Machine Learning Practice Problems Python

The machine learning practice process includes the integrations of learning about the thesis beyond the machine learning algorithms and approaches them for solving real-world problems. Here we have only high qualified writers on machine learning areas who are working. Our research editors do multiple revisions so that our paper is free from plagiarism, Grammer errors, spellings, facts etc.…No trace of plagiarism will be found in your manuscript. For all types of machine learning practice problems in python we derive correct methodology to gain our answer.  Python is extensively deployed in machine learning because of its clarity and the strong powerful libraries like scikit-learn TensorFlow, and PyTorch.

We provide a constructive approach for practicing machine learning with python; let us start from simple problems and slowly increasing difficulties:

Beginner Level

1. Iris Classification:

• The popular Iris dataset is used by us for classifying the flower types.
• The libraries we use : scikit-learn
• Some algorithms like logistic Regression, k-Nearest Neighbours and Support Vector Machine (SVM).

2. Boston Housing Price Prediction:

• We forecast the median value of homes in the Boston area.
• Libraries are : scikit-learn
• Linear Regression, Decision Trees are the algorithms are uses in this process

3. Diabetes Progression Prediction:

• By working with a dataset, we predict diabetes in advance after one year depending on guideline measurements.
• Scikit-learn is the library that is deployed in this method.
• The algorithms are Ridge Regression and Lasso Regression.

Intermediate Level

1. Sentiment Analysis of Movie Reviews:

• The sentiment of the movie is classified by us as positive or negative reviews.
• For Natural Language Processing (NLP) pre-processing, we accomplish libraries like scikit-learn, nltk or spacy.
• Naive Bayes, Random Forest, and LSTM with keras or PyTorch are some of the algorithms used by us.

2. Handwritten Digit Recognition (MNIST):

• By utilizing MNIST dataset, we realize the handwritten numbers.
• TensorFlow or PyTorch are the accommodated libraries in this method.
• The algorithm we try in this process is Convolutional Neural Networks (CNN).

1. Stock Prices Prediction:

• Through historical data, it forecasts the future stock prices.
• We use pandas for data manipulation, matplotlib for plotting and scikit-learn for performing linear models.
• ARIMA and LSTM are the approachable algorithms.

Advanced Level

1. Image Classification with CIFAR-10:

• This works on more critical image datasets with different classifications of objects.
• Such used libraries are Tensorflow or PyTorch .
• We must try algorithms like Deep CNNs, ResNet, or Transfer Learning with pre-trained models.

2. Natural Language Processing with Transformer Models:

• Among the transformer models, BERT is utilized by us for performing various NLP tasks like question and answering or text summarization.
• Libraries that we use are transformers by Hugging Face, TensorFlow and PyTorch .

3. Forecasting Air Quality Index:

• Depending on historical data, we predict the Air Quality Index (AQI) of a zone.
• For time series forecasting, consider libraries like scikit-learn, fbprophet

Real-World Data Challenges

1. Kaggle Competitions:

• We must engage in kaggle competitions for solving practical problems with datasets distributed by the companies and associations.
• Based on the problem statement, we use all extracted libraries.

2. UCI Machine Learning Repository:

• A dataset is chosen by us for the UCI storehouse and an attempt to define the problem statement, data pre-processing and then apply ML algorithms for solutions.

3. Create Our Own Project:

• Collect our own data or deploy API (Application Programming Interface) for fetching data from the web.
• First clean the data pre-process and apply machine learning for gaining awareness or making a prediction.

   Reminders:

• Matplotlib or Seaborn libraries used by us for data visualization.
• Considering data manipulation, we use pandas libraries.
• For examining the performance of our model, always divide the data into training and test sets.
• Cross-validation process is employed for the best evaluation model.
• We practice the hyper parameter tuning with GridSearchCV or RandomizedSearchCV in Scikit-learn.

By practising these types of problems will not only develop our machine learning knowledge, but also it makes us adaptable with the Python ML (Machine Learning) ecosystem.

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