Machine Learning and Deep Learning for Interviews & Research

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

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

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.

Course Content

  • Advanced Statistics and Machine Learning –> 5 lectures • 36min.
  • Training Machine Learning Models –> 16 lectures • 1hr 29min.
  • Neural Networks –> 2 lectures • 21min.
  • Training Deep Neural Networks –> 10 lectures • 1hr 6min.
  • Unsupervised Learning –> 1 lecture • 9min.
  • Implementation and Case Studies –> 3 lectures • 44min.

Machine Learning and Deep Learning for Interviews & Research

Requirements

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!

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