**Physical Address**

304 North Cardinal St.

Dorchester Center, MA 02124

Microsoft Learn provides several interactive ways to get an introduction to classic machine learning. These learning paths will get you productive on their own, and also are an excellent base for moving on to deep learning topics.

From the most basic classical machine learning models, to exploratory data analysis and customizing architectures, you’ll be guided by easy to digest conceptual content and interactive Jupyter notebooks, all without leaving your browser.

Choose your own path depending on your educational background and interests.

**Option 1: The complete course: Foundations of data science for machine learning:**

This path is recommended for most people. It has all the same modules as the other two learning paths with a custom flow that maximizes reinforcement of concepts. If you want to learn about both the underlying concepts and how to get into building models with the most common machine learning tools this path is for you. It’s also the best path if you plan to move beyond classic machine learning, and get an education in deep learning and neural networks, which we only introduce here.

**Option 2: The Understand data science for machine learning learning path:**

If you are looking to understand how machine learning works and don’t have much mathematical background then this path is for you. It makes no assumptions about previous education (other than a light familiarity with coding concepts) and teaches with code, metaphor, and visual that give you the ah ha moment. It’s hands-on, but focuses more on understanding fundamentals and less on the power of the tools and libraries available.

**✔ Option 3: The Create machine learning models learning path:**

If you already have some idea what machine learning is about or you have a strong mathematical background you may best enjoy jumping right in to the *Create Machine Learning Models* learning path. These modules teach some machine learning concepts, but move fast so they can get to the power of using tools like scikit-learn, TensorFlow, and PyTorch. This learning path is also the best one for you if you’re looking for just enough familiarity to understand machine learning examples for products like Azure ML or Azure Databricks.

✔ You are currently on this path, scroll down to begin.

**Prerequisites:**

This learning path assumes knowledge of basic mathematical concepts. Some experience with Python is also beneficial.

Data exploration and analysis is at the core of data science. Data scientists require skills in programming languages like Python to explore, visualize, and manipulate data.

**Learning objectives:**

In this module, you will learn:

- Common data exploration and analysis tasks.
- How to use Python packages like NumPy, Pandas, and Matplotlib to analyze data.

**Prerequisites:**

- Knowledge of basic mathematics
- Some experience programming in Python

**This module is part of these learning paths:**

Q1. You have a NumPy array with the shape (2,20). What does this tell you about the elements in the array?

**The array is two dimensional, consisting of two arrays each with 20 elements**- The array contains 2 elements, with the values 2 and 20
- The array contains 20 elements, all with the value 2

Q2. You have a Pandas DataFrame named df_sales containing daily sales data. The DataFrame contains the following columns: year, month, day_of_month, sales_total. You want to find the average sales_total value. Which code should you use?

- df_sales[‘sales_total’].avg()
**df_sales[‘sales_total’].mean()**- mean(df_sales[‘sales_total’])

Q3. You have a DataFrame containing data about daily ice cream sales. You use the corr method to compare the avg_temp and units_sold columns, and get a result of 0.97. What does this result indicate?

- On the day with the maximum units_sold value, the avg_temp value was 0.97
**Days with high avg_temp values tend to coincide with days that have high units_sold values**- The units_sold value is, on average, 97% of the avg_temp value

Regression is a commonly used kind of machine learning for predicting numeric values.

**Learning objectives:**

In this module, you’ll learn:

- When to use regression models.
- How to train and evaluate regression models using the Scikit-Learn framework.

**Prerequisites:**

- Basic mathematical concepts
- Programming with Python

**This module is part of these learning paths:**

Q1. You are using scikit-learn to train a regression model from a dataset of sales data. You want to be able to evaluate the model to ensure it will predict accurately with new data. What should you do?

- Use all of the data to train the model. Then use all of the data to evaluate it
- Train the model using only the feature columns, and then evaluate it using only the label column
**Split the data randomly into two subsets. Use one subset to train the model, and the other to evaluate it**

Q2. You have created a model object using the scikit-learn LinearRegression class. What should you do to train the model?

- Call the predict() method of the model object, specifying the training feature and label arrays
**Call the fit() method of the model object, specifying the training feature and label arrays**- Call the score() method of the model object, specifying the training feature and test feature arrays

Q3. You train a regression model using scikit-learn. When you evaluate it with test data, you determine that the model achieves an R-squared metric of 0.95. What does this metric tell you about the model?

**The model explains most of the variance between predicted and actual values.**- The model is 95% accurate
- On average, predictions are 0.95 higher than actual values

Classification is a kind of machine learning used to categorize items into classes.

**Learning objectives:**

In this module, you’ll learn:

- When to use classification
- How to train and evaluate a classification model using the Scikit-Learn framework

**Prerequisites:**

- Basic mathematical concepts
- Programming with Python

**This module is part of these learning paths:**

Q1. You plan to use scikit-learn to train a model that predicts credit default risk. The model must predict a value of 0 for loan applications that should be automatically approved, and 1 for applications where there is a risk of default that requires human consideration. What kind of model is required?

**A binary classification model**- A multi-class classification model
- A linear regression model

Q2. You have trained a classification model using the scikit-learn LogisticRegression class. You want to use the model to return labels for new data in the array x_new. Which code should you use?

**model.predict(x_new)**- model.fit(x_new)
- model.score(x_new, y_new)

Q3. You train a binary classification model using scikit-learn. When you evaluate it with test data, you determine that the model achieves an overall Recall metric of 0.81. What does this metric indicate?

- The model correctly predicted 81% of the test cases
- 81% of the cases predicted as positive by the model were actually positive
**The model correctly identified 81% of positive cases as positive**

Clustering is a kind of machine learning that is used to group similar items into clusters.

**Learning objectives:**

In this module, you’ll learn:

- When to use clustering
- How to train and evaluate a clustering model using the scikit-learn framework

**Prerequisites:**

- Basic mathematical concepts
- Programming with Python

**This module is part of these learning paths:**

Q1. K-Means clustering is an example of which kind of machine learning?

- Supervised machine learning
**Unsupervised machine learning**- Reinforcement learning

Q2. You are using scikit-learn to train a K-Means clustering model that groups observations into three clusters. How should you create the KMeans object to accomplish this goal?

**model = KMeans(n_clusters=3)**- model = KMeans(n_init=3)
- model = KMeans(max_iter=3)

Deep learning is an advanced form of machine learning that emulates the way the human brain learns through networks of connected neurons.

**Learning objectives:**

In this module, you will learn:

- Basic principles of deep learning
- How to train a deep neural network (DNN) using PyTorch or Tensorflow
- How to train a convolutional neural network (CNN) using PyTorch or Tensorflow
- How to use transfer learning to train a convolutional neural network (CNN) with PyTorch or Tensorflow

**Prerequisites:**

- Basic mathematical concepts
- Understanding of classical machine learning techniques.
- Programming with Python

**This module is part of these learning paths:**

Q1. You are creating a deep neural network to train a classification model that predicts to which of three classes an observation belongs based on 10 numeric features. Which of the following statements is true of the network architecture?

- The input layer should contain three nodes
- The network should contain three hidden layers
**The output layer should contain three nodes**

Q2. You are training a deep neural network. You configure the training process to use 50 epochs. What effect does this configuration have?

**The entire training dataset is passed through the network 50 times**- The training data is split into 50 subsets, and each subset is passed through the network
- The first 50 rows of data are used to train the model, and the remaining rows are used to validate it

Q3. You are creating a deep neural network. You increase the Learning Rate parameter. What effect does this setting have?

- More records are included in each batch passed through the network
**Larger adjustments are made to weight values during backpropagation**- More hidden layers are added to the network

Q4. You are creating a convolutional neural network. You want to reduce the size of the feature maps that are generated by a convolutional layer. What should you do?

- Reduce the size of the filter kernel used in the convolutional layer
- Increase the number of filters in the convolutional layer
**Add a pooling layer after the convolutional layer**

I hope this Create machine learning models Microsoft Quiz Answers would be useful for you to learn something new from this problem. If it helped you then don’t forget to bookmark our site for more Coding Solutions.

This Problem is intended for audiences of all experiences who are interested in learning about Data Science in a business context; there are no prerequisites.

Keep Learning!