Machine Learning for Interviews & Research and DL basics
Machine Learning for Interviews & Research and DL basics, available at $54.99, has an average rating of 4.1, with 38 lectures, 2 quizzes, based on 88 reviews, and has 832 subscribers.
You will learn about 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 This course is ideal for individuals who are Machine learning enthusiasts, scholars or anyone seeking to hone the data science skills necessary or Beginner and intermediate developers interested in data science. It is particularly useful for Machine learning enthusiasts, scholars or anyone seeking to hone the data science skills necessary or Beginner and intermediate developers interested in data science.
Enroll now: Machine Learning for Interviews & Research and DL basics
Summary
Title: Machine Learning for Interviews & Research and DL basics
Price: $54.99
Average Rating: 4.1
Number of Lectures: 38
Number of Quizzes: 2
Number of Published Lectures: 38
Number of Published Quizzes: 2
Number of Curriculum Items: 40
Number of Published Curriculum Objects: 40
Original Price: $34.99
Quality Status: approved
Status: Live
What You Will 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 Should Attend
- Machine learning enthusiasts, scholars or anyone seeking to hone the data science skills necessary
- Beginner and intermediate developers interested in data science.
Target Audiences
- Machine learning enthusiasts, scholars or anyone seeking to hone the data science skills necessary
- Beginner and intermediate developers interested in data science.
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
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Covariance
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Eigen Value Decomposition
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Principal Component Analysis
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Central Limit Theorem
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Gaussian Distribution
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Types of Machine Learning
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Parametric Models
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Non-parametric Models
2.) Training Machine Learning Models
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Supervised Machine Learning
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Regression
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Classification
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Linear Regression
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Gradient Descent
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Normal Equations
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Locally Weighted Linear Regression
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Ridge Regression
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Lasso Regression
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Other classifier models in sklearn
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Logistic Regression
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Mapping non-linear functions using linear techniques
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Overfitting and Regularization
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Support Vector Machines
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Decision Trees
3.) Artificial Neural Networks
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Forward Propagation
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Backward Propagation
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Activation functions
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Hyperparameters
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Overfitting
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Dropout
4.) Training Deep Neural Networks
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Deep Neural Networks
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Convolutional Neural Networks
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Recurrent Neural Networks (GRU and LSTM)
5.) Unsupervised Learning
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Clustering (k-Means)
6.) Implementation and Case Studies
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Getting started with Python and Machine Learning
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Case Study – Keras Digit Classifier
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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!
Course Curriculum
Chapter 1: Advanced Statistics and Machine Learning
Lecture 1: Types of Machine Learning
Lecture 2: Parametric Models
Lecture 3: Non-parametric Models
Lecture 4: Central Limit Theorem. Gaussian Distribution. ML framework
Lecture 5: Dimensionality Reducing Principle Component Analysis – Eigen Decomposition
Chapter 2: Training Machine Learning Models
Lecture 1: Supervised Machine Learning
Lecture 2: Regression
Lecture 3: Classification
Lecture 4: Linear Regression
Lecture 5: Gradient Descent
Lecture 6: Tips for Gradient Descent
Lecture 7: Normal Equations
Lecture 8: Non-parametric method – Locally Weighted Linear Regression
Lecture 9: Ridge Regression
Lecture 10: Lasso Regression
Lecture 11: Classification Models in sklearn
Lecture 12: Classification Model – Logistic Regression
Lecture 13: Mapping non-linear functions using linear techniques
Lecture 14: Overfitting and Regularization
Lecture 15: Support Vector Machines
Lecture 16: Decision Trees
Chapter 3: Neural Networks
Lecture 1: Neural Networks Forward Propagation Backward Propagation GD/stochastic/Minibatch
Lecture 2: Tuning Hyperparameters in Neural Network
Chapter 4: Training Deep Neural Networks
Lecture 1: Deep Learning – Requirements
Lecture 2: Common Tricks for building a Deep NN & Improving accuracy performance
Lecture 3: Overfitting – Regularization – Dropout
Lecture 4: Batch Normalization
Lecture 5: Is it possible for deeper networks to be faster than shallow networks? ResNet
Lecture 6: Convolutional Neural Networks
Lecture 7: Maximum Pooling Layers
Lecture 8: Recurrent Neural Networks
Lecture 9: LSTM Units
Lecture 10: GRU Units
Chapter 5: Unsupervised Learning
Lecture 1: Clustering
Chapter 6: Implementation and Case Studies
Lecture 1: Getting started with Python and Machine Learning
Lecture 2: Case Study – Using Keras – Digits Classification
Lecture 3: Case Study – Load Forecasting
Lecture 4: Case Study – Multiple Linear Regression
Instructors
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Learn with Amine
PhD, AWS ML Specialty & Snr Data Scientists Creators Group
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 1 votes
- 3 stars: 11 votes
- 4 stars: 24 votes
- 5 stars: 51 votes
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