7 High-Protein Breakfasts to Try When You're Tired of Eggs


Eggs aren't the only healthy, high-protein breakfast option.Credit: Alexander Spatari / Getty Images
Eggs aren't the only healthy, high-protein breakfast option.
Credit: Alexander Spatari / Getty Images
  • Eggs are a nutritious breakfast option, but plenty of other foods can provide protein and key nutrients in the morning.
  • Options like Greek yogurt, cottage cheese, and tofu can offer similar protein benefits while adding variety to your routine.
  • Many of these alternatives also provide extra nutrients, such as fiber and probiotics.

Eggs are a breakfast staple for many. Two large eggs pack 12.6 grams of protein and also deliver healthy fats, vitamins, and minerals. However, eating eggs every morning can get old fast.  Fortunately, many nutritious, satisfying alternatives provide key nutrients, helping you start your day in a healthy and balanced way.

1. Greek Yogurt

Credit: pamela_d_mcadams / Getty Images
Credit: pamela_d_mcadams / Getty Images

Greek yogurt is a perfect substitute for eggs because it’s rich in protein. Greek yogurt contains about twice as much protein as regular yogurt, making it a better choice for those who want a high-protein breakfast.

  • Key nutritional benefits: Greek yogurt contains 17 grams of protein per three-quarter cup serving and is also rich in calcium, B12, and selenium. Products made with “live and active cultures” are a good source of gut-friendly probiotics.
  • How to enjoy: Enjoy Greek yogurt topped with fresh fruit and seeds, or mix it into smoothies or smoothie bowls.

2. Oatmeal

Credit: BURCU ATALAY TANKUT / Getty Images
Credit: BURCU ATALAY TANKUT / Getty Images

While oats aren't high in protein on their own, pairing them with high-protein ingredients, like collagen peptides, nut butters, Greek yogurt, and protein powder, can create a breakfast with staying power.

  • Key nutritional benefits: Oats provide a type of soluble fiber called beta-glucan, which supports heart health by regulating blood lipid levels and blood sugar. Oats also provide small amounts of essential minerals, like zinc and iron.
  • How to enjoy: Oats can be used to make meal prep-friendly recipes, like overnight oats and baked oats. 

3. Tofu 

Credit: HUIZENG HU / Getty Images
Credit: HUIZENG HU / Getty Images

Tofu is commonly used as an egg replacement in plant-based dishes like scrambles and omelets. Like eggs, tofu is high in protein and works well with savory ingredients like vegetables and cheese.  

  • Key nutritional benefits: Tofu is high in protein, packing just under 22 grams per half-cup serving. It’s also low in carbs and a good source of calcium, selenium, zinc, and iron, which are nutrients that tend to be low in many plant-based diets, such as vegan diets.
  • How to enjoy: Try tofu scrambles with vegetables, beans, and cheese, or enjoy pan-fried tofu sliced on top of avocado toast. 

4. Cottage Cheese

Credit: Aksana Ban / Getty Images
Credit: Aksana Ban / Getty Images

Cottage cheese is high in protein, which can help you feel full for hours after eating. In fact, cottage cheese is often added to egg dishes, like scrambles, to boost their protein content. Cottage cheese works well in both sweet and savory breakfast dishes, making it a versatile ingredient to keep in your fridge. 

  • Key nutritional benefits: Cottage cheese provides 25 grams of protein per cup and is an excellent source of calcium and phosphorus, which support and protect bone health.
  • How to enjoy: Top cottage cheese with fruit, nuts, and a drizzle of honey for a sweet and straightforward breakfast. Or combine cottage cheese with salt and pepper, then pair it with chopped cucumber and tomatoes for a more savory option. 

5. Beans and Lentils

Credit: Grace Cary / Getty Images
Credit: Grace Cary / Getty Images

Packed with plant-based protein and fiber, beans and lentils are among the healthiest breakfast foods you can eat. They make excellent stand-ins for eggs in dishes like scrambles and hashes, and are a solid breakfast option for those following vegetarian or vegan diets.

  • Key nutritional benefits: Beans and lentils are loaded with protein and fiber. A cup of lentils provides 17.9 grams of protein and 15.6 grams of fiber, nutrients that help slow digestion, supporting feelings of fullness and regulating blood sugar. Beans and lentils are also high in folate, magnesium, and zinc.
  • How to enjoy: Make a vegetarian hash with chickpeas, sweet potatoes, and spinach, or use mashed black beans or lentils as a high-protein topping for toast.

6. Smoothies

Credit: zefirchik06 / Getty Images
Credit: zefirchik06 / Getty Images

Smoothies are convenient, take just minutes to make, and can be tailored to your taste and dietary preferences. Protein smoothies make an excellent egg substitute and are suitable for almost every diet, including plant-based, low-carb, and keto diets

  • Key nutritional benefits: Smoothies made with high-protein ingredients, like whey protein or Greek yogurt, can pack 35 grams or more of protein per serving.
  • How to enjoy: Blend frozen fruit with your choice of milk, a protein powder such as whey or pea protein, and nutrient-dense add-ins like nut butter or seeds.

7. Chia Pudding

Credit: kasia2003 / Getty Images
Credit: kasia2003 / Getty Images

Chia pudding provides protein, but it also delivers fiber and other key nutrients eggs lack. You can also prepare it in batches ahead of time, making it an easy, nutritious breakfast to grab on busy mornings.

  • Key nutritional benefits: Chia seeds, the main ingredient in chia pudding, are especially high in fiber, providing about 9.75 grams per ounce, and are also rich in minerals such as calcium, iron, magnesium, manganese, and selenium. While they don’t pack as much protein as eggs, chia seeds still deliver a solid amount, providing roughly 4.7 grams per ounce.
  • How to enjoy: To make chia pudding, combine ½ cup of unsweetened milk or plant-based milk with two tablespoons of chia seeds and a touch of sweetener, such as monk fruit or maple syrup. Stir or shake well, then refrigerate until the mixture thickens and becomes creamy.



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What Are Selection Techniques

Selection techniques in machine learning help in reducing the noise by taking in only the relevant data after the pre-processing. The techniques have the ability to choose the relevant variables according to the type of user’s problem. In case any data comes up that is not relevant to the requirement, it tends to slow down the efficiency process of the model and also decrease the accuracy. Therefore, it is very important to have appropriate feature selection techniques for the models in order to have better outcomes and accuracy. 

The main idea of working with selection techniques is to manually extract the relevant settings from the parent set to have high-accuracy model structures.

Feature Selection in Machine learning

The techniques are divided into the category of supervised and unsupervised learning. These two categories are further divided into 4 main methods for selecting the features.

Filter Method :

There are statistical ways for selecting the features using the filter method. The features are selected in the pre-processing stage as there is no learning process involved in this. The aim of this approach is to filter out the unrequired and irrelevant features by using matrices and ranking methods. The most important advantage of using the filter method is that it does not overfit the data.

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Wrapper Method :

In this method, a user makes different combinations that are evaluated or compared with a lot of other possible combinations. In this way, the feature selection is done. A subset of features is selected and the algorithm is trained based on the subset. The output of the algorithm then decides if the features will be added or not. This method is further based on 4 types which are:

  • Forward Selection : This process takes in an empty feature set. It keeps adding a feature to each interaction and checks the progress simultaneously as if it is improving or not. This method keeps on iterating unless there comes a feature that does not improve the progress of the model.
  • Backward Elimination : This approach is the complete opposite of the forward selection approach. The process takes in all the features of the algorithm and then keeps removing a feature one by one on each iteration. It checks the progress simultaneously as if it is improving or not. This method keeps on iterating unless there comes a feature that does not improve the progress of the model.
  • Exhaustive Feature Selection : It is the most common approach for feature selection as each feature is set as brute-force. The approach aims to try various combinations of features in order to give the best outcome.
  • Recursive Feature Elimination : This method is based on the greedy approach as its features are selected in a smaller amount. An estimator is made to test every set of features designed and thus we get an outcome of the best features.
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Embedded Method :

This is a great method for feature selection as it has the advantages for both filter and wrapper methods collectively. The processing time in the embedded method is very high just like the filter method, however, they provide more accurate outcomes.

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There are a few techniques involved with embedded methods which are:

  • Regularisation : This aims at regularising the feature selection method simply by adding a penalty if the data gets overfitted in the model. The points shrink to a value of 0 and they are eliminated from the dataset. The types of regularizations are L1, L2, L3, etc. 
  • Random Forest Importance : This technique involves a lot of tree-based approaches to select the features for an algorithm. A number of decision trees are involved in this as the ranking of nodes is performed in all the trees to get the results. After filtering out the irrelevant nodes, a subset of the most relevant nodes creates a final selection of features.
Hybrid Method :

This approach takes in features as small-sized samples. The main idea is to select the features using instance learning. The features that correspond to the instances are selected as they are relevant to the algorithm.

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Feature Selection Models

Supervised Model :

This model is defined as the class of machine learning methodologies where the user can train with the help of continuous and well-labelled data. For instance, the data can be historical data where the user wishes to predict whether a customer will take a loan or not. Supervised algorithms tend to train over the well-structured data after the preprocessing and feature characterization of this labelled data. It is further tested on a completely new data point for the prediction of a loan defaulter. The most popular supervised learning algorithms are the k-nearest neighbour algorithm, linear regression algorithm, logistic regression, decision tree, etc.

This is further divided into 2 categories:

  • Regression: The dealing of output variables is done using regressions as it includes graphs, images, etc. For example to determine age, height, etc. 
  • Classification: it helps in classifying different objects such as yellow, orange, wrong or right, etc.
Unsupervised Model

This model is defined as a class of machine learning methodologies where the tasks are performed using the unlabelled data. Clustering is the most popular use case for unsupervised algorithms. It is defined as the process of grouping similar data points together without manual intervention. The most popular unsupervised learning algorithms are k-means, k-medoids, etc. 

This is further divided into 2 categories:

  • Clustering :This means when the machine requires an inherent group while training the data.
  • Association :This category has a set of rules which helps in the identification of massive data. For example, a list of students who could be interested in artificial intelligence as well as machine learning.
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How To Choose a Feature Selection Model

It is very important for machine learning engineers as well as researchers to understand which feature selection model is most suitable for them. The most data types are known by the engineer, the easier it will be for him to choose properly and wisely. This whole concept is based on 4 main approaches which are:

  • Numerical Input, Numerical Output : There are two methods used in this technique which are Pearson’s correlation coefficient and Spearman’s Rank Coefficient.  The numerals are basically used for the prediction of regression models for continuous numerical such as int, float, etc. 
  • Numerical Input, Categorical Output : There are two methods used in this technique which are the ANOVA correlation coefficient, and Kendall’s rank coefficient. The numerals are basically used for the classification of predictive models for continuous numerical such as int, float, etc. 
  • Categorical Input, Numerical Output : This is a case of the prediction of regression models using input based on categories. The process is the same as numerical input, and categorical output but in a reverse fashion. 
  • Categorical Input, Categorical Output : This is a case of classification of predictive models using both categorical inputs as well as outputs. The main approach affiliated with this method is the Chi-squared method. Moreover, information gain can also be used with this technique.

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Conclusion:

The process of selecting features in machine learning is a vast concept and it involves a lot of research to select the best features. However there is no hard and fast rule for making the selection, it all depends on the type of model and its algorithm and how a machine learning engineer wants to pursue it. Selection techniques in machine learning help in reducing the noise by taking in only the relevant data after the pre-processing. 

In this article, we have talked about various feature selection methods that use certain algorithms for making the best possible outcomes and why we should make this feature selection method. Along with this, we have talked about how we can finalise the best feature selection model to work with.

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