Top 10 marchine learning and deep learning online courses in Udemy (Part 1)

0

1, Deep Learning Prerequisites: Linear Regression in Python

This couse learn how learn linear regression from scratch and build your own working program in Python for data analysis.

What Will You Learn?
  • Derive and solve a linear regression model, and apply it appropriately to data science problems.
  • Program your own version of a linear regression model in Python
Level: All Level

Price: 120$

Rating: 4.6

Lecture: 32 in 3.5 hour(s)

Visit course: https://www.udemy.com/data-science-linear-regression-in-python/

2. Data Science: Deep Learning in Python

A guide for writing your own neural network in Python and Numpy, and how to do it in Google’s TensorFlow.

What Will You Learn?
  • Code a neural network from scratch in Python and numpy
  • Code a neural network using Google’s TensorFlow
  • Describe the various terms related to neural networks, such as “activation”, “backpropagation” and “feedforward”
  • Describe different types of neural networks and the different types of problems they are used for
  • Derive the backpropagation rule from first principles
  • Create a neural network with an output that has K > 2 classes using softmax
  • Install TensorFlow

Level: Intermediate

Price: 120$

Rating: 4.6

Lecture: 49 in 5 hours

Visit course: https://www.udemy.com/data-science-deep-learning-in-python/

3. Deep Learning Prerequisites: Logistic Regression in Python

Data science techniques for professionals and students – learn the theory behind logistic regression and code in Python

What Will You Learn?
  • Program logistic regression from scratch in Python
  • Describe how logistic regression is useful in data science
  • Derive the error and update rule for logistic regression
  • Understand how logistic regression works as an analogy for the biological neuron
  • Use logistic regression to solve real-world business problems like predicting user actions from e-commerce data and facial expression recognition

Level: All Level

Price: 120$

Rating: 4.6

Lecture: 34 in 3 hour(s)

Visit course: https://www.udemy.com/data-science-logistic-regression-in-python/

4. Deep Learning: Recurrent Neural Networks in Python

Target: GRU, LSTM, + more modern deep learning, machine learning, and data science for sequences.

What Will You Learn?
  • To understand the simple recurrent unit (Elman unit)
  • To understand the GRU (gated recurrent unit)
  • To understand the LSTM (long short-term memory unit)
  • Write various recurrent networks in Theano
  • Understand backpropagation through time
  • Understand how to mitigate the vanishing gradient problem
  • Solve the XOR and parity problems using a recurrent neural network
  • Use recurrent neural networks for language modeling
  • Use RNNs for generating text, like poetry
  • Visualize word embeddings and look for patterns in word vector representations

Level: All Level

Price: 120$

Rating: 4.6

Lecture: 35 in 4 hour(s)

Visit course: https://www.udemy.com/deep-learning-recurrent-neural-networks-in-python/

5. Unsupervised Deep Learning in Python

Purpose: Autoencoders + Restricted Boltzmann Machines for Deep Neural Networks in Theano, + t-SNE and PCA.

What Will You Learn?
  • Understand the theory behind principal components analysis (PCA)
  • Know why PCA is useful for dimensionality reduction, visualization, de-correlation, and denoising
  • Derive the PCA algorithm by hand
  • Write the code for PCA
  • Understand the theory behind t-SNE
  • Use t-SNE in code
  • Understand the limitations of PCA and t-SNE
  • Understand the theory behind autoencoders
  • Write an autoencoder in Theano
  • Understand how stacked autoencoders are used in deep learning
  • Write a stacked denoising autoencoder in Theano
  • Understand the theory behind restricted Boltzmann machines (RBMs)
  • Understand why RBMs are hard to train
  • Understand the contrastive divergence algorithm to train RBMs
  • Write your own RBM and deep belief network (DBN) in Theano
  • Visualize and interpret the features learned by autoencoders and RBMs

Level: All Level

Price: 120$

Rating: 4.6

Lecture: 31 in 3 hour(s).

From Online Courses Review

You might also like More from author

Comments

Loading...