Machine Learning: Master the Fundamentals

Master simple linear regression using hours studied versus marks, learn the equation and r squared, then compare linear and polynomial models in Python, including test versus train and data preprocessing.

Master multiple linear regression with two factors—hours studied and class questions—to predict marks, and manage outliers via box plots, IQR, and transformations.

Apply Bayes theorem and conditional probability to build a Naive Bayes classifier, using weather scenarios and movie reviews, with vectorization, training, and accuracy results.

Explore unsupervised clustering with k-means and distance-based similarity, using the iris dataset, and apply principal component analysis for dimensionality reduction, evaluating clusters with silhouette and elbow method.

Explore core NLP concepts such as sentence segmentation, tokenization, stemming, lemmatization, stopwords, POS tagging, and dependency parsing using NLTK and SpaCy.

Explore lexical, syntactic, and referential ambiguities in NLP and how multiple interpretations arise for words and sentences, impacting system interpretation and dependability.

Understand why recurrent neural networks fit sequential data by preserving context through loops and a hidden state, enabling memory of past inputs for time series and next-word prediction.

Explore the math of RNNs, where the hidden state updates in loops using inputs, weights, and biases to produce y_t via softmax, trained by gradient descent against ground truth.

Learn how LSTM networks overcome long-term dependency and vanishing gradient problems by using memory cells and four interacting gates to retain and selectively update context for sequence prediction.

Build a spam detection model with RNN and LSTM using TensorFlow and Keras, incorporating embedding, dropout, and a word cloud for preprocessing and visualization, achieving about 98% accuracy.

Contrast lstm and transformer architectures, showing how parallel processing and attention enable transformers to capture long-term dependencies with multi-head self-attention and positional encodings.

Design and deploy a chat bot with Streamlit using OpenAI API keys, emphasizing data privacy, and tune the model with max tokens, top_p, frequency penalty, presence penalty, and temperature.

Set up infrastructure for streamlined machine learning apps with Anaconda and Notepad, then use the Anaconda prompt to install Streamlit and libraries like numpy and pandas, code a .py app.

Create your first web app with Streamlit to display hello world, adjust font size and color via markdown, save as app.py, and run it to view the local web page.

Create headers, sub headers, and a title with warning, success, and error messages for an app, then run the Streamlit app, upload a file, and view machine learning results.

Access a folder-stored CSV file, read its data, and display the contents in a web app using Streamlit. Debug a name SD is not defined error by importing Streamlit.

Create a file upload button with a file uploader widget to select and process files. Browse local files and handle size limits, with higher limits available via paid options.

Create a natural language processing powered word cloud app that highlights the most frequent words in articles while removing stopwords and displays results with larger fonts for higher frequency.

Deploy a streamlit app to Heroku by preparing files like requirements.txt and Procfile, logging into Heroku, creating the app, and pushing with git to obtain an external URL.

Learn Python in Google Colab, a cloud-based environment where you sign in with Google, connect to Google Compute Engine, run code in a browser, and use code cells and comments.

Explore Python arrays by comparing list, tuple, set, and dictionary, noting order, mutability, duplicates, and indexing. Learn key operations like append, remove, clear, and union to manage data.

Explore NumPy's powerful array operations, including one- and two-dimensional arrays, indexing, slicing, and reshaping, to manage large data efficiently. Master joining, splitting, searching, and sorting arrays for data analysis.