**Introduction to Machine Learning**

Machine learning, a branch of artificial intelligence (AI), empowers computers to evolve and adjust independently without explicit programming. The learning process initiates from data or observations, like examples, firsthand experiences, or tutoring, enabling us to spot patterns in data and enhance future decision-making based on the examples introduced. The essential objective is for computers to autonomously learn without human intervention, subsequently adjusting their actions.

**Text Interpretation in Machine Learning**

When using conventional machine learning algorithms, text is considered a sequence of keywords; however, semantic analysis techniques replicate the human aptitude to understand a document's meaning.

**Handling Categorical Data**

Talking about algorithm vs. model framework, to utilize categorical data for machine classification, the text labels need to be transformed into an alternate format. There are primarily two commonly applied encodings. Label encoding is the first, which substitutes each text label value with a distinct number. Alternatively, one-hot encoding converts each text label value into a binary value column. Most machine learning frameworks come with functions that perform these transformations. Label encoding can occasionally mislead the machine learning system into assuming that the encoded column is ordered, hence one-hot encoding is generally favoured.

**Machine Regression and Data Normalization**

For machine regression, numerical data must be normalized. Otherwise, numbers with a bigger range might amplify the Euclidean distance between feature vectors, potentially overshadowing other fields, and the gradient descent optimization may struggle to converge. Several techniques exist for normalizing and standardizing data for ML, including mean normalization, standardization, min-max normalization, and scaling to unit length.

**Features and Their Importance**

A machine learning feature is akin to an explanatory variable used in statistical techniques like linear regression. Feature vectors are numerical vectors encapsulating all the features for a single row. Feature selection involves choosing a minimum set of independent variables that serve to explain the problem. If two variables are considerably correlated, they should either be fused into a single feature or one should be eliminated.

**Spectrum of Machine Learning Algorithms**

Within machine learning, the spectrum of algorithms stretches from simple logistic and linear regression to combinations of other models (known as ensembles) and intricate deep neural networks. Machine learning adopts two methods: supervised and unsupervised learning. Supervised learning involves educating a model on known input and output data while unsupervised learning involves exploring hidden structures and unseen patterns within input data.

Unsupervised ML algorithms resonate best when the training data isn't classified or labelled, exploring the data and drawing out unseen structures from unlabelled data using datasets. On the other end, supervised ML algorithms can forecast future occurrences by applying past learning to fresh data using labelled instances.

Reinforcement ML algorithms are learning techniques that examine their surroundings by executing actions and noting errors or rewards. Semi-supervised ML algorithms fall between supervised and unsupervised learning as they incorporate both labelled and unlabelled data.

**Conclusion**

In essence, machine learning algorithms are like engines that convert a dataset into a model. The appropriate type of algorithm (unsupervised, supervised, classification, regression) depends on the kind of problem at hand, the computing resources at disposal, and the data's inherent nature.