Introduction To Python Sets, Lists, Dictionaries: Fill & Download for Free

Thanks for the A2A.The courses you need to take will vary greatly depending on your background. I haven’t done the Udacity Machine Learning NanoDegree but I can tell you what Udacity suggests and offer my insights as I have taken many of their courses.From Udacity:Prior to entering the Machine Learning Engineer Nanodegree program, the student should have the following knowledge:Intermediate Python programming knowledge, of the sort gained through the Introduction to Programming Nanodegree, other introductory programming courses or programs, or additional real-world software development experience. Including:Strings, numbers, and variablesStatements, operators, and expressionsLists, tuples, and dictionariesConditions, loopsProcedures, objects, modules, and librariesTroubleshooting and debuggingResearch & documentationProblem solvingAlgorithms and data structuresIf you already know what all of those bullet points are move on.If you are a beginner with Python, the free Intro to CS course is a solid intro to Python with lots of practice exercises. It took me about 3 months spending 10 hours a week.I DO NOT recommend Udacity’s Python course especially not for beginners. I found it consisted of a lot of really fun projects but didn’t actually teach me anything. It also includes some powerful things like how to text and email people through Python without a lot of cautions that beginning coders need. Intro to CS is a much better introduction to Python.If you have some Python experience but aren’t sure about some of the items above I would try out the practice questions from the Intro to Programming class or the Intro to CS and review the lessons is necessary. I’d say Intro to Programming reaches a little more intermediate level of Python.Continuing from Udacity:Intermediate statistical knowledge, of the sort gained through any of Udacity’s introductory statistics courses. Including:Populations, samplesMean, median, modeStandard errorVariation, standard deviationsNormal distributionPrecision and accuracyIntermediate calculus and linear algebra mastery, addressed in the Linear Algebra Refresher Course, including:DerivativesIntegralsSeries expansionsMatrix operations through eigenvectors and eigenvaluesAdditional external resources recommended by Udacity:Neural Networks:Welch LabsLinear Algebra:Khan Academy - Linear AlgebraMIT - Linear AlgebraCalculus:MIT - Variable CalculusKhan Academy - Differential CalculusKhan Academy - Integral CalculusKhan Academy - Multivariable CalculusKhan Academy - Differential EquationsHonestly if you have some Python experience (one year or a solid course) and have taken a standard statistics course and a linear algebra class at some point in your life I would just review the linear algebra a little and go for it. Even if you haven’t taken a formal linear algebra course, the “refresher” course covers everything you need and more. Alternatively, try the free Machine Learning class after which you should be able to breeze through the NanoDegree. I’m assuming you want to do the NanoDegree to get some sort of credit or help or motivation but if not, consider just taking the free courses and following my other suggestions below.Other tips for studying Machine Learning:Keep a notebook where you can write down new concepts. They say that actually writing down notes vs. typing them helps you learn because it gives your brain a little more time to process the new concepts. Draw out some maps of how these concepts relate to each other. This may not apply to everyone but I helps me.Explain/teach new concepts to others such as coworkers or children. I made a simple powerpoint about neural networks and presented it to anyone I could get to listen. Creating that lecture forced me to really learn the concepts and the math so that I could be confident in explaining and answering questions.Read and ANSWER questions on Quora. Share your newfound knowledge. If you wrote out an answer for everything you learned, you will learn it on a much deeper level and help others.Start your own machine learning project (even if you are a complete beginner) that works toward solving some classification or prediction problem you care about whether it is for work or sports or for a social cause i.e. hate speech or human trafficking . There are any number of basic classifiers on Github ie Keras that you can copy and modify to your ends using your own or free data sets. WildML also has lots of free code and a nice glossarySeek out others who are learning on their own. This guy has accumulated the most comprehensive list of resources for learning machine learning that I have ever seen and is pretty inspirational. Devote some time everyday. If you ever feel frustrated, be thankful you have the internet where you can look up/ask for anything you want without having to be enrolled at any fancy schools.Read machine learning papers. Here is a list of some of the outstanding papers from the Neural Information Processing Systems (NIPS) 2016 conference.Write a machine learning research paper(s). Write up a paper like the ones you see at NIPS. If you were in a MS or phD program you would be required to do this so you need to do it too.As far as classes to take after that, again it depends greatly on what you want to do. Udacity’s Deep Learning course is a good start. I also recommend checking out the many Stanford classes online such as 224D Deep Learning for Natural Language Processing (syllabus, YouTube lectures) and continuing to research on your own and for nonprofits (Code Alliance can help you find one) if you don’t yet have a job where you can start machine learning projects.

Dear Ekansh,Thanks for A2A.It has taken me countless hours to find the right content. I had to dig through a lot of bookmarks and archives to compile the stuff I have mentioned below. The reason why I have done this is, so you guys don’t reinvent the wheel and use this answer as a platform to accomplish more.I hope this post answers your question. If it does, then share it with others and help in spreading the knowledge. :)Note: This is a very long post. You may exhibit tl;dr syndrome.Firstly, it is heartening to see your eagerness to learn about Computer Science. Kudos to your spirit!!However, you, like many others are exhibiting FOMO, Fear Of Missing Out.You need not dig 100 wells of depth 10 feet each, rather you should dig 10 wells of 100 feet.Time is a crucial resource and hence you should choose your battles wisely. Instead of chasing 10 different things simultaneously, pick up 2–3 items and try to build expertise in that area.Since you have learnt C, try learning C++ and STL. Learn the constructs of the language and understand the concept/philosophy of Object Oriented Programming.Also, take up courses on Data Structures and Algorithms.Start from the basics. Learn how to code: array, stack, queue, linked list, bi-directional linked list, list of list, Binary search tree. Once you have gained confidence in these, pick up advanced topics like hash maps, sets, AVL tree, red-black tree, B tree, segment tree, dynamic arrays, TRIE etc.All these are tools to accomplish a certain task. Hence you should know when to use them. Understand their space-time complexity, where all these structures are applied etc.Now in order to solve a problem, you come with algorithms. There are tons of Algorithms out there. You need not know all, but read/implement searching, sorting Algorithms.There are different sorts of problem solving strategies, like brute Force, greedy, divide and conquer, dynamic programming etc. Learn more about them and understand when to apply them.Sometime while learning, we focus so much on the DS/Algo, that we fail to bridge the gap between the concept and it's application. Hence practice is the most crucial part of the entire learning cycle.IDE for development:You can use CodeBlocks. It is a good IDE for Windows.If you use Linux, then you can go for Vi editor and GDB for debugging.There are many online courses on DS, Algorithms, programming languages.Now, I'll answer your main question: which resources to use and how to go about accomplishing your objectives.Warning: Before you go any furtherThe content below is very exhaustive and can be intimidating.The objective is not to scare you away, but to share a thorough list with you and people like you who would prefer the info at one single place.Despite my efforts, the list might still be missing something. If so, do let me know and I’ll add it.You might not require some of the items mentioned below. So study what you need.Online Courses:C++, Object Oriented Programming:For those who have some background in C, you must go through the NPTEL-HRD video series by Dr. Partha Pratim Das.AlgorithmsThere is a 4 courses specialization by Stanford University’s Dr. Tim Roughgarden on Coursera. These are good lecture series to enroll in.Algorithms | CourseraOr alternatively, you can also look at Algorithms course from Princeton University offered by Dr. Sedgewick.Algorithms, Part I | CourseraAlgorithms, Part II | CourseraPythonI have heard about Udemy’s Python program. However I dont have any first hand experience. But you can check it out from the following link:Complete Python Bootcamp: Go from zero to hero in PythonDSThere are YouTube tutorials by Tushar Roy, Saurabhschool, iDeserve etc which you can refer to for your DS related issues. If you want, then there are courses available on EdX and Coursera for the same.Standard Template Library (STL)If you are planning to go with C++ as your language of preference, then I’ll recommend you to first study the C++ video series I mentioned above and then go about studying STL. There is a book called “Effective STL” by Scott Meyers which you can refer to. Additionally, you can refer to online resources wherever you get stuck.Android/IOS DevelopmentI haven’t personal done this, hence I might not be the right guy to comment on it. But I’ll suggest you to go to YouTube, type “android programming” and there will be tons of video series which will provide you with step-by-step instructions on how to go forward.Your best buddy throughout this journey would be stackoverflow. It will help you resolve your queries on the fly.Competitive Programming:If you are really interested in competitive programming, then you can start with Codechef. Look at their past 25-30 long-events. Pick up the top 2 most solved problems and try solving them on your own. Once you gain confidence, then you should transition to the next 2 most solved problems.After every event, Codechef posts editorial for the competition, go through the editorials of all the questions (even for those you could solve).Gradually, you’ll see that there are a set of algo/DS which are required to ace it in such contests. Practice them and excel further.Getting Started with the Sport of Programming will help you in selecting the right set of problems. Although you should follow the above strategy as well.You can refer to Data Science Tutorials on TopCoder for further insights w.r.t competitive programming.Data Structures:Following is the exhaustive list of DS that I found on geeksforgeeks. The relevant links are mentioned below:Go through the list and implement as many as you can. Understand their importance and try to connect them with a relevant application. Some of them might not be useful to you upfront, but I wanted to keep it exhaustive.Linked List:Singly Linked List:Introduction to Linked ListLinked List vs ArrayLinked List InsertionLinked List Deletion (Deleting a given key)Linked List Deletion (Deleting a key at given position)A Programmer’s approach of looking at Array vs. Linked ListFind Length of a Linked List (Iterative and Recursive)How to write C functions that modify head pointer of a Linked List?Swap nodes in a linked list without swapping dataReverse a linked listMerge two sorted linked listsMerge Sort for Linked ListsReverse a Linked List in groups of given sizeDetect and Remove Loop in a Linked ListAdd two numbers represented by linked lists | Set 1Rotate a Linked ListGeneric Linked List in CCircular Linked List:Circular Linked List Introduction and Applications,Circular Singly Linked List Insertion<Circular Linked List TraversalSplit a Circular Linked List into two halvesSorted insert for circular linked listDoubly Linked List:Doubly Linked List Introduction and InsertionDelete a node in a Doubly Linked ListReverse a Doubly Linked ListThe Great Tree-List Recursion Problem.QuickSort on Doubly Linked ListMerge Sort for Doubly Linked ListAll Articles of Linked ListQuiz on Linked ListCoding Practice on Linked ListRecent Articles on Linked ListStack:Introduction to StackInfix to Postfix Conversion using StackEvaluation of Postfix ExpressionReverse a String using StackImplement two stacks in an arrayCheck for balanced parentheses in an expressionNext Greater ElementReverse a stack using recursionSort a stack using recursionThe Stock Span ProblemDesign and Implement Special Stack Data StructureImplement Stack using QueuesDesign a stack with operations on middle elementHow to efficiently implement k stacks in a single array?Sort a stack using recursionQuiz on StackAll Articles on StackCoding Practice on StackRecent Articles on StackQueue:Queue Introduction and Array ImplementationLinked List Implementation of QueueApplications of Queue Data StructurePriority Queue IntroductionDeque (Introduction and Applications)Implementation of Deque using circular arrayImplement Queue using StacksFind the first circular tour that visits all petrol pumpsMaximum of all subarrays of size kAn Interesting Method to Generate Binary Numbers from 1 to nHow to efficiently implement k Queues in a single array?Quiz on QueueAll Articles on QueueCoding Practice on QueueRecent Articles on QueueBinary Tree:Binary Tree IntroductionBinary Tree PropertiesTypes of Binary TreeHandshaking Lemma and Interesting Tree PropertiesEnumeration of Binary TreeApplications of tree data structureTree TraversalsBFS vs DFS for Binary TreeLevel Order Tree TraversalDiameter of a Binary TreeInorder Tree Traversal without RecursionInorder Tree Traversal without recursion and without stack!Threaded Binary TreeMaximum Depth or Height of a TreeIf you are given two traversal sequences, can you construct the binary tree?Clone a Binary Tree with Random PointersConstruct Tree from given Inorder and Preorder traversalsMaximum width of a binary treePrint nodes at k distance from rootPrint Ancestors of a given node in Binary TreeCheck if a binary tree is subtree of another binary treeConnect nodes at same levelQuiz on Binary TreeQuiz on Binary Tree TraversalsAll articles on Binary TreeCoding Practice on Binary TreeRecent Articles on TreeBinary Search Tree:Search and Insert in BSTDeletion from BSTMinimum value in a Binary Search TreeInorder predecessor and successor for a given key in BSTCheck if a binary tree is BST or notLowest Common Ancestor in a Binary Search Tree.Inorder Successor in Binary Search TreeFind k-th smallest element in BST (Order Statistics in BST)Merge two BSTs with limited extra spaceTwo nodes of a BST are swapped, correct the BSTFloor and Ceil from a BSTIn-place conversion of Sorted DLL to Balanced BSTFind a pair with given sum in a Balanced BSTTotal number of possible Binary Search Trees with n keysMerge Two Balanced Binary Search TreesBinary Tree to Binary Search Tree ConversionQuiz on Binary Search TreesQuiz on Balanced Binary Search TreesAll Articles on Binary Search TreeCoding Practice on Binary Search TreeRecent Articles on BSTHeap:Binary HeapWhy is Binary Heap Preferred over BST for Priority Queue?Binomial HeapFibonacci HeapHeap SortK’th Largest Element in an arraySort an almost sorted array/Tournament Tree (Winner Tree) and Binary HeapAll Articles on HeapQuiz on HeapCoding Practice on HeapRecent Articles on HeapHashing:Hashing IntroductionSeparate Chaining for Collision HandlingOpen Addressing for Collision HandlingPrint a Binary Tree in Vertical OrderFind whether an array is subset of another arrayUnion and Intersection of two Linked ListsFind a pair with given sumCheck if a given array contains duplicate elements within k distance from each otherFind Itinerary from a given list of ticketsFind number of Employees Under every EmployeeQuiz on HashingAll Articles on HashingCoding Practice on HashingRecent Articles on HashingGraph:Introduction, DFS and BFS:Graph and its representationsBreadth First Traversal for a GraphDepth First Traversal for a GraphApplications of Depth First SearchApplications of Breadth First TraversalDetect Cycle in a Directed GraphDetect Cycle in a an Undirected GraphDetect cycle in an undirected graphLongest Path in a Directed Acyclic GraphTopological SortingCheck whether a given graph is Bipartite or notSnake and Ladder ProblemMinimize Cash Flow among a given set of friends who have borrowed money from each otherBoggle (Find all possible words in a board of characters)Assign directions to edges so that the directed graph remains acyclicAll Articles on Graph Data StructureQuiz on GraphQuiz on Graph TraversalsQuiz on Graph Shortest PathsQuiz on Graph Minimum Spanning TreeCoding Practice on GraphRecent Articles on GraphAdvanced Data Structure:Advanced Lists:Memory efficient doubly linked listXOR Linked List – A Memory Efficient Doubly Linked List | Set 1XOR Linked List – A Memory Efficient Doubly Linked List | Set 2Skip List | Set 1 (Introduction)Self Organizing List | Set 1 (Introduction)Unrolled Linked List | Set 1 (Introduction)Segment Tree:Segment Tree | Set 1 (Sum of given range)Segment Tree | Set 2 (Range Minimum Query)Lazy Propagation in Segment TreePersistent Segment Tree | Set 1 (Introduction)All articles on Segment TreeTrie:Trie | (Insert and Search)Trie | (Delete)Longest prefix matching – A Trie based solution in JavaPrint unique rows in a given boolean matrixHow to Implement Reverse DNS Look Up Cache?How to Implement Forward DNS Look Up Cache?All Articles on TrieBinary Indexed Tree:Binary Indexed TreeTwo Dimensional Binary Indexed Tree or Fenwick TreeBinary Indexed Tree : Range Updates and Point QueriesBinary Indexed Tree : Range Update and Range QueriesAll Articles on Binary Indexed TreeSuffix Array and Suffix Tree:Suffix Array IntroductionSuffix Array nLogn Algorithmkasai’s Algorithm for Construction of LCP array from Suffix ArraySuffix Tree IntroductionUkkonen’s Suffix Tree Construction – Part 1Ukkonen’s Suffix Tree Construction – Part 2Ukkonen’s Suffix Tree Construction – Part 3Ukkonen’s Suffix Tree Construction – Part 4,Ukkonen’s Suffix Tree Construction – Part 5Ukkonen’s Suffix Tree Construction – Part 6Generalized Suffix TreeBuild Linear Time Suffix Array using Suffix TreeSubstring CheckSearching All PatternsLongest Repeated Substring,Longest Common Substring, Longest Palindromic SubstringAll Articles on Suffix TreeAVL Tree:AVL Tree | Set 1 (Insertion)AVL Tree | Set 2 (Deletion)AVL with duplicate keysSplay Tree:Splay Tree | Set 1 (Search)Splay Tree | Set 2 (Insert)B Tree:B-Tree | Set 1 (Introduction)B-Tree | Set 2 (Insert)B-Tree | Set 3 (Delete)Red-Black Tree:Red-Black Tree IntroductionRed Black Tree Insertion.Red-Black Tree DeletionProgram for Red Black Tree InsertionAll Articles on Self-Balancing BSTsK Dimensional Tree:KD Tree (Search and Insert)K D Tree (Find Minimum)K D Tree (Delete)Others:Treap (A Randomized Binary Search Tree)Ternary Search TreeInterval TreeImplement LRU CacheSort numbers stored on different machinesFind the k most frequent words from a fileGiven a sequence of words, print all anagrams togetherTournament Tree (Winner Tree) and Binary HeapDecision Trees – Fake (Counterfeit) Coin Puzzle (12 Coin Puzzle)Spaghetti StackData Structure for Dictionary and Spell Checker?Cartesian TreeCartesian Tree SortingSparse SetCentroid Decomposition of TreeGomory-Hu TreeAlgorithms:Following is the list of algorithms that you should know. Again, it is an exhaustive list, which has been compiled from Codechef. You need not study all of them, but you can refer to them if a need arisesBinary Search : Tutorial, Problems, Tutorial, Implementation, ProblemQuicksort : Tutorial, Implementation, TutorialMerge Sort : Tutorial, Implementation, TutorialSuffix Array : Tutorial, Tutorial, Implementation, Tutorial, Implementation, Problem, ProblemKnuth-Morris-Pratt Algorithm (KMP) : Tutorial, Tutorial, Implementation, Tutorial, ProblemRabin-Karp Algorithm : Tutorial, Implementation, Tutorial, Problem, ProblemTries : Tutorial, Problems, Tutorial : I, II, Tutorial, Problem, Problem, ProblemDepth First Traversal of a graph : Tutorial, Impelementation, Tutorial, Problems, Problem, Problem, ProblemBreadth First Traversal of a graph : Tutorial, Impelementation, Tutorial, Problems, Problem, Problem, Problem, Flood FillDijkstra's Algorithm : Tutorial, Problems, Problem, Tutorial(greedy), Tutorial (with heap), Implementation, Problem, ProblemBinary Indexed Tree : Tutorial, Problems, Tutorial, Original Paper, Tutorial, Tutorial, Problem, Problem, Problem, Problem, Problem, Problem, ProblemSegment Tree (with lazy propagation) : Tutorial, Implementation, Tutorial, Tutorial, Problems, Implementation, Tutorial, Implementation and Various Uses, Persistent Segment Tree, problems same as BIT, Problem, Problem/HLD is used as well/Z algorithm : Tutorial, Problem, Tutorial, problems same as KMP.Floyd Warshall Algorithm : Tutorial, Implementation, Problem, ProblemSparse Table (LCP, RMQ) : Tutorial, Problems, Tutorial, Implementation(C++), Java implementationHeap / Priority Queue / Heapsort : Implementation, Explanation, Tutorial, Implementation, Problem, Chapter from CLRSModular Multiplicative InverseBinomial coefficients (nCr % M): Tutorial, Tutorial, Paper, ProblemSuffix Automaton : Detailed Paper, Tutorial, Implementation (I), Tutorial, Implementation (II), Problem, Problem, Problem, Problem, Tutorial, ImplementationLowest Common Ancestor : Tutorial, Problems, Paper, Paper, Problem, Problem, ProblemCounting Inversions : Divide and Conquer, Segment Tree, Fenwick Tree, ProblemEuclid's Extended AlgorithmSuffix Tree : Tutorial, Tutorial, Intro, Construction : I, II, Implementation, Implementation, Problem, Problem, Problem, ProblemDynamic Programming : Chapter from CLRS(essential), Tutorial, Problems, Problem, Problem, Problem, Problem, Tutorial, Problem, Problem, Problem, Longest Increasing Subsequence, Bitmask DP, Bitmask DP, Optimization, Problem, Problem, Problem, Problem, Problem, Problem, Problem, DP on Trees : I, IIBasic Data Structures : Tutorial, Stack Implementation, Queue Implementation, Tutorial, Linked List ImplementationLogarithmic ExponentiationGraphs : Definition, Representation, Definition, Representation, Problem, ProblemMinimum Spanning Tree : Tutorial, Tutorial, Kruskal's Implementation, Prim's Implementation, Problem, Problem, Problem, Problem, ProblemEfficient Prime FactorizationCombinatorics : Tutorial, Problems, Problem, TutorialUnion Find/Disjoint Set : Tutorial, Tutorial, Problems, Problem, Problem, ProblemKnapsack problem : Solution, ImplementationAho-Corasick String Matching Algorithm : Tutorial, Implementation, Problem, Problem, Problem, ProblemStrongly Connected Components : Tutorial, Implementation, Tutorial, Problem, Problem, ProblemBellman Ford algorithm : Tutorial, Implementation, Tutorial, Implementation, Problem, ProblemHeavy-light Decomposition : Tutorial, Problems, Tutorial, Implementation, Tutorial, Implementation, Implementation, Problem, Problem, ProblemConvex Hull : Tutorial, Jarvis Algorithm Implementation, Tutorial with Graham scan, Tutorial, Implementation, Problem, Problem, Problem, Problem, ProblemLine Intersection : Tutorial, Implementation, Tutorial, ProblemsSieve of ErastothenesInterval Tree : Tutorial, Implementation, Problem, Problem, Problem, Problem, Problem, Problem, TutorialCounting SortProbabilitiesMatrix Exponentiation : Tutorial, TutorialNetwork flow : (Max Flow)Tutorial : I, II, Max Flow(Ford-Fulkerson) Tutorial, Implementation, (Min Cut) Tutorial, Implementation, (Min Cost Flow)Tutorial : I, II, III, Dinic's Algorithm with Implementation, Max flow by Edmonds Karp with Implementation, Problem, Problem, Problem, Problem, Problem, Problem, Problem, Problem, Problem, Problem, Problem, Problem, Problem, Problem, ProblemK-d tree : Tutorial, Tutorial, Implementation, ProblemDequeBinary Search Tree : Tutorial, Implementation, Searching and Insertion, DeletionQuick Select : Implementation, ImplementationTreap/Cartesian Tree : Tutorial(detailed), Tutorial, Implementation, Uses and Problems, Problem, ProblemGame Theory : Detailed Paper, Tutorial, Problems, Grundy Numbers, Tutorial with example problems - I, II, III,IV, Tutorial, Problems, Problem, Problem, Problem, Problem, Problem, Problem, Problem, Problem, Problem, Problem, Problem, NimSTL (C++) : I, II, Crash CourseMaximum Bipartite MatchingManacher's Algorithm : Implementation, Tutorial, Tutorial, Implementation, Tutorial, Implementation, Problem, Problem, ProblemMiller-Rabin Primality Test : CodeStable Marriage ProblemHungarian Algorithm, TutorialSweep line Algorithm : I, IILCP : Tutorial, Implementation, Tutorial, ImplementationGaussian EliminationPollard Rho Integer Factorization, problemTopological SortingDetecting Cycles in a Graph : Directed - I, II Undirected : IGeometry : Basics, TutorialBacktracking : N queens problem, Tug of War, SudokuEulerian and Hamiltonian Paths : Tutorial, Tutorial, (Eulerian Path and Cycle)Implementation, (Hamiltonian Cycle)ImplementationGraph Coloring : Tutorial, ImplementationMeet in the Middle : Tutorial, ImplementationArbitrary Precision Integer(BigInt), IIRadix Sort, Bucket SortJohnson's Algorithm : Tutorial, Tutorial, ImplementationMaximal Matching in a General Graph : Blossom/Edmond's Algorithm, Implementation, Tutte Matrix, ProblemRecursion : I, II, Towers of Hanoi with explanationInclusion and Exclusion Principle : I, IICo-ordinate CompressionSqrt-Decomposition : Tutorial, Tutorial, Problem, ProblemLink-Cut Tree : Tutorial, Wiki, Tutorial, Implementation, Problem, Problem, Problem, ProblemEuler's Totient Function : Explanation, Implementation, Problems, Explanation, ProblemsBurnside Lemma : Tutorial, Tutorial, ProblemEdit/Levenshtein Distance : Tutorial, Introduction, Tutorial, Problem, ProblemBranch and BoundMath for Competitive ProgrammingMo's Algorithm : Tutorial and ProblemsIf you have gone through the entire post, then here is a <3 (heart) for you.I hope it is useful!Cheers!#happyLearning

Introduction to Data Science Deep Learning & Artificial IntelligenceIntroduction to Deep Learning & AIDeep Learning: A revolution in Artificial IntelligenceLimitations of Machine LearningWhat is Deep Learning?Need for Data ScientistsFoundation of Data ScienceWhat is Business IntelligenceWhat is Data AnalysisWhat is Data MiningWhat is Machine Learning?Analytics vs Data ScienceValue ChainTypes of AnalyticsLifecycle ProbabilityAnalytics Project LifecycleAdvantage of Deep Learning over Machine learningReasons for Deep LearningReal-Life use cases of Deep LearningReview of Machine LearningDataBasis of Data CategorizationTypes of DataData Collection TypesForms of Data & SourcesData Quality & ChangesData Quality IssuesData Quality StoryWhat is Data ArchitectureComponents of Data ArchitectureOLTP vs OLAPHow is Data Stored?Big DataWhat is Big Data?5 Vs of Big DataBig Data ArchitectureBig Data TechnologiesBig Data ChallengeBig Data RequirementsBig Data Distributed Computing & ComplexityHadoopMap Reduce FrameworkHadoop EcosystemData Science Deep DiveWhat Data Science isWhy Data Scientists are in demandWhat is a Data ProductThe growing need for Data ScienceLarge Scale Analysis Cost vs StorageData Science SkillsData Science Use CasesData Science Project Life Cycle & StagesData AcuqisitionWhere to source dataTechniquesEvaluating input dataData formatsData QuantityData QualityResolution TechniquesData TransformationFile format ConversionsAnnonymizationPythonPython OverviewAbout Interpreted LanguagesAdvantages/Disadvantages of Python pydoc.Starting PythonInterpreter PATHUsing the InterpreterRunning a Python ScriptUsing VariablesKeywordsBuilt-in FunctionsStringsDifferent LiteralsMath Operators and ExpressionsWriting to the ScreenString FormattingCommand Line Parameters and Flow Control.ListsTuplesIndexing and SlicingIterating through a SequenceFunctions for all SequencesOperators and Keywords for SequencesThe xrange() functionList ComprehensionsGenerator ExpressionsDictionaries and Sets.Numpy & PandasLearning NumPyIntroduction to PandasCreating Data FramesGroupingSortingPlotting DataCreating FunctionsSlicing/Dicing Operations.Deep Dive – Functions & Classes & OopsFunctionsFunction ParametersGlobal VariablesVariable Scope and Returning Values. SortingAlternate KeysLambda FunctionsSorting Collections of CollectionsClasses & OOPsStatisticsWhat is StatisticsDescriptive StatisticsCentral Tendency MeasuresThe Story of AverageDispersion MeasuresData DistributionsCentral Limit TheoremWhat is SamplingWhy SamplingSampling MethodsInferential StatisticsWhat is Hypothesis testingConfidence LevelDegrees of freedomwhat is pValueChi-Square testWhat is ANOVACorrelation vs RegressionUses of Correlation & RegressionMachine Learning, Deep Learning & AI using PythonIntroductionML FundamentalsML Common Use CasesUnderstanding Supervised and Unsupervised Learning TechniquesClusteringSimilarity MetricsDistance Measure Types: Euclidean, Cosine MeasuresCreating predictive modelsUnderstanding K-Means ClusteringUnderstanding TF-IDF, Cosine Similarity and their application to Vector Space ModelCase studyImplementing Association rule miningWhat is Association Rules & its use cases?What is Recommendation Engine & it’s working?Recommendation Use-caseCase studyUnderstanding Process flow of Supervised Learning TechniquesDecision Tree ClassifierHow to build Decision treesWhat is Classification and its use cases?What is Decision Tree?Algorithm for Decision Tree InductionCreating a Decision TreeConfusion MatrixCase studyRandom Forest ClassifierWhat is Random ForestsFeatures of Random ForestOut of Box Error Estimate and Variable ImportanceCase studyNaive Bayes Classifier.Case studyProject DiscussionProblem Statement and AnalysisVarious approaches to solve a Data Science ProblemPros and Cons of different approaches and algorithms.Linear RegressionCase studyIntroduction to Predictive ModelingLinear Regression OverviewSimple Linear RegressionMultiple Linear RegressionLogistic RegressionCase studyLogistic Regression OverviewData PartitioningUnivariate AnalysisBivariate AnalysisMulticollinearity AnalysisModel BuildingModel ValidationModel Performance Assessment AUC & ROC curvesScorecardSupport Vector MachinesCase StudyIntroduction to SVMsSVM HistoryVectors OverviewDecision SurfacesLinear SVMsThe Kernel TrickNon-Linear SVMsThe Kernel SVMTime Series AnalysisDescribe Time Series dataFormat your Time Series dataList the different components of Time Series dataDiscuss different kind of Time Series scenariosChoose the model according to the Time series scenarioImplement the model for forecastingExplain working and implementation of ARIMA modelIllustrate the working and implementation of different ETS modelsForecast the data using the respective modelWhat is Time Series data?Time Series variablesDifferent components of Time Series dataVisualize the data to identify Time Series ComponentsImplement ARIMA model for forecastingExponential smoothing modelsIdentifying different time series scenario based on which different Exponential Smoothing model can be appliedImplement respective model for forecastingVisualizing and formatting Time Series dataPlotting decomposed Time Series data plotApplying ARIMA and ETS model for Time Series forecastingForecasting for given Time periodCase StudyMachine Learning ProjectMachine learning algorithms PythonVarious machine learning algorithms in PythonApply machine learning algorithms in PythonFeature Selection and Pre-processingHow to select the right dataWhich are the best features to useAdditional feature selection techniquesA feature selection case studyPreprocessingPreprocessing Scaling TechniquesHow to preprocess your dataHow to scale your dataFeature Scaling Final ProjectWhich Algorithms perform bestHighly efficient machine learning algorithmsBagging Decision TreesThe power of ensemblesRandom Forest Ensemble techniqueBoosting – AdaboostBoosting ensemble stochastic gradient boostingA final ensemble techniqueModel selection cross validation scoreIntroduction Model TuningParameter Tuning GridSearchCVA second method to tune your algorithmHow to automate machine learningWhich ML algo should you chooseHow to compare machine learning algorithms in practiceText Mining& NLPSentimental AnalysisCase studyPySpark and MLLibIntroduction to Spark CoreSpark ArchitectureWorking with RDDsIntroduction to PySparkMachine learning with PySpark – MllibDeep Learning & AI using PythonDeep Learning & AICase StudyDeep Learning OverviewThe Brain vs NeuronIntroduction to Deep LearningIntroduction to Artificial Neural NetworksThe Detailed ANNThe Activation FunctionsHow do ANNs work & learnGradient DescentStochastic Gradient DescentBackpropogationUnderstand limitations of a Single PerceptronUnderstand Neural Networks in DetailIllustrate Multi-Layer PerceptronBackpropagation – Learning AlgorithmUnderstand Backpropagation – Using Neural Network ExampleMLP Digit-Classifier using TensorFlowBuilding a multi-layered perceptron for classificationWhy Deep NetworksWhy Deep Networks give better accuracy?Use-Case ImplementationUnderstand How Deep Network Works?How Backpropagation Works?Illustrate Forward pass, Backward passDifferent variants of Gradient DescentConvolutional Neural NetworksConvolutional OperationRelu LayersWhat is Pooling vs FlatteningFull ConnectionSoftmax vs Cross Entropy” Building a real world convolutional neural networkfor image classification”What are RNNs – Introduction to RNNsRecurrent neural networks rnnLSTMs understanding LSTMslong short term memory neural networks lstm in pythonRestricted Boltzmann Machine (RBM) and AutoencodersRestricted Boltzmann MachineApplications of RBMIntroduction to AutoencodersAutoencoders applicationsUnderstanding AutoencodersBuilding a Autoencoder modelTensorflow with PythonIntroducing TensorflowIntroducing TensorflowWhy Tensorflow?What is tensorflow?Tensorflow as an InterfaceTensorflow as an environmentTensorsComputation GraphInstalling TensorflowTensorflow trainingPrepare DataTensor typesLoss and OptimizationRunning tensorflow programsBuilding Neural Networks usingTensorflowTensorsTensorflow data typesCPU vs GPU vs TPUTensorflow methodsIntroduction to Neural NetworksNeural Network ArchitectureLinear Regression example revisitedThe NeuronNeural Network LayersThe MNIST DatasetCoding MNIST NNDeep Learning usingTensorflowDeepening the networkImages and PixelsHow humans recognise imagesConvolutional Neural NetworksConvNet ArchitectureOverfitting and RegularizationMax Pooling and ReLU activationsDropoutStrides and Zero PaddingCoding Deep ConvNets demoDebugging Neural NetworksVisualising NN using TensorflowTensorboardTransfer Learning usingKeras and TFLearnTransfer Learning IntroductionGoogle Inception ModelRetraining Google Inception with our own data demoPredicting new imagesTransfer Learning SummaryExtending TensorflowKerasTFLearnKeras vs TFLearn Comparison