Hi all, just trying to get a feel for different paths into the field! Any info on courses, self learning, other resources etc. welcome!

if we have b0 = 5000 and b1 = 200

how would you calculate the cost function J(b1, b0)

don't really get this

def gradient_descent(data, starting_b, starting_m, learning_rate, num_iterations):
"""runs gradient descent

    Args:
        data (np.array): training data, containing x,y
        starting_b (float): initial value of b (random)
        starting_m (float): initial value of m (random)
        learning_rate (float): hyperparameter to adjust the step size during descent
        num_iterations (int): hyperparameter, decides the number of iterations for which gradient descent would run

    Returns:
        list : the first and second item are b, m respectively at which the best fit curve is obtained, the third and fourth items are two lists, which store the value of b,m as gradient descent proceeded.
    """
# initial values
    b = starting_b
    m = starting_m

# to store the cost after each iteration
    cost_graph = []

# to store the value of b -> bias unit, m-> slope of line after each iteration (pred = m*x + b)
    b_progress = []
    m_progress = []

# For every iteration, optimize b, m and compute its cost
for i in range(num_iterations):
        cost_graph.append(compute_cost(b, m, data))
        b, m = step_gradient(b, m, array(data), learning_rate)
        b_progress.append(b)
        m_progress.append(m)

return [b, m, cost_graph,b_progress,m_progress]

In order to serve as a strategy against over-fitting, would it be better to impose a minimum number of samples at leaf nodes or is it better to impose a restriction that each leaf node must be pure (as in the instances for each node belong to the same class)?

https://github.com/AbhishekSinhaCoder/Computer-Science-Notes-Only-Source-Code-

https://github.com/AbhishekSinhaCoder/Complete-ML-Coursework

I am slightly confused about a concept but what is the way to find the optimal theta value? Is it through gradient descent?

I'm in chapter 6 of the course and doing the programming assignment (in python). In Exercise 5 part 2, it required me to use the cost function in the utils.trainLinearReg() function which threw me off.

Also, If anyone has up to chapter 6 with the python version, would like to ask an additional question or two as well.

Thanks!

edit: I think i'm confused about this utils.trainLinearReg() function if anyone could explain what that does to me

I'm trying to use this version of the course that translate the octave to python but pretty confused:

https://github.com/mstampfer/Coursera-Stanford-ML-Python

I'm confused on which ones are the assignment vs. copies of the text. For example, in week 1, there are many independent .py files such as gradientDescent.py, normalEqn.py, plotData.py BUT also ex1.py that seems to have all of them conbined. Can someone who can make sense of this explain where to start?

Can anyone please provide me with a URL where I can download the examples referred to in week 2 of the class, and any other necessary example or assigned problems for the course?

Given a set of customer records, you want to identify which customers are similar and whether there are group of customers with similar preferences.

1- What learning technique you will use to build the machine learning model.?

2- What data you will be using at input to the model?

please can anyone explain in detail..

thanks

I'm currently on assignment 4, and here is a line of code for the oneVsAll.m file.

fmincg(@(t)(lrCostFunction(t, X, (y==c),lambda)))

What do @(t) and t mean?

Hi, I enrolled for the sept 2019 batch of professor Andrew's ML course. I am not a programmer but I know some Python and R. The course is using Octave / Online MATLAB and has tutorials on Octave. My question is, should I use Octave (will it be easy, since it has tutorials) or Online MATLAB?

Machine Learning is everywhere and all the successful companies are employing skilled engineers to apply machine learning methods to optimally improve the personalization of their technologies.

According to the piece published last year on Forbes about Machine Learning Engineer is the best Job which indicated that the Machine Learning jobs grew 344% between 2015 to 2018 and have an average salary of $146,085. Similarly, the Computer Vision Engineers earn an average salary of $158,303, the highest salaries in tech.

If you want to learn Machine Learning, then this article about Free Machine Learning Courses will shed some light on how you can intellectually bootstrap your abilities and upgrade your skills to profitability in the rewarding field of Artificial Intelligence.

https://www.reddit.com/user/Vaishnavi999/draft/31ecb23c-4d33-11e9-86b8-0e8bfebc4974

I'm using the data from Week 2's ex1data2.txt, which contains a training set of housing prices in Portland, Oregon. The first column is the size of the house (in square feet), the second column is the number of bedrooms, and the third column is the price of the house.

In python, I computed theta using Normal function:

def normalFn(X, y, theta):
    temp = np.dot(X.T, X)
    temp = np.linalg.inv(temp)
    temp = np.dot(temp, X.T)
    theta = np.dot(temp, y)
    return theta

and plotted hypothesis fn which looks to be along the independent variable points.

I now wanted to use the theta matrix computed to do a prediction. I used independent variables of 3000 and 4 and used the formula h(x) = theta^T.X:

    output = np.dot([1 ,3000 , 4], theta)
    print(output)

However I got a way large prediction of: [[1.02166919e+09]]

What am I missing?