Complete DataScience with Python and Tensorflow

Master Python basics as a high-level language with a rich standard library, covering strings, dictionaries, variables, data structures, hashing, operators, conditionals, and functions.

Practice python basics through arithmetic operations like division and modulo, explore even/odd logic, work with strings and lists, and learn to comment code with hash to explain it.

Practice python basics by evaluating boolean expressions with and/or, determining true or false conditions across multiple statements.

Learn to create and manipulate lists in Python, separating values, adding and moving elements, and managing numeric and text items in a hands-on lab.

Learn how to work with Python lists: access the first and last elements using indices, and explore negative indexing. Perform insertions, removals, and sorting in ascending order.

Explore Python basics by comparing strings and lists, access elements by index, use negative indexing, measure length, and split strings in practical labs.

Explore Python basics in data science by manipulating strings: trim left and right spaces, convert strings, and perform basic operations to shape data.

Practice python basics in a hands on lab to reinforce core data science concepts using python and tensorflow as part of the complete data science with python and tensorflow course.

Create and manipulate dictionaries in Python basics lab, defining keys and values, accessing items with brackets, and listing keys to manage data.

Learn to use Python conditional statements to check whether a number lies between 20 and 30 or between 40 and 50. Combine multiple conditions with and and print the results.

Master defining and invoking generic Python functions, specifying expected inputs, and performing numeric checks to validate conditions during the Python basics lab.

Explore python basics through a lab that examines conditions and true evaluations to drive simple program logic.

Practice Python basics in the lab by defining and iterating over lists, building an empty list, and collecting items into a list.

Learn to create and populate a pandas data structure by defining column names, assigning values, and using an index to organize rows and columns.

Create and name columns, assign values, and form a new group in a pandas workflow, using school, numbers, and color to illustrate data organization and symbolism.

Explore core pandas operations on numeric columns, compute sums, and observe outputs in a data frame, preparing you to move on to the next lecture.

Apply pandas to create and manage groups, categories, and customer-like data within datasets. Explore values and basic data operations to organize data effectively in Python and TensorFlow contexts.

Explore basic pandas data manipulation by renaming columns, adjusting values, and leveraging a column for data selection and transformation in this introductory pandas lesson.

Apply custom functions to pandas data, compute column-wise maxima and minima, and explore practical steps for data exploration using simple, iterative techniques.

Learn pandas basics in part 9 of the introduction to pandas, focusing on naming two columns and showing the creativity column.

Explore numpy basics by practicing slicing and indexing to fetch specific rows, columns, and elements from arrays, and understand common patterns for retrieving data.

Explore numpy part 2 basics by analyzing the shape, counting rows and columns, and identifying the maximum value in the data.

Discover how to work with CSP formats, file system formats, and UCSC arguments, and apply various methods to adjust and replace steps, while reducing liability and keeping things professional.

Explore data in this EDA part, inspecting columns and missing values, using transpose for readability, and assessing distribution skewness to improve the model.

Explore data science concepts through eda part 3 and define class structures, while referencing reading materials in the context of python and tensorflow.

Explore exploratory data analysis (eda) in part 4, including handling the column, space, and aggregation functionality to prepare data in Python and TensorFlow workflows.

Examine quick, example-driven discussions of names, faces, and space as the topic unfolds, showing how suspects and events vacillate in eda part5.

Master the basics of machine learning, a field using statistical techniques to enable computers to learn from data without explicit programming, including supervised and unsupervised approaches and classification.

Explore fundamental machine learning concepts, including classification, handling missing values, data preprocessing, and model evaluation. Learn about bias, variance, training versus testing, and techniques to improve predictive performance.

Assess model performance with regression metrics—mean absolute error, mean squared error, and root mean squared error—then interpret classification with confusion matrix terms like true positives and false positives.

Explore how probability distributions describe random outcomes in machine learning, distinguishing discrete and continuous variables with examples like coin flips and rainfall, and introducing density functions.

Explore normal distribution and skewness, and apply log transformations to adjust skewed data. Review key properties, including mean, median, and the 68-95-99.7 percent rules.

Explore machine learning basics in a lab setting by analyzing a dataset, identifying missing values, and distinguishing numerical data from text data using Python object types.

Learn to count the total missing values per column, inspect per-column totals, and explore basic ways to address missing data in a machine learning basics lab.

Identify missing values in numerical and object columns, check data types, and fill numerical gaps with the average and object gaps with the most frequent value.

Replace missing values in the data by the average for the age column and by the most frequent value for other categorical columns, then verify all missing values are filled.

Explore handling missing values by using existing columns and simple imputation, then detect outliers with a box plot using the 1.5 iqr rule.

Identify two points from given equations, compute their coordinates, and determine potential low and high class regions, illustrating fundamentals from the machine learning basics lab.

Create a new column initialized to zero to flag potential outliers, then set values via filtering and above-or-below comparisons. Use lookups to count frequencies and craft a reusable function.

Extract input features from the dataset and define the output variable to train a classifier that predicts passenger survival using all available columns, reserving about 20 percent for testing.

Learn to build and test a classification model that trains on input data, predicts outputs, and evaluates accuracy using real data.

Identify the most important features from hundreds of inputs using feature importance scores and rank them in descending order for a classification model.

Learn the basics of cross-validation in machine learning, using randomization and simple estimators to train models and assess accuracy with cost-based metrics.

Explore core machine learning concepts with a focus on classification and regression metrics, including unsupervised and logistic regression, and learn practical implementations using Kettler.

Explore regression in machine learning by examining how predictors or input variables relate to a response, and how regression coefficients capture these relationships.

Continue discussion on linear regression with one response and one predictor, fit a best line by minimizing the sum of squared errors, using actual versus predicted values to assess fit.

Explore non-linear relationships in regression by applying normalization and polynomial features, compare linear versus elastic net models in scikit-learn, and select features to improve predictions.

Explore building a simple linear regression model, setting up data, and evaluating results with tests in a hands-on lab for regression fundamentals.

Apply regression modeling techniques using a decision matrix and metrics to evaluate how well a linear model fits data, emphasizing metric meaning and model comparison through simple calculations.

Practice lasso and elastic net regression in regression lab part 3, initialize models, fit on test data, and compare performance to select the best regularized regression approach.

Review the last lecture's issues, try solutions on top of them, and explore different styles and colors in regression lab part 4.

Define x and y, apply an ml regression estimator, and compute the average estimate in a practical lab.

Transform the stock variable, cross traditional direction, and proceed with coding steps in this regression lab.

Explore how to frame a problem as discrete classification, using variables to label an individual as healthy or not, and introduce logistic regression.

Apply logistic regression for binary classification by using a sigmoid function to map inputs X into probabilities. Evaluate the model with training and test data to interpret predictions.

Explore logistic regression for classification by breaking down the concept through a practical example. See how a test outcome informs predictions and identifies when results may fail.

Learn how to use decision trees for classification from scratch, including basic data splitting and model building. Apply these concepts to practical data tasks in this lecture.

Learn how principal component analysis reduces thousands of features to key components, enabling easier visualization with scatterplots and managing computational complexity in high-dimensional data.

Learn how the k-nearest neighbors algorithm uses distance measures for classification, examines records, and applies majority voting to predictions.

Explore discriminant classification methods, comparing linear discriminant analysis and quadratic discriminant analysis, and discuss the assumptions of normal class distributions, limitations, and practical considerations for real-world data.

Clustering groups objects so items in the same cluster resemble each other more. It covers machine learning applications and string based distribution for handling data, including text.

Apply association rule based machine learning through market basket analysis, understanding how item frequency and confidence reveal meaningful association rules.

Explore classification models by applying logistic regression and discriminant analysis, including linear and quadratic discriminant analysis, and compare their accuracies around 70–77 percent.

Practice ml algorithms in a hands-on lab, implementing nearest neighbors and classification, tuning features and scaling, and exploring decision trees and max-based changes.

Learn how to classify data using skm, explore changing parameters like entropy, and apply packages to identify different groups, while preparing for future topics and assignments.

Explore ml algo lab part 6 by practicing lasso and elastic like so, and perform checks and use metrics to understand the results.

Improve a classification automation model by selecting estimators and tuning parameters like max features and criterion, and analyze performance using cv values to choose the best setup.

Explore selecting the best values for max features and estimators in an ml lab, using dictionaries and lists to set values and evaluate the final configuration.

Explore tuning two hyperparameters to identify the best values, demonstrate copying and applying the max feature, and discuss related concepts and documentation references for model optimization.

Apply standard scaler to features, transform training and test data with PCA, replace raw features with principal components, and train a classifier using a chosen explained variance like 95 percent.

Parse and filter data, scale features, and build a cluster-based workflow, collecting results in a list while evaluating the optimal k and incorporating a share column for insight.

Explore k-fold cross-validation as a robust technique for estimations in classification problems, improving model choices through iterative evaluation used in competitions.

Explore association rules by mining frequent item sets from transactions, measuring item frequencies and outcomes, and deriving rules with support and confidence to reveal purchase patterns.

Learn k means clustering by computing distances between observations to assign them to clusters and refine the grouping based on observed distances.

Master the basics of naive bayes in data science with Python and TensorFlow. Apply core concepts, data ranges, and training steps to build intuition for classification models.

Explore Naive Bayes part 2 by applying probabilistic classification to everyday data. The lecture emphasizes evaluating outcomes and interpreting context in data-driven decisions.

Learn how to perform association using Python, examine dataset columns, discuss the paper focus, and consider the risk impact of 75 percent through a practical implementation.

Learn how to use decision trees for classification and apply information concepts to construct a decision tree from a data set.

Explore decision tree concepts in this part 2 lecture, including practical data handling with tables, identifying information, copying a cell, changing values, and interpreting probabilities and noise to shape features.

Explore how to use features for classification with a decision tree, including selecting features and interpreting results on a movie dataset to assess risk and classification outcomes.

Learn how regular expressions power text analytics by extracting words and spaces, defining search patterns, and matching text with character ranges.

Explore regex basics for text analytics, including anchors, match vs search, and capturing groups to extract patterns such as phone numbers from text.

Explore how regex patterns match at the beginning of a string versus elsewhere, and compare match, search, and find to extract company information for text analytics.

Learn advanced text analytics with regex part 4, exploring digit patterns, hyphen handling, and flexible matching using dot, plus quantifiers, and combining search and match techniques.

Delve into text analytics with NLTK, part 1, practicing tokenization, sentence segmentation, and converting text into numeric representations for machine learning models.

Explore text analytics with NLTK part 2, focusing on classification techniques and practical code insights that show how simple lines of Python can demonstrate model effectiveness on text data.

Explore text analytics with nltk, applying tokenization and part-of-speech tagging guided by a corpus to handle word forms and limitations.

Explore regular expressions and build a text classification model to distinguish spam from not-spam messages.

Apply tokenization to the data column, create a new column with the tokenized results, and use a right-shift operation to explore the effects on topics.

Explore text analytics classification by converting text into numbers, implementing approaches with user-defined functions, and examining imperfect methods and topics in part 4.

Analyze how text analytics classification uses feature extraction to compare documents, illustrate feature construction, and discuss how features like teacher names influence classification outcomes.

develop text analytics classification skills by working through six documents, documenting notes, and navigating document-based interactions demonstrated in part 6 of the course.

Explore text analytics classification and implement CFIT in common using brute force as well as the FAA, with practical examples and preprocessing insights for data science with Python and TensorFlow.

explain how input values make a line shift downward toward zero, illustrating bias in deep learning.

Discover how bias and activation function map inputs through a perceptron as a binary classifier with a threshold, and optimize by minimizing misclassification via objective functions and gradient methods.

Explore the basics of deep learning, including activation functions, bias, and regulation function, with practical lab examples and a focus on learning flow and concept implementation.

Learn the basics of deep learning with TensorFlow through practical lab-style examples, exploring mathematical operations and the concept of competition as presented in the lecture.

Explore cost concepts and flow in deep learning, define a cost value, work with sessions, and evaluate how cost informs TensorFlow lab practice.

Define placeholders and a simple mathematical operation to build a computation graph, then explore values and batch sizes to observe how instances influence training in TensorFlow.

Explore how to initialize variables and constants, using an initial value, naming options, and a global variable initializer to parse to a session.

Learn the basics of linear models in TensorFlow, focusing on the role of the input X and bias b in shaping predictions.

Define the neural network model and explore the loss function in this deeplearning tensorflow lab part 4, preparing you to understand core concepts in TensorFlow.

The lecture explains setting up the optimizer with a 0.01 learning rate to minimize the loss. It covers initialization, construction, and execution phases within a TensorFlow deep learning workflow.

Explore a simple deep learning model in a TensorFlow lab, tune the optimizer, and inspect weights and biases to solidify understanding of x and y data relationships.

The lecture explains issues with gradient-based methods in deep learning, including the exploding gradient problem, and discusses simple solutions to stabilize training updates.

Delve into the basics of deep learning MLPs, outline the construction and execution workflow, and cover loss functions, batching, and how to measure the model.

Explore deep learning in ANN lab part 2 by training image recognition on x-ray data, extracting features, and refining observation based training steps.

Define inputs and labels for a 10-class digit recognition task, adjust neuron counts, and structure features and instances across dimensions in the deeplearning annlab part 3.

Discover core neural network concepts, including neurons per layer, feature dimensions, and how instances populate the model. Learn about two important concepts to simplify complex networks and interpret them clearly.

Learn to construct a neural network model by defining layers via function calls, choosing an activation function and initializer, and configuring dense layers with input, class count, and skip connections.

Explain how a multi-class classifier uses a cost function to evaluate predictions, select the class with the highest probability, and compare correct and incorrect classifications while examining loss across observations.

Train a model using softmax cross-entropy loss and a gradient descent optimizer to minimize loss, then evaluate accuracy by comparing predictions to targets.

Discuss why it's too late to initialize all variables and save to the hard disk for later use. Prepare to begin the execution phase.

begin by clarifying names mentioned in the lecture and prepare to apply the exclusion rule in the next part of the ANN lab.

Explore building and training a neural network with batch processing, and evaluate accuracy and loss on test data to interpret results and compare performance across batches.

Explore deep learning training in the ann lab, examining convergence, batch size, and how accuracy may increase, while stopping training when accuracy stops changing for a specified number of iterations.

Explore how to use TensorBoard to visualize training runs, inspect histograms, monitor different name scopes, and manage checkpoints for efficient deep learning with TensorFlow.

Discover the basics of convolutional neural networks, how they process images, and why CNNs are essential for image recognition tasks.

Explore CNN pooling and padding concepts and their impact on image size and information flow in neural networks, within the complete data science with Python and TensorFlow course.

Explore cnn lab 1 within the complete data science with python and tensorflow course, focusing on practical lab exercises.

Explore cnn lab 3 concepts within complete data science with python and tensorflow, applying practical neural network techniques in a lab setting.

Explore CNN lab 4 in complete data science with Python and TensorFlow, highlighting practical lab work and decisions about data formats and reference materials.

Discover the basics of recurrent neural networks and memory in this introduction to Rnn, using sentences and everyday scenarios as examples.

Explore an rnn lab that treats images as sequence data, select the number of recurrent neurons, and learn how to structure inputs and states for sequential modeling.