Some people start by learning Python in order to advance their programming skills before they get to machine learning, You may just be like, “I want to learn machine learning”, and don’t understand that there’s a bit of preparation you have to go through before you get there.

Today, people are witnessing the rapid evolution of many industries by means of the ML method and the changes that take place in societies’ interactions with technologies. Starting from the recommendation technique on Netflix to the self-driving cars, all these facilities rely on the concepts of ML. As has been seen, for beginners the idea of engaging with ML can be quite overwhelming. But due to its simplicity and the availability of powerful libraries it is quite popular among the people. This blog gives guide to the newcomers on how to begin with machine learning using the programming language, Python.

Understanding Machine Learning

Machine learning is a subfield of of AI that deals with designing models that are able to improve their correct answers with experience as well as data fed into the model. Thus, while in traditional programming, the developers are providing instructions for the system regarding the rules according to which it must operate, in ML, the algorithms are trained on data to make decisions.

Types of Machine Learning

1. Supervised Learning: This is still one of the most used types of ML. It entails the building of a model based on a set of data where the data set is labeled, in that, the data contains the input to the model and the expected output. It learns the current inputs to compute the required output. Some of the uses of the models are in spam detection, image classification, and house price prediction.
2. Unsupervised Learning: In this type, the model is developed on the data that does not contain the responses which are to be predicted. It attempts to search for pendulum like structures or some form of relationship in the data. Some of the regular activities include; clustering, which involves establishing relations between closely related elements and association where the commonalities are outlined with reference to a majority rule of the data.
3. Reinforcement Learning: In development, learning occurs through an interaction proceeding from an environment significant for the model. Depending on the action it executes, it is rewarded or punished. Such a strategy is in great demand in robotics, video games, and navigational applications.

The Machine Learning Workflow

When it comes to the actual problem-solving process of any normal ML project, having an idea of how it looks helps in proper organization of the work. Here’s a high-level overview:

1. Define the Problem The first step of the process therefore calls for one to establish the nature of the problem to be addressed. For instance, if you’re working on a house price prediction model, your problem definition would be: “Make a forecast on the possible price of a house given some of its properties such as size, the area of the house and number of bedrooms. ”
2. Collect and Prepare Data This means that data forms the basis of any ML model that is developed. Getting good data is important and using the right data is important too. After the collection of data, the type of data sometimes requires some preprocessing which may include; handling of missing values, converting categorical features to numerical features, and normalization or standardization of the numerical features respectively.
3. Explore and Visualize Data Exploratory measures assist you, to get a feel of various patterns existing with your data as well as how they are related. Libraries like Pandas and Seaborn help in plotting histograms, scatter plots, and correlation matrix to name a few.
4. Choose a Model Depending on the type of the problem, choosing of the right model is appropriate. For the regression classification (the output or dependent variable is continuous) you may use linear regression or decision trees. In case of classification problems (predicting classes) the models like logistic regression, support vector machines or random forest might be more suitable.

5. Train the Model Training is the process through which you make your model go through the data set in order for it to learn some of the patterns. This step can involve partitioning of your data in to training and testing sets to assess the model’s predictive capability on new data.
6. Evaluate the Model When validating your model, use specific measures related to your problem to gauge your model’s proficiency. Regarding regression analysis, the typical measures of accuracy are Mean Squared Error (MSE) and R-squared. Such measures as accuracy, precision, recall, as well as F1 score can be used in classification.
7. Tune Hyperparameters They are settings that you set before training the model that helps in controlling the training process of your model. There is Grid Search and Random Search that can be used to identify the most appropriate hyperparameters for increasing the efficiency of the given model.
8. Deploy and Monitor After completing the desired accuracy, the model is best deployed to a production network where it can predict continuously. Testing is also vital so that we can detect whether the performance of the model on new data is as excellent as that on the current data so that appropriate adjustments can be made.

A Simple Machine Learning Project: Forecasting the price of houses

To illustrate these steps, let’s consider a simple project: used for the prediction of house prices. While we won’t dive into the code, we’ll outline the process:

1. Define the Problem: The client can forecast houses prices given their size, location, and number of bedrooms, among others.
2. Collect Data: Get a dataset from whatever sources one can find them from a website like Kaggle.
3. Prepare Data: Preprocess the data by, imputing the missing values, encoding the categorical or nominal data, and normalizing/standardizing the numerical or Continuous data.
4. Explore Data: When the data is collected, present the data visually to notice patterns and relations.
5. Choose Model: Choose a type of regression model for this regression task – it should be linear.
6. Train Model: Tear the data into train and test data and then fit it on to the model with the training data set.
7. Evaluate Model: Mean Squared Error was used to check the performance of the model and > comparison between actual and predicted data was drawn through the help of map.
8. Tune Hyperparameters: Tune the model hyperparameters so it reflects a better solution for the given problem.
9. Deploy Model: Test the model in conditions of an operational web application or similar environment and review the results. Next Steps Once you’ve grasped the basics of ML with Python, you can explore more advanced topics and techniques to enhance your skills:
10. Feature Engineering: In this case the procedure of creating new features from the existing ones in an endeavour to have better performing models.
11. Hyperparameter Tuning: Tuning of parameters of model in order to increase precision.
12. Cross-Validation: Using methods such as k-fold cross-validation to have better evaluation on the models.
13. Deep Learning: Entering into Neural Networks and using Tensor Flow and PyTorch and other frameworks to perform more advanced tasks.

Resources

• Books: “A. Géron ‘Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow’, 2017. ”
• Online Courses: There exists a full course offering platform for ML such as Coursera, edX, and Udacity.
• Communities: This is where one can join discussion boards such as Stack Over flow for actual interactions, Reddit’s r/Machine Learning and Kaggle for interactions and learning.

Conclusion

Python is one of the incredible tools to learn, and when combined with machine learning, infinite opportunities are created. This tutorial aims to give the reader a basic understanding of the principles, Introduction to key tools that will be used subsequently, together with an initial, easy project. When you are progressing in the field you need to know that it is a continuous process of learning and experimenting to be a successful machine learning engineer. You’ll be able to put in hard work and gain proficiency in this wonderful and rapidly growing field.