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Machine Learning and Deep Learning for Interviews & Research
Last Update: 6/2022
Duration: 4h 39m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 1.7 GB
Genre: eLearning | Language: English
Last Update: 6/2022
Duration: 4h 39m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 1.7 GB
Genre: eLearning | Language: English
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
Requirements
Basic knowledge of programming is required.
No prior data science experience required. You will learn everything you need to know in the course.
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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