The #1 Python Data Scientist: Sentiment Analysis & More

Explore how to use NumPy to replace Python lists with fast, multi-dimensional arrays, create and manipulate NumPy arrays, and perform array operations in Google Colab.

Explore NumPy, a Python library that provides fast, powerful arrays with a C backend, supporting data science, machine learning, web services with Flask, and conversions with pandas.

Install numpy and access the numpy library via Google Colab or terminal, import numpy as np, and run numpy in your Python programs to use its functionality.

Explore creating one-dimensional numpy arrays by converting a Python list with np.array, and by building zeros, ones, and empty arrays; compare arange and linspace for ranges and spacing.

Explore creating and reshaping multi-dimensional NumPy matrices from Python lists, zeros, ones, and empty arrays, and using the from function to generate matrix data.

Access and modify elements in one-dimensional and multi-dimensional NumPy arrays, and use slicing to retrieve multiple values. Index by row and column, and reassign rows or ranges while preserving shape.

Explore arithmetic operations on NumPy arrays, including scalar and vector calculations, matrix addition, slicing, and dot products using the at operator and the dot function.

Explore NumPy functions to modify arrays and matrices, including append, insert, and delete, with guidance on axis-based operations for rows and columns.

Explore numpy built-in functions to retrieve array properties and modify arrays, including max, min, mean, argmax, argmin, and non-zero indices, plus sort, flip, transpose, and flatten for arrays and matrices.

Explore numpy basics, from creating arrays from lists and dictionaries to indexing, properties, and one-dimensional and multi-dimensional operations, plus using built-in functions and future learning paths.

Learn to use pandas for data analysis, mastering series, data frames, date ranges, and reading CSVs through practical, code-first tutorials in Python with NumPy and Google Colab.

Discover pandas for data manipulation, focusing on series and data frames, multi dimensional data, and Python and C efficiency. Learn its applications in data science, machine learning, and CSV reading.

Install pandas, import it in Google Colab or any Python program, and explore basic pandas concepts like series and data frames using pip, terminal, and basic editors.

Create pandas series from Python lists and dictionaries, with explicit indices, data type control, and handling of None and NaN, with numpy arrays.

Explore how to create and manipulate date ranges in pandas using date_range, start and end dates, periods, and frequency, and extract date components for indexed data.

Retrieve single elements from a pandas series by position or label, then slice for multiple elements. Filter with boolean tests and use series.get for safe defaults.

Explore how to retrieve and describe numerical and object properties from pandas series, including count, max, min, describe, value counts, and index of extremes.

Modify pandas series by adding, removing, and reindexing elements, renaming labels, and shuffling data using slicing, drop, append, and pop techniques.

Explore pandas series operations: compare and iterate two series, obtain boolean results, and distinguish elementwise equals from series.equals, with fill values and scalar comparisons.

Learn how to create pandas data frames from lists, dictionaries, and series, with explicit row and column labels, data types, and scalar filling to shape the frame.

Explore how to fetch elements from data frames by column, by row, and by boolean conditions; practice slicing rows and columns, using loc and iloc, and applying head and tail.

Explore how to fetch numerical properties from data frames using pandas, including max, min, count, and describe, and use axis, index, and histograms to summarize distributions.

Learn to modify data frames by adding and removing elements, reassigning individual values, and restructuring columns, rows, and indexes, including appending, dropping, and reshaping data frames.

Master dataframe operations in pandas, including arithmetic, function application, and transposition. Learn typecasting, sorting by index or values, and using transform and apply for flexible data manipulation.

Explore pandas data frame comparisons and iteration across rows and columns, using operators and methods to compare entire frames, individual elements, and iterated rows or columns.

Master reading csv files with pandas in Google Colab by uploading, decoding to utf-8, and using read_csv with options for separators, headers, names, and skipping rows or blank lines.

Master pandas by learning to create and manipulate series and data frames, read data, and perform comparisons and iterations across multi-dimensional data. Explore the pandas documentation.

Master the python pipelines plotting library and graph data with practical, follow-along examples. Install pipelines, build line and scatter plots, then explore bar, histogram, and 3D visuals.

Explore how the pipeline library in map plot makes graphing in Python easy by treating graphs as figures and handling axes. Learn to create scatter, line, bar, and 3D graphs.

Install the map plot library via Google Colab or the terminal, learn where to find the package and docs, and import it in a Python file to access pipeline.

Create a basic line plot and a scatter plot in Python using 100 random data points, pairing x values 1 to 100 and visualizing with a plot library.

Customize graphs by adding titles and axis labels, and adjust line and dot appearance. Explore color options, marker shapes, and line styles with the plot function and set_p.

Plot multiple datasets on one plot or create subplots to compare data directly, using colors and line styles, with options for side-by-side or stacked layouts.

Learn to create and customize a bar chart for categorical data, using a simple dictionary dataset to plot counts across categories, with titles, axis labels, and color options.

Explore how to build and customize a pie chart to represent proportional data, with values summing to 100, using labels, start angle, explode effects, and shadows.

Explore how to build and customize a histogram from a single data set, compare it with bar charts, and use color, cumulative, and density options to display counts or proportions.

Explore 3D plotting with x, y, and z data using mpl_toolkits.mplot3d to create and customize a 3D scatter plot, including axis labels and marker options.

Learn to graph diverse data with pi plot in Python, from bar graphs and pie charts to line, scatter, histogram, and 3D plots, with easy customization and subplots.

Learn what machine learning is, what it can do, and why beginners study it in a theory-only overview that covers problems, types, model construction, and benefits.

Explore what machine learning is, how it compares to AI, and how data patterns guide tasks from image and speech recognition to text processing, with an overview of ML workflows.

Explore what machine learning is by studying data to infer patterns with statistics and algorithms, training models that improve over time to solve tasks without explicit programming.

Explore how machine learning solves real-world problems—pattern recognition, training models, mapping inputs to outputs, and applying prediction and classification across domains like customization, translation, and game ai.

Explore how machine learning solves problems through customization, translation, and game ai, while revisiting prediction and classification to improve the customer experience.

Explore the three main machine learning types—supervised, unsupervised, and reinforcement—focused on pattern recognition, training with inputs and labels, data clustering, and reward-based learning.

Learn how machine learning builds neural networks from inputs to outputs, detailing neurons, weights, bias, the weighted sum, and activation functions with training using sigmoid, tanh, and softmax.

Explore common neural network structures, including feedforward (single and multi-layer), radial basis, convolutional, recurrent, modular, and sequence-to-sequence models, with training via backpropagation, delta rule, and supervised learning.

Decide whether machine learning can solve the problem, choose a model, gather and format data, build and train the model, then test, evaluate, and refactor to improve performance.

Reflect on the theory behind machine learning, its capabilities, the types of learning, and how machine learning works, guiding steps to build a model.

Explore how to use TensorFlow to build machine learning projects with beginner-friendly guidance. Learn basics like linear regression, training and testing models, and using Google Colab for code execution.

Explore tensor flow, a free open-source library for numerical computation and machine learning, and its 2.0 overhaul that streamlines building, training, and deploying models with tensors.

Install tensorflow in google colab or a local python file, import it as tf, and run pip install tensorflow in mac, linux, or windows with admin privileges.

Learn linear regression by fitting a line of best fit described by y equals m x plus b, using a scatter plot and gradient descent to minimize total error.

Create synthetic training and testing data for a linear regression model by generating x values with linspace, defining m and b, adding noise, and plotting the dataset.

Build a linear regression model using tensorflow variable nodes for weights and bias; implement the forward graph to output y = mx + b and train with gradient descent.

Build a linear regression model by implementing a loss function that computes the mean of squared differences between outputs and expected labels, guiding gradient descent to adjust weights and biases.

Train a linear regression model by building a train function and training loop, optimize weights and biases with gradient descent, monitor loss across epochs.

Evaluate a linear regression model in TensorFlow by inspecting weights and bias, plotting the line of best fit, and assessing performance via loss.

Explore how to use TensorFlow to build, train, and test a simple linear regression model, implement gradient descent, and evaluate performance with loss metrics.

Learn to build a sentiment analysis model with TensorFlow that classifies movie reviews as positive or negative, and perform preprocessing, training, and evaluation in Python with numpy, pandas, and Colab.

Explore how machines interpret text, compare human and machine analysis, and learn core techniques like word vectors, embeddings, and recurrent neural networks for sentiment analysis.

Examine and load two datasets of positive and negative reviews, label them with sentiment indicators, and prepare dataframes for model training.

Format and encode sentiment data by tokenizing, selecting the 10000 most common words, padding to 20 time steps, reversing sequences, and one-hot encoding labels for model training.

Build a sentiment analysis model with a sequential architecture using embedding, GRU, and dense layers, leveraging tokenization and softmax for a two-class output.

train the sentiment analysis model using categorical cross entropy loss and the atom optimizer, run with x and y for 10 epochs, and plot loss over time to evaluate performance.

Test the sentiment analysis model by preparing inputs, tokenizing text, padding sequences, and predicting whether reviews are positive or negative.

Explore how machines interpret sentiment in text and build a TensorFlow model that classifies reviews as positive or negative using embedding and dense layers.