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  • Introduction to Machine Learning 4

    • Lecture1.1
      Introduction to Machine Learning 13 min
    • Lecture1.2
      Key point in Machine Learning ( Basics of ML ) 10 min
    • Lecture1.3
      Steps in Machine learning 06 min
    • Lecture1.4
      Issue in Machine Learning 07 min
  • Learning with Regression and Trees - Machine Learning 5

    • Lecture2.1
      Linear Regression 09 min
    • Lecture2.2
      Logistic Regression part 1 14 min
    • Lecture2.3
      Logistic Regression part 2 14 min
    • Lecture2.4
      Decision Tree Sum using ID3 24 min
    • Lecture2.5
      Decision Tree Sum using CART 22 min
  • Learning with Classification and clustering - Machine Learning 13

    • Lecture3.1
      Bayesian Belief Network 09 min
    • Lecture3.2
      Bayes Theorem and Naive Bayes Classifier 08 min
    • Lecture3.3
      Markov Models
    • Lecture3.4
      Hidden Markov Model 11 min
    • Lecture3.5
      Support Vector Machine 10 min
    • Lecture3.6
      K mean Clustering Algorithm 12 min
    • Lecture3.7
      Apriori Algorithm with solved Example 12 min
    • Lecture3.8
      Agglomerative Algorithm with solved Example Part #1 13 min
    • Lecture3.9
      Agglomerative Algorithm with solved Example Part #2 05 min
    • Lecture3.10
      FP Tree Algorithm with Solved Example 15 min
    • Lecture3.11
      Hidden Markov model Basic Part 1 10 min
    • Lecture3.12
      Hidden Markov model Basic Part 2 07 min
    • Lecture3.13
      Hidden Markov model Basic Part 3 08 min
  • Dimensionality Reduction - Machine Learning 5

    • Lecture4.1
      Dimensionality Reduction 08 min
    • Lecture4.2
      PCA (Principal component Analysis ) Concept 14 min
    • Lecture4.3
      PCA (Principal Component Analysis) Sum 19 min
    • Lecture4.4
      ICA ( Independent Component Analysis ) 07 min
    • Lecture4.5
      Back Propagation 15 min
  • Introduction to Neural Network - Machine Learning 13

    • Lecture5.1
      Introduction to ANN and structure of ANN 06 min
    • Lecture5.2
      Activation functions in ANN (Discrete and Continuous) 04 min
    • Lecture5.3
      Neural Network Architecture 05 min
    • Lecture5.4
      Linear Separability 04 min
    • Lecture5.5
      Mc-Culloch-Pitts Neural Model 03 min
    • Lecture5.6
      Hebbs Network/Hebbian Learning (with solved example) 17 min
    • Lecture5.7
      Winners-Takes-All 05 min
    • Lecture5.8
      Self Organizing Maps and KSOMs 10 min
    • Lecture5.9
      Linear Vector Quantization (LVQs) 05 min
    • Lecture5.10
      Derivation of Unipolar Continuous Function. 07 min
    • Lecture5.11
      Derivation of Bipolar Continuous Function 11 min
    • Lecture5.12
      Perceptron Learning (with solved example) 11 min
    • Lecture5.13
      Backpropagation Network (with solved example) 19 min
  • Introduction to Optimization Techniques - Machine Learning 7

    • Lecture6.1
      Optimization Techniques 08 min
    • Lecture6.2
      Derivative Free Optimization 06 min
    • Lecture6.3
      Gradient (Steepest) Descent 07 min
    • Lecture6.4
      Newton Method 05 min
    • Lecture6.5
      Simulated Annealing 06 min
    • Lecture6.6
      Random Search 03 min
    • Lecture6.7
      Downhill Simplex Search 04 min
  • Machine Learning - Notes 8

    • Lecture7.1
      Module 1
    • Lecture7.2
      Module 4
    • Lecture7.3
      Module 5
    • Lecture7.4
      Module 6
    • Lecture7.5
      Module 7
    • Lecture7.6
      Module 8
    • Lecture7.7
      Neural Network Notes
    • Lecture7.8
      Optimization Technique Notes
  • Data Warehouse and Data Mining INDEX 29

    • Lecture8.1
      Introduction to Data Warehouse 11 min
    • Lecture8.2
      Meta Data 05 min
    • Lecture8.3
      Data Mart 06 min
    • Lecture8.4
      Architecture of data warehouse 07 min
    • Lecture8.5
      How to draw star schema 10 min
    • Lecture8.6
      Olap operation 08 min
    • Lecture8.7
      OLAP VS OLTP 08 min
    • Lecture8.8
      K Mean 08 min
    • Lecture8.9
      Introduction to data mining 10 min
    • Lecture8.10
      Naive Bayes 1
    • Lecture8.11
      Apriori algorithm 12 min
    • Lecture8.12
      Agglomerative clustering 13 min
    • Lecture8.13
      KDD 09 min
    • Lecture8.14
      ETL 09 min
    • Lecture8.15
      FP Tree 15 min
    • Lecture8.16
      Decision Tree
    • Lecture8.17
      K Medoid 21 min
    • Lecture8.18
      Naive Bayes 2 25 min
    • Lecture8.19
      Agglomerative Adjacency Matrix 05 min
    • Lecture8.20
      DBSCAN 04 min
    • Lecture8.21
      Design Strategy of Data warehouse and Data Mining 12 min
    • Lecture8.22
      Types of Attribute for Data Exploration 09 min
    • Lecture8.23
      K mean clustering Sum – Type 2 where K=2 23 min
    • Lecture8.24
      Data Pre-processing Part 1 17 min
    • Lecture8.25
      Data Pre-processing Part 2 09 min
    • Lecture8.26
      Data Visualization Part 1 11 min
    • Lecture8.27
      Data Visualization Part 2
    • Lecture8.28
      Schema Design – Dimension Modeling Part 1 16 min
    • Lecture8.29
      Schema Design – Dimension Modeling Part 2 11 min
  • Data Warehouse and Data Mining - Notes 1

    • Lecture9.1
      Data Warehouse and Data Mining Notes
  • Introduction - Cryptography and System Security 7

    • Lecture10.1
      Introduction to Cryptography and Security System 09 min
    • Lecture10.2
      Security Goals and Mechanism 10 min
    • Lecture10.3
      Symmetric Cipher 02 min
    • Lecture10.4
      Mono Alphabetic Cipher 08 min
    • Lecture10.5
      Poly Alphabetic Cipher 07 min
    • Lecture10.6
      Substitution Cipher 14 min
    • Lecture10.7
      Transposition Cipher 07 min
  • Symmetric and Asymmetric key Cryptography and key Management - Cryptography and System Security 9

    • Lecture11.1
      Stream and Block Cipher 04 min
    • Lecture11.2
      DES Algorithm Full Working 12 min
    • Lecture11.3
      DES key Generation Explain Step by Step 11 min
    • Lecture11.4
      AES Algorithm Full working 26 min
    • Lecture11.5
      Modes of Operation 08 min
    • Lecture11.6
      Public Key cryptogrpahy 03 min
    • Lecture11.7
      RSA Algorithm with Solved Example 14 min
    • Lecture11.8
      Diffie Hellman 07 min
    • Lecture11.9
      How to find modulus of Exponential Number (high power value ) 12 min
  • Hashes, Message Digests and Digital Certificates - Cryptography and System Security 4

    • Lecture12.1
      MD5 (Message Digest Algorithm) 24 min
    • Lecture12.2
      SHA-1 Algorithm Full Working 23 min
    • Lecture12.3
      MAC 07 min
    • Lecture12.4
      Digital Certificate and X.509 10 min
  • Authentication Protocols & Digital signature schemes - Cryptography and System Security 2

    • Lecture13.1
      Kerberos 08 min
    • Lecture13.2
      Digital Signature Full working Explained 19 min
  • Network Security and Applications - Cryptography and System Security 6

    • Lecture14.1
      DOS and DDOS Attack 10 min
    • Lecture14.2
      SSL ( Secure Socket Layer protocol ) 18 min
    • Lecture14.3
      IPSEC introduction 17 min
    • Lecture14.4
      IP SEC Security Protocols 14 min
    • Lecture14.5
      IPSEC Modes of Operation 10 min
    • Lecture14.6
      IDS and its Types 12 min
  • System Security - Cryptography and System Security 4

    • Lecture15.1
      Buffer Overflow and Buffer Overflow attack 09 min
    • Lecture15.2
      Malicious Software ( Virus and worms ) 10 min
    • Lecture15.3
      Virus and worms 13 min
    • Lecture15.4
      SQL Injection 07 min
  • Extra - Cryptography and System Security 8

    • Lecture16.1
      IDEA Algorithm Full Working 14 min
    • Lecture16.2
      Blowfish Algorithm Full working 13 min
    • Lecture16.3
      Confusion and Diffusion 02 min
    • Lecture16.4
      Meet in the Middle 03 min
    • Lecture16.5
      Phishing Attack 11 min
    • Lecture16.6
      Session Hijacking and Spoofing Attack 11 min
    • Lecture16.7
      OS Security Memory and Address Protection 11 min
    • Lecture16.8
      E commerce Security and Payment gateway 09 min
  • Cryptography and System Security - Notes 9

    • Lecture17.1
    • Lecture17.2
      Module 1 ( Introduction to Cryptography )
    • Lecture17.3
      Module 2 ( Basics of Cryptography )
    • Lecture17.4
      Module 3 ( Secret Key Cryptography )
    • Lecture17.5
      Module 4 ( Public Key Cryptography )
    • Lecture17.6
      Module 5 ( Cryptographic Hash Function )
    • Lecture17.7
      Module 6 ( Authentication Application )
    • Lecture17.8
      Module 7 ( Security and Firewalls)
    • Lecture17.9
      Module 8 ( IP Security )
  • Software Engineering Full Notes 1

    • Lecture18.1
      Software Engineering Full Notes


Machine Learning is semester 6 subject of final year of computer engineering in Mumbai University. Prerequisite for studying this subject are Data Structures, Basic Probability and Statistics, Algorithms.Module Introduction to Machine Learning consists of the following subtopics, Types of Machine Learning, Issues in Machine Learning, Application of Machine Learning, and Steps in developing a Machine Learning Application. Module Introduction to Neural Network Introduction consists of the following subtopics Fundamental concept Evolution of Neural Networks Biological Neuron, Artificial Neural Networks, NN architecture, Activation functions, McCulloch-Pitts Model. Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.

Data Warehousing and Mining is semester 6 subject of final year of computer engineering in Mumbai University. Prerequisite for studying this subject are Basic database concepts, Concepts of algorithm design and analysis.Module Introduction to Data Warehouse and Dimensional modelling contains the following topics Introduction to Strategic Information, Need for Strategic Information, Features of Data Warehouse, Data warehouses versus Data Marts, Top-down versus Bottom-up approach. Data warehouse architecture, metadata, E-R modelling versus Dimensional Modelling, Information Package Diagram, STAR schema, STAR schema keys, Snowflake Schema, Fact Constellation Schema, Factless Fact tables, Update to the dimension tables, Aggregate fact tables. Module ETL Process and OLAP contains the following topics Major steps in ETL process, Data extraction: Techniques, Data transformation: Basic tasks, Major transformation types, Data Loading: Applying Data, OLTP Vs OLAP, OLAP definition, Dimensional Analysis, Hypercubes, OLAP operations: Drill down, Roll up, Slice, Dice and Rotation, OLAP models : MOLAP, ROLAP. Module Introduction to Data Mining, Data Exploration and Preprocessing contains the following topics Data Mining Task Primitives, Architecture, Techniques, KDD process, Issues in Data Mining, Applications of Data Mining, Data Exploration Types of Attributes, Statistical Description of Data, Data Visualization, Data Preprocessing: Cleaning, Integration, Reduction: Attribute subset selection, Histograms, Clustering and Sampling, Data Transformation & Data Discretization: Normalization, Binning, Concept hierarchy generation, Concept Description Attribute oriented Induction for Data Characterization. Module Classification, Prediction and Clustering: Basic Concepts, Decision Tree using Information Gain, Induction: Attribute Selection Measures, Tree pruning, Bayesian Classification: Naive Bayes, Classifier Rule Based Classification: Using IFTHEN Rules for classification, Prediction: Simple linear regression, Multiple linear regression Model Evaluation & Selection: Accuracy and Error measures, Holdout, Random Sampling, Cross Validation, Bootstrap, Clustering: Distance Measures, Partitioning Methods (k-Means, k-Medoids), Hierarchical Methods(Agglomerative, Divisive). Module Mining Frequent Patterns and Association Rules contains the following topics Market Basket Analysis, Frequent Item sets, Closed Item sets, and Association Rule, Frequent Pattern Mining, Efficient and Scalable Frequent Item set Mining Methods: Apriori Algorithm, Association Rule Generation, Improving the Efficiency of Apriori, FP growth, Mining frequent Itemsets using Vertical Data Format, Introduction to Mining Multilevel Association Rules and Multidimensional Association Rules. Module Spatial and Web Mining contains the following topics Spatial Data, Spatial Vs. Classical Data Mining, Spatial Data Structures, Mining Spatial Association and Co-location Patterns, Spatial Clustering Techniques: CLARANS Extension, Web Mining: Web Content Mining, Web Structure Mining, Web Usage mining, Applications of Web Mining.

In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis, and is considered a core component of business intelligence. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place that are used for creating analytical reports for workers throughout the enterprise. The data stored in the warehouse is uploaded from the operational systems (such as marketing or sales). The data may pass through an operational data store and may require data cleansing for additional operations to ensure data quality before it is used in the DW for reporting.Cryptography and System Security is semester 6 subject of computer engineering in Mumbai University. Prerequisite for studying this subject are Computer Organization. Objectives of course Cryptography and System Security is to introduce classical encryption techniques and concepts of modular arithmetic and number theory.

To explore the working principles and utilities of various cryptographic algorithms including secret key cryptography, hashes and message digests, and public key algorithms. To explore the design issues and working principles of various authentication protocols, PKI standards and various secure communication standards including Kerberos, IPsec, and SSL/TLS and email. To develop the ability to use existing cryptographic utilities to build programs for secure communication. Outcomes of course Cryptography and System Security are that at the end of the course learner will able to. Understand system security goals and concepts, classical encryption techniques and acquire fundamental knowledge on the concepts of modular arithmetic and number theory. Understand, compare and apply different encryption and decryption techniques to solve problems related to confidentiality and authentication. Apply the knowledge of cryptographic checksums and evaluate the performance of different message digest algorithms for verifying the integrity of varying message sizes.  Apply different digital signature algorithms to achieve authentication and design secure applications Understand network security basics, analyze different attacks on networks and evaluate the performance of firewalls and security protocols like SSL, IPSec, and PGP. Analyze and apply system security concept to recognize malicious code.

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Course Features

  • Lectures 135
  • Quizzes 0
  • Students 1
  • Certificate No
  • Assessments Yes


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