provide case studies using keras and sklearn.


    ### MACHINE LEARNING ###

    1.) Advanced Statistics and Machine Learning

    • Covariance

    • Eigen Value Decomposition

    • Principal Component Analysis

    • Central Limit Theorem

    • Gaussian Distribution

    • Types of Machine Learning

    • Parametric Models

    • Non-parametric Models


    2.) Training Machine Learning Models

    • Supervised Machine Learning

    • Regression

    • Classification

    • Linear Regression

    • Gradient Descent

    • Normal Equations

    • Locally Weighted Linear Regression

    • Ridge Regression

    • Lasso Regression

    • Other classifier models in sklearn

    • Logistic Regression

    • Mapping non-linear functions using linear techniques

    • Overfitting and Regularization

    • Support Vector Machines

    • Decision Trees

    3.) Artificial Neural Networks

    • Forward Propagation

    • Backward Propagation

    • Activation functions

    • Hyperparameters

    • Overfitting

    • Dropout


    4.) Training Deep Neural Networks

    • Deep Neural Networks

    • Convolutional Neural Networks

    • Recurrent Neural Networks (GRU and LSTM)

    5.) Unsupervised Learning

    • Clustering (k-Means)

    6.) Implementation and Case Studies

    • Getting started with Python and Machine Learning

    • Case Study - Keras Digit Classifier

    • Case Study - Load Forecasting

    So what are you waiting for? Learn Machine Learning, and Deep Learning in a way that will enhance your knowledge and improve your career!

    Thanks for joining the course. I am looking forward to seeing you. let's get started!

    Who this course is for:

    • Machine learning enthusiasts, scholars or anyone seeking to hone the data science skills necessary
    • Beginner and intermediate developers interested in data science.

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    Dabeeruddin Syed
    Dabeeruddin Syed
    Instructor's Courses
    I am a Senior Data Scientist and have a Ph.D. in Machine Learning from Texas A&M University, College Station, Texas. I have 6+ years of experience in machine learning, deep learning, and big data analytics. I have worked in Corporate companies, research institutes, and academia. I have taught courses ranging from learning from data, electrical circuits, and the fundamentals of electrical engineering. On a lighter note, I have a YouTube channel that focuses on my hobbies. I have a high number of publications and a good citation score.
    Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.
    • language english
    • Training sessions 38
    • duration 4:39:02
    • Release Date 2022/12/14