Mastering Artificial Intelligence (AI) with Python and R

Develop intermediate ai skills by hands-on python and jupyter notebook projects, covering machine learning concepts, deep learning basics, and essential python packages like seaborn.

Explore the machine learning life cycle and key Python packages for ML, then apply deep learning concepts through practical projects, coding challenges, and end-to-end projects.

Explore supervised and unsupervised learning, including regression, classification, and clustering, with practical examples and a hands-on life cycle from data preprocessing to model retraining.

Explore data pre-processing and exploratory data analysis to identify the problem type (regression, classification, or clustering) and prepare data through labeling, encoding, and missing-value imputation.

Explore bias, variance, and their trade-off; learn how training-test splits affect model performance, and how to avoid overfitting and underfitting.

Examine data preprocessing, statistical analysis, and visualizations to assess input patterns and correlations, then select models using accuracy, precision, recall, and F1, informed by feature importance.

Choose model strategies by business context and data type, balancing metrics like accuracy, precision, recall, and F1. Learn model evaluation across iterations and interpret confusion matrices for actionable insights.

Learn scikit learn basics for regression, data pre-processing, and model evaluation, then monitor monthly performance and retrain to address underfitting or overfitting.

Load the housing data with pandas read_csv, handle missing values by imputing the mean for total bedrooms, and encode the ocean proximity. Scale the data to standardize for regression readiness.

Check visualizations with heat maps and box plots to assess distributions and outliers. Build and evaluate a baseline linear regression model using mean squared error and R-squared.

Explore predicting with a random forest regressor, evaluating with mean squared error and R^2, and visualizing predicted versus actual values; then classify iris data with KNN and logistic regression.

Load iris.csv, inspect its shape and columns, visualize distributions with seaborn, and note PCA and LDA as dimensionality reduction techniques.

Apply principal component analysis and linear discriminant analysis to reduce dimensions of iris data, extracting two components and visualizing PCA and LDA results.

Explore model selection for iris classification by preparing data, dropping the id column, encoding the species with label encoding, and performing a stratified train-test split.

Explore train-test split, build and evaluate a k nearest neighbor classifier with scikit-learn, using confusion matrices and accuracy scores for train and test sets.

Analyze classifier accuracy by comparing train and test scores, visually plotting accuracy and highlighting high performance. Explore boosting and bagging techniques, including XGBoost, and discuss deployment and monitoring considerations.

Explore clustering in unsupervised learning with k-means, using the elbow method to determine cluster count within unlabeled data, identify outliers, and apply these techniques to a live data csv.

Assess dataset statistics with describe to view quartiles, mean, std, and count; examine status id and status published for uniqueness, then prepare for unsupervised learning via clustering with label encoding.

Use a label encoder to convert the status type to categorical labels and apply the min max scaler, preparing data for a k means clustering model and evaluating inertia.

Assess labeling accuracy by computing the accuracy score on the labeled data, then apply the elbow method to determine the optimal number of clusters for k-means.

Learn how a multilayer perceptron uses multiple hidden layers and forward propagation to model complex decisions, and how a cost function, gradient descent, and learning rate guide training.

Explore how a multilayer perceptron learns: tune learning rate, perform forward and back propagation, and train with batches and epochs, using dropout and batch normalization to improve generalization.

Explore selecting the number of clusters in k-means using inertia and elbow analysis, compare accuracy across cluster counts, and relate clustering to the machine learning life cycle.

Explore Keras, PyTorch, and TensorFlow Lite, and Theano as deep learning frameworks, including their focuses and use cases. Preview setup steps using Anaconda, conda environments, and Jupyter Notebook for TensorFlow.

Learn to set up and run TensorFlow in Jupyter, build a tf.keras sequential model, compile with loss and metrics, and apply the deep learning life cycle—from define to predict.

Learn to build a binary classification model with the TensorFlow functional API, including data loading, label encoding, train-test split, dense layers, training, evaluation, and prediction.

Explore heat maps, box plots, and pear plots; define x and y data, perform train-test splits, and compare linear regression with random forest using mean squared error and r-squared.

Learn to load MNIST data in python, inspect train and test shapes, and visualize digits with matplotlib pyplot in a 5x5 grayscale subplot, prepping for deep learning image processing tasks.

Explore ensemble learning and decision trees, build a classifier in Python, visualize the results, and evaluate performance with classification reports from training and testing data.

Explore random forest and extremely random forest as ensemble learning methods built from decision trees with random data subsets, and learn their boundaries, classification report, and confidence measures.

Learn to address class imbalance in predictive analytics with Python by balancing data using class weights in an extremely random forest classifier, improving model bias and classifier performance.

Explore how grid search cv finds optimal classifier parameters by testing a parameter grid with cross-validation, comparing precision and recall to select the best settings.

Explore AdaBoost regressor, its adaptive boosting that emphasizes misclassified data, and how it computes feature importance with a decision tree base learner and mean squared error and explained variance.

Predict traffic counts using an extremely random forest regressor on a real-world dataset from Los Angeles Dodgers Stadium, highlighting preprocessing and predicting vehicle counts from day, time, and game indicators.

This lecture demonstrates predicting traffic using an extremely random forest regressor with label encoding, evaluating with mean absolute error, and predicting vehicles for a specific date, time, and location.

Explore unsupervised learning to detect patterns in unlabeled data by clustering with k-means, using similarity metrics to form subgroups and iteratively update centroids.

Learn to implement k-means clustering in Python, visualize five clusters, initialize centroids with k-means++, and plot decision boundaries and centroids.

Learn how the meanshift clustering algorithm, a non-parametric method, identifies cluster peaks by shifting centroids within windows until convergence, with applications in object tracking and real-time data analysis, using Python.

Load 2d data from a csv, import numpy, matplotlib, sklearn, and apply meanshift clustering with bandwidth estimated by quantile for kernel density estimation.

Learn affinity propagation, a clustering algorithm that does not require pre-set cluster counts, using message passing to identify exemplars through responsibility and availability. Apply to stock market data with Python.

Use silhouette scores to evaluate clustering quality and identify the optimal number of clusters with a Python-based visualization.

This lecture demonstrates using Python with NumPy, Matplotlib, and scikit-learn to load two-dimensional data, run k-means with varying cluster counts, compute silhouette scores, and identify the optimal clusters.

Learn gaussian mixture models, a weighted sum of gaussian components that model multi-modal data. Apply to image database retrieval, stock market fluctuation, biometric verification, with Python examples.

Build and visualize a Gaussian mixture model using the iris data with an 80/20 split, training a GMM classifier and plotting ellipses for three distributions.

Explore classification in artificial intelligence by building a classification model from a labeled training data set of points with labels, distinguishing data into classes such as face and no face.

Master data preparation by pre-processing input data for AI training, including binarization, mean removal, scaling, and normalization with scikit-learn tools like Binarizer and MinMaxScaler, covering L1 and L2 norms.

Learn to use the logistic regression classifier to estimate class probabilities with a sigmoid function, linking independent variables to dependent variables for classification, and implement a Python classifier with Tkinter.

Build a logistic regression classifier in Python, train with fit, and visualize decision boundaries using a mesh grid and scatter plots, comparing C values 1 and 100 with liblinear.

Explore the Naive Bayes classifier built on Bayes theorem with an independence assumption, implement Gaussian NB in Python, and evaluate with cross-validation and metrics like accuracy, precision, recall, and F1.

Explore how a confusion matrix evaluates classifier performance, distinguishing true positives, true negatives, false positives, and false negatives in binary classification, with a Python example.

Create and visualize a confusion matrix in Python using NumPy, Matplotlib, and scikit-learn, compare true and predicted labels, and generate a classification report with precision, recall, and F1 scores.

Learn how support vector machines classify data by an optimal hyperplane with maximum margin, using support vectors, and apply Python examples to predict income brackets from 14 attributes.

Demonstrates building an svm classifier in python with numpy, sklearn, and preprocessing. Uses one-vs-one with linear kernel, cross validation, encodes mixed data, trains with 80/20 split, and evaluates F1 score.

Explore logic programming as a paradigm, learning about facts, rules, unification, and solving problems with computation and deduction, with Python examples.

Learn to match mathematical expressions using logic programming in python, with logpy or kanren. The lecture demonstrates defining operations, variables, and comparing expressions to reveal a, b, and c values.

Parse a family tree with logic programming in Python using Kanren and a JSON data set. Define parent, grandparent, sibling, and uncle relations and query children, grandchildren, and spouses.

Analyze geography with logic programming by defining facts and rules about US states and querying adjacent and coastal relations.

build a puzzle solver with logic programming in python by defining facts and rules about people, pets, colors, and countries to determine who owns the rabbit.

Learn heuristic search for solving problems by eliminating obviously wrong options to speed up solutions. Compare uninformed and informed search, with depth-first, breadth-first, and uniform-cost techniques.

Explore local search techniques for constraint satisfaction problems, updating variables iteratively toward the goal with heuristic functions; learn hill climbing, simulated annealing, and annealing schedule to avoid local maxima.

Learn to build an eight puzzle solver with the a-star algorithm, using a heuristic search cost function and Manhattan distance to select the most promising path toward the goal.

Solve the region coloring problem with heuristic search in a constraint satisfaction framework (CSP) using Python, coloring regions with four colors so adjacent regions differ.

Solve mazes with the a* heuristic search in Python using SimpleAI to handle a maze of obstacles, start O and goal X, and find the shortest path.

build an eight puzzle solver with a-star heuristic search in python, combining dijkstra and greedy best-first search to find the optimal path using Manhattan distance.

Explore natural language processing as computers understand human language by recognizing data patterns, using Python tools like NLTK, NumPy, and collections, with practical installation and corpus examples.

Explore examining text with nltk using concordance to show word occurrences with context. See how similar shows words in context and common context reveals shared context between words in Python.

Access and tokenize raw text from local files or web sources using Python. Learn to use urllib, urlopen, read, and NLTK's word_tokenize to produce tokens.

Explore the NLP pipeline from text extraction to vocabulary building using Python and NLTK. Learn tokenization, lowercasing, de-duplication, and sorting to create a vocabulary from URL or file text.

Learn to use regular expressions with NLTK to detect patterns in text, via the corpus collection and analytic module, and preprocess with re for pattern matching.

Explore how stemming reduces inflected words to base forms using NLTK, and compare Porter and Lancaster stemmers through Python tokenization and stem application.

Explore lemmatization, grouping inflected forms into a lemma and distinguishing it from stemming by context; learn with the NLTK WordNet lemmatizer, tokens, and practical examples.

Explore segmentation in natural language processing, contrasting sentence segmentation and word segmentation, and learn how tokenization uses a segmenter such as a Python-based sentence segmenter, with challenges from abbreviations.

Explore practical sentence segmentation using inbuilt data and a custom segmenter, loading files and applying tokenizers in nltk. Learn about corpus and word segmentation to extract sentences from text.

Learn to implement a word segmentation function with two segmentation types, apply slicing, and compare segment one and segment two for text tokenization in continuous streams.

Explore information extraction in natural language processing, converting unstructured text to structured data, and applying steps—sentence segmentation, tokenization, pos tagging, entity and relation recognition—using Python and NLTK.

Describe tag patterns as rules for chunk grammar using angle brackets and part-of-speech tags to define optional, zero-or-more, and one-or-more sequences for parsing sentences.

Explore chunking for information extraction and entity recognition, labeling tokens with parts of speech to identify noun phrase chunks via a non-overlapping grammar-driven parser producing a tree.

Learn iob text tagging for chunks with i, o, b labels and chunk types, and compare with tree representations where s marks a sentence and np denotes noun phrases.

Explore chunking and chinking in NLP with NLTK, building noun phrase chunks while excluding sequences like VBD and IN, and visualizing the resulting parse trees.

Apply chunking with regular expressions using NLTK's regex parser to build chunk structures from grammar rules, observe leftmost precedence for overlapping matches, and generate noun phrase chunks.

Explore named entity recognition, known as entity chunking and extraction, as a subtask of information extraction that locates boundaries and identifies types such as persons, organizations, locations, times, and quantities.

Explore how trees represent sentence structure in natural language processing, using labeled nodes, a root, and phrases like noun phrases and verb phrases, with Python and nltk examples.

Explore context free grammar in natural language processing by defining productions in NLTK grammar module, using S as start symbol, root label, tokenizing and parsing with a recursive descent parser.

Explore recursive descent parsing of context-free grammars, build a parse tree from the start symbol, and apply a top-down parser with grammar predictions, demonstrated with a Python/NLTK example.

Explore recursive descent parsing with NLTK by parsing 'the dog saw a man in the park,' illustrating noun phrase and verb phrase structures, backtracking, and final parse trees.

Discover shift reduce parsing, a bottom-up method using a stack to apply shift and reduce against a context-free grammar to reach the start symbol, with no backtracking and one parse.

This lecture presents an introduction to artificial intelligence, outlines the course topics on search, machine learning, and reasoning, and surveys theories of intelligence and factors affecting it, including IQ testing.

Define artificial intelligence as the science and engineering of making intelligent machines and intelligent agents; the Turing test guides objective evaluation and modern AI emphasizes practical problem solving.

Explore how intelligent agents perceive environments, map the percept sequence to actions, and design rational agents considering environment properties.

Explore intelligent agents that perceive environments through sensors, act via actuators, and use heuristic search, learning, planning, and knowledge representation to solve problems.

Explore how Euler's bridges of Königsberg inspired graph theory for state space search, modeling cities as nodes and bridges as edges with odd-degree constraints.

Explore state space search by modeling problems as a four-tuple with nodes, arcs, a start state, and a goal state, using eight puzzle and traveling salesman examples.

Explore state space search for the traveling salesman problem by evaluating path costs on a graph, comparing full enumeration with the nearest neighbor heuristic, and noting its drawbacks.

Explore finite state machines with states, inputs, and the state transition function, and visualize a flip-flop via a transition matrix. Compare data driven and goal driven searches.

Discover how bfs on a graph uses an open and a close list to explore level by level from a starting node toward a goal state, enqueuing each node's children.

Trace the depth-first search algorithm on a graph, using a last-in, first-out open stack and a closed list, where DFS adds children to the left and checks for the goal.

Trace depth first search with iterative deepening, illustrating open and closed lists, path reconstruction, and how depth bounds yield a complete search that can find the shortest path.

Mastering backtracking algorithms, this lecture explains systematic path exploration in a state-space graph, starting from the start state, managing dead ends, and backtracking to find the goal.

Trace the backtracking algorithm on a graph using the given pseudo code; start at a, generate b, c, d, then explore b, e, f, and backtrack on dead ends.

Illustrates backtracking on a graph by managing the state list, the next-state list, and the dead end, while generating children and tracing the path to the goal gca.

Explains state-space search using backtracking, depth-first and breadth-first search, detailing nodes, states, and the role of the state list and NSL in iterative search.

Explore heuristic search for problem solving, including defining heuristics, their role in guiding state-space search, and methods like best first search and pruning to avoid combinatorial explosion.

Apply a heuristic calculation technique to tic tac toe by counting possible wins from x's position, then select the highest heuristic value to guide next moves using f(n)=g(n)+h(n).

This lecture explains heuristic calculations for the eight puzzle, using tiles out of place, sum of distances, and direct tile reversals to compute f(n)=g(n)+h(n) across moves.

Explore simple hill climbing, a heuristic search that expands the current state, evaluates its children, and selects the best for expansion, acknowledging local maxima and comparing with best first search.

Explore best first search, using an open and close list with a heuristic function f(n)=h(n) to greedily reach the goal via a priority queue, maintain ancestor data and manage duplicates.

Trace the best first search on a graph, updating open and closed lists, generating children, and tracing the path from start node A5 to goal node P using heuristic values.

Apply best first search to the eight puzzle by maintaining open and closed sets, expanding the lowest f(n)=g(n)+h(n) state and generating children until the goal state is reached.

apply best-first search with f(n)=g(n)+h(n) to the 8-puzzle, computing g and h (tiles out of place) and selecting lowest f for expansion; discuss admissibility, optimality, and performance.

Explore state space search using breadth-first search and heuristic functions, including monotonic, admissible, and more informed heuristics, and apply minimax with fixed ply lookahead to game graphs.

Apply a heuristic conflict measure to tic-tac-toe states, computing e(n) = m(n) − o(n) for my and opponent's possible winning lines, using two-ply minimax on opening moves.

Learn how alpha beta pruning enhances the min max search by using a depth-first approach with alpha and beta values to prune branches in two-person games, reducing the search space.

Explore the foundations of machine learning within artificial intelligence, including non-symbolic learning, neural networks, gradient descent, and the McCulloch-Pitts neuron models for and/or logic.

Learn the perceptron learning algorithm for a single-layer network with inputs, weights, and a threshold; update weights via the learning rate and error (t-y), noting non-linear separability limits.

Explain how perceptrons linearly separate data and why xor is not linearly separable, requiring multi-layer networks; introduce sigmoid activation and lambda's effect.

Explore backpropagation in multilayer networks, propagating output errors backward to adjust weights via the delta rule, gradient descent, mean squared error, and the logistic activation function.

Compute hidden node delta w and propagate errors through layers using backpropagation, applying sigmoid activations and iterative weight and bias updates with a learning rate.

Apply backpropagation by initializing weights, forward-propagating inputs, computing and propagating errors for output and hidden units, and updating weights and biases with a learning rate until a threshold is met.

Compute backpropagation values for nodes i5 and i6 using weighted prior activations and theta terms, and derive hidden-layer outputs like o4 = 0.332, i5 = 0.1, i6 = -0.105.

Compute input values i4, i5, i6 and outputs o4, o5, o6, then calculate the output error using o6(1-o6)(t6-o6) and propagate to hidden nodes 5 and 4.

Update weights and theta using learning rate and error signals to adjust neural network parameters, then explore clustering with K-means and unsupervised learning, minimizing intra-cluster distance and maximizing inter-cluster distance.

Explore k-means clustering, centroid initialization, and iterative reassignment, contrasting unsupervised with supervised learning, and see clustering applications in biology, business, and medicine.

Explore logic and reasoning, including reasoning, logical inference, and logic languages, with propositional calculus and predicate calculus, and see how the knowledge base and inference engine derive conclusions.

Explore propositional calculus concepts, including atomic symbols p, q, r, and truth values true and false, and learn how negation, conjunction, disjunction, implication, and equivalence form sentences.

Explore converting English statements into propositional calculus, map sentences to propositions, visualize implications as graphs, and apply laws like modus ponens, contrapositive, and de Morgan to prove implications.

Explore the predicate calculus, a more flexible logic with predicate variables and quantifiers, and its symbols, constants, variables, functions, and arity, and how sentences are formed.

This lecture introduces the first-order predicate calculus, explains quantified variables and negation, and demonstrates translating English sentences into predicate logic with examples.

Represent family relationships with predicate calculus, and apply concepts like satisfiability, models, validity, and inference rules such as modus ponens, tollens, elimination, introduction, and universal instantiation.

Build a reasoning system with an inference engine and knowledge base, master unification of predicate expressions, Skolemization, and resolution-based deduction in logic.

Apply resolution refutation to prove or disprove propositions by converting statements to disjunctive clause form, negating the goal, and deriving the empty clause.

Convert English statements to predicate and clause forms, apply negation, use resolution refutation, and show variable cancellation to determine if John is happy.

Explore resolution refutation by translating English statements into predicate and clause forms, deriving a contradiction to determine if anyone can have an exciting life.

Convert statements to predicate and clause formats, then perform a resolution proof. Use unification and substitution to derive the null clause and conclude an exciting life (John is exciting).

Explore unification substitution and skolemization via resolution refutation, extracting answers from clause-formatted statements, negation, and substitution, illustrated with Fido at library.

Illustrate Prolog as a logic programming language, its syntax, and converting predicate calculus to Prolog. Explore horn clauses, resolution, and non-monotonic reasoning with abductive reasoning.

Explore model based reasoning using theoretical device models and simulations to predict behavior and diagnose faults, then apply case based reasoning by retrieving past cases.

Explore rule based programming and production systems as models of human problem solving. Learn how facts, short term memory, long term memory, and rule firing drive inference and action.

Trace a simple production system by substituting left-hand side patterns with right-hand side patterns, using ba→ab, ca→ac, and cb→bc to reach memory a a b c c.

Explore the knight's tour on a 3x3 chessboard by mapping moves to production rules of conditions and actions, then trace an iteration from 1 to 2 using highest-priority choices.

Explore rule matching and conflict resolution in production rules, including refraction, recency, and specificity, with data-driven and goal-driven chaining in clips and working memory.

Demonstrates a goal driven data driven production system that uses working memory and rule firing to diagnose engine problems, iterating through questions about gas, spark plugs, and battery.

Contrast data driven and goal driven approaches, examining branching factors that shape search space and complexity, then explore production-system concepts and clips rule based programming with facts, rules, and inference.

Learn to build rule-based systems with clips by defining facts, watching and resetting them, and creating diff rules that map patterns to actions, with testing and loading capabilities.

Install clips 6.3 on windows or linux, launch the clips interface, and learn to use exit, clear, reset, run, and manage facts and rules.

Open CLIPS and learn to manage a fact database by asserting facts such as color green, listing them with the facts command, and retracting or clearing as needed.

Explore asserting facts in CLIPS, noting case and space sensitivity, and learn to manage facts with retract, watch facts, and memory indicators.

Learn how diff rules express if-then logic in clips, translating natural language into executable actions with facts and left-hand side and right-hand side concepts, via a duck quack rule.

Use deferral and if-then rules to infer new facts in a rule-based system, such as asserting 'animal is duck' when a duck's webbed feet and feathers are present. Practice building diff rules with two patterns, handling syntax errors, and using reset to clear facts.

Learn to use clips to run and assert facts like animal is duck and sound is quack, using forward chaining. Load .clp files to reuse rules.

Save and load a CLP file by asserting facts like animal is duck and sound is quack, then run batch loading and view results in the facts window.

Examine using defects to preload facts and the reset mechanism to reintroduce them. Apply wildcards and variables in rules to match animal facts and print 'animal found' for each match.

Explore how a rule-based AI uses multiple patterns, facts, and assertion rules to infer mammals, using warm-blooded and egg-laying patterns with diff rule.

Demonstrate diff-based pattern matching to infer mammals from animal facts, identify warm-blooded animals that do not lay eggs like dog, and assert and print mammal facts such as mammal dog.

Learn to retract facts that start with mammal in a CLIPS-style rule system using diff rules, and apply logic and maths operators to convert into CLIPS format and evaluate expressions.

Learn advanced wildcard pattern matching in CLIPS, using a single question mark and multi-field dollar to extract band names and members from facts, then print results via a DF rule.

Learn how to perform wildcard matching with multi-field wildcard entries, using dollar and dollar question mark wildcards to print band members and their bands from a fact database.

Explore variables in Clips using the bind function to create temporary right-hand side variables and add up numbers with the diff rule.

Learn to use the def template to populate slots with personal data, apply the assert statement, and link multiple people with def templates, handling multi-slot values like blood pressure ranges.

Explore how deffacts and deftemplates structure personal data facts with named slots, modify facts to update age and details, and use truth maintenance through retraction and birthday rules.

Apply conditional elements in rule-based logic to test facts like weight thresholds, define rules with def rule, use and/or operators, and print matching personal data such as name and weight.

Explore the not operator in the CLIPS programming language, using conditional elements to negate a predicate with personal data fields like name and weight and birthday checks.

Use the test element to evaluate conditions on the left-hand side of a rule, and apply exists and for all to print matching names from the personal data.

Learn how to apply exist and for all rules in a fact database, validate at least one person and check every person has a name, with debugging tips for syntax.

Learn to build a deftemplate with multiple slots for personal data, assert facts with deffacts, manage date of birth fields, and apply logical conditional elements for truth maintenance.

Design and debug diff rules and def templates within a personal data and cardiac risk scenario, using trial-and-error learning to assert facts into the fact database.

Explore how intelligent agents operate in simple and complex environments by mapping percept sequences to actions via the agent function and program, using sensors, actuators, and internal state.

Explore the simple reflex agent, connecting current perceptions to actions via condition-action rules in an environment. Note its limitations in partial observability and how randomization can help avoid infinite loops.

Learn how a model-based simple reflex agent uses internal state to handle partial observability, updating state from percepts and rules to choose actions.

Explore goal based agents that use explicit goals and reasoning to select actions, contrasting with model based reflex agents, and highlighting search and planning to achieve goal-driven behavior.

Explore how a utility based agent extends goal based reasoning by maximizing expected utility under uncertainty. Balance trade-offs like speed and safety using decision networks and Bayesian reasoning.

Explore basics of utility theory, including utility functions, lotteries, and the six axioms governing rational decisions under uncertainty.

Compute maximum expected utility by evaluating actions across states with probabilities and utilities, and explore utility theory, preference axioms, and the value of information.

Explore decision theory by combining probability and utility to form a rational agent, using decision networks with chance, decision, and utility nodes, illustrated by airport site cases.

Explore reinforcement learning where agents learn from rewards to maximize outcomes in uncertain environments, using Markov decision processes, partially observable MDPs, and dynamic decision networks.

Explore Markov decision processes, dynamic decision networks, and partially observable MDPs to understand policies, value iteration, policy iteration, and linear programming value iteration, game theory, and mechanism design under uncertainty.

Explore how artificial intelligence handles uncertainty through inductive, probabilistic, and fuzzy reasoning. Understand the shift from strict logic to probabilistic and qualitative approaches, including rule-based and default reasoning.

Explore set theory basics, including union, intersection, complements, and Cartesian products, then apply the multiplication principle and counting rules (factorial, permutation, combination) to compute probabilities.

Explore random variables across discrete, continuous, and boolean types, learn probability distributions including binomial, Poisson, and normal, and apply joint, independent, and conditional probability concepts.

Explore Bayesian rule for conditional probability through examples of sequential draws without replacement, joint and conditional probabilities, random variables, and joint distributions.

Apply Bayes theorem to real scenarios by calculating P(D2|A1) from dealer priors and model purchase probabilities, and assess gender inference using conditional probabilities.

Explore how data from environments and sensors drives machine learning, turning raw data into intelligent action through data input, abstraction, and generalization, while considering ethics and privacy.

Discover how abstraction and knowledge representation shape data models, training them to generalize. Assess learning with bias checks, overfitting and underfitting, and evaluation metrics like OLS and ROC.

Collect, preprocess, and explore data to support effective machine learning outcomes; apply missing value imputation, outlier detection, and exploratory data analysis, then train and evaluate models.

Explore supervised learning by contrasting regression and classification. Learn regression types such as linear and non-linear, and classification algorithms like decision trees and k nearest neighbor, plus unsupervised clustering.

Explore basic data manipulation in R, a free open-source tool for statistics, graphics, and statistical programming, built on S with lexical scoping and data frame handling.

Navigate RStudio's interface, manage history and environment, and use preloaded data sets, packages, and help to perform data manipulation in R.

Learn basic data manipulation in R, explore R's capabilities for statistics and graphics, and compare RStudio and R console while executing simple commands.

Create vectors in R with the c constructor, compare vectors and scalars, and compute mean, median, standard deviation, variance, correlation, and covariance, using air quality data and na.rm.

Explore vector operations in R, including handling missing values with na.rm, computing mean and sd, creating sequences with seq and rep, and performing elementwise comparisons, indexing, and logical selection.

Master conditional selection of vector elements and name assignment; explore vector arithmetic with recycling rules and understand operator precedence, including double colon and dollar-dollar access.

Master R operators from exponentiation to modulo, assignment and logical and bitwise operators, then define functions, explore gcd and coefficient of variance, return value, and common invocation mistakes.

Identify and avoid common R mistakes—escaping backslashes, confusing assignment with comparison, and improper vector recycling—while mastering list access, data frame handling, and correct use of mean, max, and min.

Master simple linear regression with ordinary least square method to estimate the intercept and slope, and interpret r squared, t statistics, p values, and residuals for predictive modeling.

Fit a simple linear regression in R using x and y with lm, plot the data, and interpret intercept 2.2 and slope 0.6 from the model.

Explain how to compute r-square and adjusted r-square in a simple regression using x and y values, and demonstrate estimating y, calculating standard error, and interpreting model fit.

Explains how to compute standard error and residuals in linear regression, including residual sum of squares, residual standard error, coefficient estimation, and the f-statistic for testing coefficients.

Compute restricted sum of squares with x=0, estimate y as 2.2, and evaluate residuals. Use F-statistic 4.5 against critical 10.128 with df1=1, df2=3 to reject coefficients are zero.

Explore how t and f statistics assess a simple linear regression, including p value, residuals, r square, and interpreting model coefficients like 2.2 and 0.6.

Learn regression basics and types, perform simple and multiple linear regression in R, and compute intercept, slope, r-squared, standard error, t statistics, p values, F statistics, and residuals.

Learn a simple linear regression in R using x and y, fit an ordinary least squares model with lm, and read the coefficients and model summary.

Explore how to compute r square and standard error in a simple regression with x and y, using mean, estimated y, and adjusted r square to assess model fit.

Explore how standard error reflects the difference between actual and estimated y in linear regression, compute residuals and residual standard error, and test coefficients with the f-statistic in Excel.

Evaluate a simple linear regression model by computing f-statistics, t-statistics, p-values, and residuals, and interpret coefficients 2.2 and 0.6 to test if predictors matter.

Learn variance, covariance, and correlation with their formulas, explore how they describe relationships between variables, assess linear regression, multicollinearity, and hypothesis testing with p values.

Analyze covariance and correlation between X and Y, build a simple linear regression model, and test correlations with p-values; examine discrete and continuous distributions and related R and Python functions.

Learn to generate random numbers and explore combinations in R, using set.seed for reproducible results, specify ranges and samples, apply rnorm for normal distributions, and floor rounding.

Explore normal distribution concepts, including mean and standard deviation, and compute binomial probabilities with R functions dbinom and pbinom, illustrated by practical widget examples.

Explore the Poisson distribution, its lambda parameter, and how events in a fixed time follow a Poisson model, with connections to normal approximations, quantiles, and practical computation in R.

Master t distribution and hypothesis testing by calculating the t statistic, comparing it to the critical value, and interpreting the null and alternative hypotheses with p-values and degrees of freedom.

Explore standard normal distribution concepts, central limit theorem, and six sigma using z-scores, p-values, and R's p.test to infer drug effects.

Learn to perform multiple linear regression in R with the lm function, visualize data, and select regression variables using the cars 93 dataset and associated preparation steps.

Demonstrate data visualization in R by exploring how horsepower, weight, and origin relate to city mpg using coplot and the pair command, and a linear model on the cars dataset.

Explore a multiple linear regression model, interpreting coefficients and p-values for city mpg, train with lm, and validate with R-squared while splitting data 70/30 and comparing models with AIC.

Learn stepwise linear regression with backward and forward selection using AIC, as the lm function iteratively adds or removes variables to reach the best model.

Explore regression variable selection using forward, backward, and both directions, build linear models with lm, compare models via AIC and RSS, and predict mileage per liter with real data.

Explore generalized linear models and generalized least squares as extensions of linear regression, handling non-normal errors and non-constant variance, demonstrated with the air quality data where ozone relates to wind.

Explore generalized least squares with air quality data to model ozone using wind and date, assess correlation and autocorrelation, and compare gls variants with predictions.

Learn kNN classification by labeling a new item using majority vote of its k nearest neighbors and distance measures such as Euclidean, Manhattan, Minkowski, Hamming, cosine, and Jaccard.

Learn to measure distances with Hamming distance, Manhattan distance, Euclidean distance, and cosine distance in R, then apply KNN steps on Iris, including normalization, train-test split, and evaluate misclassification rate.

Analyze iris data by comparing petal length and width for species, normalize all four features with a min–max function, and create train (100) and test (50) sets for knn.

Explore how k-nearest neighbors classifies iris data, evaluate with a confusion matrix and roc curve, and interpret metrics like accuracy, true/false positive rates, and model strengths.

Learn to construct and prune decision trees, classify or predict with leaves and branches, and use information gain, Gini index, chi-square, and reduction in variance for purer subnodes.

Compute the Gini index for features B, C, and D to assess impurity, then compare Gini gains to select the root feature and sort by y.

Explore pruning of decision trees using the Gini index to guide splits, with a practical R example on the car seats data yielding a high, medium, or low sales indicator.

Train a decision tree on car seat data, converting sales indicator to a factor and predicting sales category. Use k-fold cross-validation to prune and avoid overfitting, selecting size 21.

Prune the decision tree to reduce misclassifications and leaves, compare prune models against test data, and discuss post-pruning benefits for avoiding overfitting.

Explore random forest, an ensemble of many decision trees built by bootstrap sampling of data and random feature selection to improve accuracy, using voting for classification or averaging for regression.

Explore Ada boosting, an adaptive boosting method that updates weights on weak features and demonstrates ensemble learning with models like random forest of decision trees.

This lecture covers preparing a ctg csv dataset, converting nsp to a factor, and building a random forest with training and test splits to evaluate accuracy, sensitivity, and specificity, overfitting.

Tune a random forest by adjusting number of trees and mtry, reduce out-of-bag error, and improve test accuracy, while analyzing feature importance and tree-size distribution.

Learn to evaluate random forest performance by extracting variable importance from fitted models, interpreting Gini-based rankings, and understanding how removing features like class or ltv affects accuracy.

Learn how k-means clustering, an unsupervised algorithm, partitions data into k homogeneous groups using nearest centroids, updates centroids, and employs distance measures and the elbow method to select k.

Apply k-means clustering and the elbow method to select an optimal k, balancing within-group homogeneity and heterogeneity in a 30,000-record social network dataset, with suggested k around 3 or 4.

prepare a numeric dataset for kmeans by cleaning missing values, encoding gender as features, imputing age with graduation-year averages, and selecting 36 numeric interest features for clustering in R.

Normalize 36 word features with z-score scaling, apply k-means clustering on 30,000 records, use elbow method to choose five clusters, and examine centroids.

Apply k-means clustering to a 30,000-record data set to assign cluster labels, uncover dominant interests and demographics, and translate insights into targeted advertising strategies.

Apply aggregation functions to reveal cluster demographics, including mean age, gender patterns, and friends counts, using k-means with within, between, and total sum of squares and R square.

Learn how Naive Bayes classifier uses Bayes theorem to compute posterior probability, assuming class conditional independence, and apply it to spam filtering and text classification.

Explore how joint probability and the overlap of spam and Viagra usage illustrate dependent events. Use Bayes' theorem to compute posterior probability with prior, likelihood, and marginal likelihood.

Learn how to build Bayes theorem components from a frequency table, derive a likelihood table, and apply Naive Bayes to text classification, using spam vs ham and words like Viagra.

Explain how Naive Bayes classifies messages by multiplying conditional feature probabilities with the class prior, and how Laplace smoothing prevents zero likelihood from ruining the posterior.

Explore Naive Bayes with Laplace smoothing and discretization of numerical features into bins, then apply it to an SMS spam dataset distinguishing spam from ham in R.

Apply text mining in R to prepare SMS messages (spam/ham) for classification by reading CSV, cleaning text, building a corpus, and generating a document term matrix.

Create and analyze a term document matrix from SMS data, split into training and test sets, and visualize spam vs ham words with separate word clouds in R.

Create a sms dictionary from training data, filter the document term matrix to frequent terms, convert counts to yes/no, and apply naive Bayes with E1071 for spam detection.

Explore Naive Bayes classification for text in R, building an sms spam filter from training data, evaluating with cross tables, and improving accuracy with Laplace smoothing.

Explore support vector machines as a black box method that uses a maximum margin hyperplane to separate data for binary classification, with vectors, high-dimensional spaces, and practical applications.

Explore linearly separable data and how SVM finds maximum margin by optimizing the hyperplane between convex hulls, while handling non linearly separable data with slack variables and the C parameter.

Explore the kernel trick for support vector machines, transforming nonlinear data into higher dimensional space to achieve linear separation with kernels like polynomial and sigmoid.

Explore how the Gaussian RBF kernel drives SVM-based OCR for classifying 26 letters using 16 features. Train on 16k and test on 4k, noting trial-and-error kernel choices.

Explore how Gaussian RBF kernels improve OCR with SVM on a 16-feature letter dataset, comparing linear and RBF kernels, achieving 84% to 93% accuracy and tuning via E1071.

Explore support vector machines, tuning cost and gamma to maximize accuracy; select the best model from multiple SVM variants and predict labels, achieving around 97% accuracy on test data.

Study dimension reduction techniques by comparing feature selection and feature extraction. Explore methods such as wrapper, filter, embedded, and MRMR, plus PCA applications like data compression.

Explore principal component analysis and kernel approaches for dimensionality reduction, plus nonlinear and discriminant methods, and practical feature selection techniques with correlation analysis and random forest.

Load libraries and data, remove features with 50% missing and near-zero variance, select numeric variables, compute and plot correlations, and drop highly correlated features to reduce from 160 to 36.

Use random forest to rank features by mean decrease accuracy, select the top 20 predictors, and create a compact data set for training and testing to achieve dimension reduction.

Transform data with PCA as a statistical procedure based on orthogonal transformation that yields linearly uncorrelated principal components for dimension reduction. Cover eigenvalues and eigenvectors, variance and covariance in PCA.

Learn to perform PCA by mean-centering data, computing the covariance matrix, extracting eigenvalues and eigenvectors, and projecting data onto principal components for dimensionality reduction while preserving variance.

Explore PCA reducing two-dimensional data to one, noting information loss and compression trade-offs, and review eigenvalues, eigenvectors, and steps to compute principal components with a simple example.

Compute eigenvalues and eigenvectors of a 2x2 matrix by solving det(A - lambda I) = 0, yielding lambda -1 and 8, and an eigenvector for -1 such as (1,2).

Explore eigenvalues and eigenvectors by solving a matrix's characteristic equations, compute with R's eigen function, and visualize how eigenvectors form the principal component analysis basis under transformation.

Explore principal component analysis with the prcomp function using the iris data set to demonstrate dimension reduction, scaling, variance, rotations, and scree plots.

Learn how principal component analysis identifies pc1 and pc2 with maximum variance, examines loadings and rotation, and uses biplots to interpret transformed data for predictive analytics.

Explore using principal component analysis on the iris data to predict species with a rpart decision tree, training on 100 samples and testing on 50 using pc1 and pc2.

Develop an R type model for iris data, apply standardization and PCA, then use the predict function to classify test cases into setosa, versicolor, or virginica.

Explore how neural networks, as a black box method, use artificial neurons to map input signals to outputs across diverse applications.

Explains how artificial neurons use weighted inputs, bias, and an activation function to produce outputs, and contrasts threshold, sigmoid, linear, tanh, and radial basis function with standardization and normalization.

Explore neural network topology, including input, hidden, and output layers, and learn how feedforward, recurrent, and backpropagation enable deep learning and function approximation.

Explore how neural networks adjust weights and biases through case and batch updates with backpropagation, learning rate, and stopping criteria to prevent overfitting.

Build an artificial neural network in R using a UCLA admission dataset, normalize GRE, GPA, and rank to 0-1, and split into training and testing sets.

Explore building a neural network in R using the neural net package, including setting seeds, defining inputs, weights, bias, sigmoid output, and predicting admission probability.

See how input nodes feed values, weights and biases create a probabilistic output via sigmoid across layers, with iterative learning rate updates to minimize error.

Demonstrates building and evaluating a neural network in R with multiple hidden layers, plotting models, tuning with life sign and repeat parameters, and comparing training and testing errors.

Explore time series analysis and forecasting theory, focusing on autoregressive models and econometrics approaches like ARIMA, and distinguish univariate time series from cross-section data.

Explore common time series patterns, from upward trends and sine waves to random series, and identify the four components—trend, seasonality, cyclic, and randomness, guiding forecasts.

Explore univariate time series modeling to forecast future data using past values, including AR(p) and MA approaches with white noise assumptions and applications like inflation and stock prices.

Learn how moving average models rely on past error terms and white noise, and how ARMA and ARIMA integrate auto regression, moving average components, and differencing to achieve stationarity.

Learn how to make a time series stationary using first and second differences and apply this to ARIMA modeling. Identify lags with the autocorrelation function and partial autocorrelation function.

Infer ACF and PACF to decide how many past values matter for forecasting with AR, MA, or ARMA models, using correlograms and estimation methods.

Learn diagnostic checking for time series by comparing ARIMA models with AIC/BIC, inspecting residuals' ACF for randomness, and exploring random walk, exponential smoothing, and AR/ARMA structures.

Forecast stock prices using an arima model by sourcing historic sbi data from nse india, merging two csvs, selecting closing prices, and formatting dates for time series forecasting.

Explore time series analysis in R for stock price data using xts, ACF/PACF and ARIMA modeling, with data preparation, stationarity testing, and practical limitations.

Explore ARIMA forecasting for stock price indices by splitting data into training and testing sets. Fit ARIMA(1,1,1), evaluate coefficient confidence intervals and AIC, and select the best model for prediction.

Explore time series forecasting of stock prices using Prophet, addressing rising forecast error and random walk behavior, and learn data preparation with y and date parameters for Prophet.

Learn to forecast stock prices using Prophet, train an ARIMA baseline, create future data frames, and interpret forecast plots with trend, weekly, and yearly components.

Master time series data and deseasonalization in Excel by converting monthly airline passengers data to quarterly, using pivot tables to create clear, presentable reports.

Create an Excel line chart with markers for airline passenger data, label year and quarter on the horizontal axis, and apply four-quarter moving average CMA to reveal seasonality and trend.

Learn how to identify seasonality and irregular components in time series, deseasonalize data, and estimate trend with linear regression using Excel's data analysis tools.

learn to build a simple linear regression model in R, estimate coefficients and p-values, and forecast time series with trend and seasonality on quarterly data.

Explore how gradient boosting machines create a sequential ensemble of weak models, weighting errors to optimize a differentiable loss and boost prediction accuracy.

Discover how gradient boosting turns weak classifiers into a strong learner, addresses overfitting with tricks, and blends h1, h2, h3 via weighted voting and bagging concepts.

Explore gradient boosting using decision tree stumps to partition data and reduce error rate. Learn how to weight weak learners with alphas and minimize error to create a wisdom-of-crowds ensemble.

Explore how gradient boosting updates observation weights via an exponential form, using a learning rate alpha and z, where misclassified items gain weight according to h_t(x) and y.

Gradient boosting trees build sequentially, each new tree correcting the previous error, using learning rate, depth, and number of trees to balance accuracy and generalization.

Explore gradient boosting and AdaBoost as adaptive ensemble methods that improve weak learners by focusing on misclassified errors in a practical R example.

Explore how to structure a 12,000-record dataset for training and testing, build classification and regression trees with rpart, prune with cross-validation, and boost performance with gradient boosting.

Learn gradient boosting with gbm and adaBoost, tuning trees, shrinkage, and distribution options like bernoulli and multinomial to improve accuracy on training and test data.

Explore market basket analysis as an unsupervised learning method that discovers association rules among item sets using support, confidence, and lift, with differential analysis for retailer insights.

Explore market basket analysis as a simple unsupervised algorithm that uses lift, support, and confidence to reveal associations and warn when lift signals positive or negative correlations.

Interpret market basket rules by analyzing support, confidence, and lift with region-based transaction examples. Understand how x and y co-occur and influence probabilities.

Explore how market basket analysis uses association rules with high support, high confidence, and high positive lift to guide product placement, pricing, and cross-selling.

Explore market basket analysis using a groceries data set from 30 days, learning about support, confidence level, and leap for association rules, and implementing with Excel Miner and R.

Apply data mining and apriori market basket analysis to grocery transaction data, modeling binary matrices and using support, confidence, and lift to uncover item associations.

conduct market basket analysis in Rstudio by loading the groceries data set, applying association rules with arules and arules visualization, and converting transactions to a sparse matrix for insights.

Explore market basket analysis in RStudio by inspecting a grocery csv, computing item frequencies, plotting top items, and deriving association rules with apriori, including support and confidence.

Explore association rules in Rstudio for market analysis by inspecting, sorting, and pruning rules based on support, confidence, and lift, and visualize with graph plots and interactive options.

Explore new developments in machine learning, the data scientist role, salary trends, six predicted jobs in the decade, Twitter data harvesting, Google Analytics, and a Facebook chatbot example.

Explore the data scientist's role in the big data landscape, including tools from ETL and Hadoop to visualization platforms, and Google's pre-trained machine learning APIs.

Explore how Google Vision API detects labels, faces, text via OCR, landmarks with longitude and latitude, logos, and explicit content, returning json for integration with Firebase, BigQuery, and NLP workflows.

Learn natural language processing with sentiment analysis and entity extraction, using Google BigQuery for analytics, the cloud natural language API, and RStudio workflows to analyze tweets and build word clouds.

The lecture demonstrates collecting and preprocessing Twitter data in R, converting tweets to text, creating and cleaning a corpus, generating a word cloud, and preparing for sentiment analysis.

Develop a data science model to predict Titanic passenger survival using Python and machine learning basics. Master data preparation, feature engineering, training, and visualization with matplotlib and scikit-learn on datasets.

Outlines the data science life cycle from problem definition to deployment, detailing data gathering, cleaning, feature engineering, modeling, evaluation, and algorithm selection with logistic regression and SVC.

Import numpy, pandas, seaborn, and matplotlib to preprocess and visualize data, then apply logistic regression, random forest, decision tree, and SVC classifiers.

Learn to set up a data science project by importing numpy and pandas for numerical computation and data preprocessing, then visualize data with seaborn and matplotlib.

Explore the decision tree classifier, its recursive partitioning and interpretable rules, then learn how random forest ensembles improve accuracy for classification and regression, including SVC/SVM hyperplanes.

Mastering artificial intelligence with Python and R introduces logistic regression for binary classification, comparing sklearn models like logistic regression, random forest, decision tree, and linear SVC.

Perform exploratory data analysis (EDA) to summarize data characteristics, reveal insights, and guide modeling through univariate and bivariate analyses, correlations, hypothesis testing, and visualization.

Import pandas, load the train and test CSVs, and explore the data with info, describe, and head to identify data types, statistics, and missing values such as cabin and embarked.

Compute null value percentages and round them, then visualize survival patterns with seaborn plots and facet grids by age, sex, embarked port, and class.

Plot a bar plot of survival rate by class to reveal class-related differences, and preprocess Titanic data by creating relatives, dropping passenger id, encoding cabin to deck, and imputing age.

The lecture demonstrates feature engineering: converting name to a numeric title, encoding sex and embarked, dropping the name column, and creating age group, fare group, and fare per person.

Train and compare logistic regression, decision tree, random forest, and SVC models on Xtrain and Ytrain; assess scores with tenfold cross-validation, explore random forest feature importance, and tune hyperparameters.

Explore splitting data into training, validation, and test sets, training and validating models, and evaluating classification performance with metrics like confusion matrix components, accuracy, precision, recall, F1, and ROC AUC.

Draw the confusion matrix, run three-fold cross validation with logistic regression on the training data, and plot precision-recall and ROC curves while noting a ROC AUC of 97%.

Explore building a propensity model to predict card purchases, import the dataset with read.csv, identify the card_offer target, and discuss numeric versus factor conversion and event rate.

Evaluate missing and blank spaces, explore unique variables, and compute information value (IV) to gauge predictor importance for logistic regression, then apply backward elimination to remove suspicious variables.

Explore plotting categorical variables with ggplot2, mapping card offer and gender, and encoding categorical variables to compare event rates across country regions.

Split data into training and test sets with caTools, then scale numeric features, apply binning (including weight of evidence) and try logistic regression to compare performance.

Fit a logistic regression model (glm, binomial) in R, inspect coefficients and p-values, predict on the test set, and use a stepwise, aic-based variable selection to improve accuracy near 84%.

Apply backward elimination with GLM to identify key predictors—income, country, holding balance, and credit score—then evaluate model performance using AIC, ROC, and AUC for optimal variable selection.

Explore building lift charts and gain charts from a training set to evaluate predictive models, score customers by purchase probability, and compare train and test data.

Learn to build and interpret a lift chart and gain for a logistic regression model, using deciles, pivot tables, and goods-vs-bads metrics to assess training and test performance.

Evaluate model performance on the test set by generating predictions and calculating test scores, then analyze the fourth decile lift and case to understand out-of-time validity.

Learn how to save and load models in R, use scores and deciles to evaluate performance, and deploy scoring with logistic regression for predictions.

Explore fitting a decision tree model and compare its performance to logistic regression. Learn data preparation, handling missing values, and training with rpart in R, noting scale not required.

Plot the classifier and set margin to 0.1 to visualize the decision boundaries, and use the basic model with a 0.5546 threshold to predict whether a customer buys, with rpart.plot.

Explore how to generate predictions with a classifier, view class probabilities by setting type=class, and assess model performance via confusion matrices, lift and gain charts, and pruning forests.