Explore machine learning's essentials for software engineers, delve into supervised learning, neural networks, and deep learning, and gain skills to tackle real-world data challenges effectively.

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Machine learning is the future for the next generation of software professionals.

This course serves as a guide to machine learning for software engineers. You’ll be introduced to three of the most relevant components of the AI/ML discipline; supervised learning, neural networks, and deep learning. You’ll grasp the differences between traditional programming and machine learning by hands-on development in supervised learning before building out complex distributed applications with neural networks. You’ll go even further by layering networks to create deep learning systems. You’ll work with complex real-world datasets to explore machine behavior from scratch at each phase.

By the end of this course, you’ll have a working knowledge of modern machine learning techniques. Using software engineering, you’ll be prepared to build complex neural networks and wrangle real-world data challenges.

Fundamentals of Machine Learning for Software Engineers

By calculating the derivative of loss with respect to bias

By calculating the derivative of loss with respect to weight

By calculating the mean squared error of the loss

How do we measure the gradient descent in our machine learning model?

How do we measure the gradient descent in our machine learning model?

The learning rate measures how large each step of gradient descent is.

If the learning rate is too large, it is possible that each step will overshoot the minimum and end on a peak that is even higher than the starting point in the race to find global minimum.

Increasing the learning rate always results in finding the global minimum using gradient descent.

**( Select all that apply.)** Which statements are true about the relationship between the learning rate and the gradient descent?

( Select all that apply.) Which statements are true about the relationship between the learning rate and the gradient descent?

When GD gets stuck in the small local minima

Which is a valid reason for gradient descent to fail?

Which is a valid reason for gradient descent to fail?

The number of dimensions increases with the increase in input variables

Number of variables is inversely proportional to dimensions

Which is the correct relationship between input variables and dimensions?

Which is the correct relationship between input variables and dimensions?

$M_1=\begin{bmatrix}
1 & 2 &3 \\ 
 4& 5 &6 
\end{bmatrix},M_2=
\begin{bmatrix}
1 & 2 &3 \\ 
 4& 5 &6 
\end{bmatrix}$ 

$
M_1=
\begin{bmatrix}
1 & 2 &3 \\ 
 4& 5 &6 \\
7 & 8 & 9
\end{bmatrix}
, 
M_2=
\begin{bmatrix}
1 & 2 &3  
\end{bmatrix}
$ 

$
M_1=
\begin{bmatrix}
1 & 2 &3 \\ 
 4& 5 &6 \\
7 & 8 & 9
\end{bmatrix}
,  
M_2=
\begin{bmatrix}
1 \\ 2 \\3  
\end{bmatrix}
$ 

**( Select all that apply.)** Select the options where the multiplication  $M_1 \times M_2$ is possible.  

( Select all that apply.) Select the options where the multiplication <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>M</mi><mn>1</mn></msub><mo>×</mo><msub><mi>M</mi><mn>2</mn></msub></mrow><annotation encoding="application/x-tex">M_1 \times M_2</annotation></semantics></math>M1​×M2​ is possible.

How Machine Learning Works

Our First Learning Program

Walking the Gradient

Hyperspace

A Discern Machine

Get Real

The Final Challenge

The Perceptron

Designing the Network

Building the Network

Training the Network

How Classifiers Work

Batchin’ Up

The Zen of Testing

Let’s Do Development

A Deeper Kind of Network

Defeating Overfitting

Taming Deep Networks

Beyond Vanilla Networks

Into the Deep

Quiz: The Gradient Descent