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Start Date

May 2023

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Duration

3 Months

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What is this course about?

The purpose of this course is to enable learners to develop an in-depth understanding of the machine learning algorithms, principals, and techniques with a special focus on the underlying mathematics and statistics. This course will enable the learners to develop hands-on skills in the implementation of various machine learning algorithms. The learner will be able to analyze a real-world problem, design a solution and implement it using an appropriate machine learning algorithm(s).

Learning Outcomes:
On successful completion of this course the student will be able to:
  • Understand and appreciate the underlying issues and challenges of machine learning algorithms for implementation and application in challenging real-world scenarios.
  • Analyze and relate the mathematical and statistical background of machine learning models for finding optimal solutions for the industry.
  • Implement various supervised and unsupervised machine learning algorithm for dynamic real-world problems.
  • Evaluate the results of a model and conclude the possible steps for evolving them into a usable application.

Become an Machine Learning
Expert in just 3 Months

Course Curriculum

Here is what’s included in this Machine Learning Course
Week 1: What is Machine Learning?

  • Supervised learning
  • Unsupervised learning

Week 2: Linear Regression

    • Cost function
    • Gradient Descent (single and multi-variables)
    • Normal equation

Week 3: Logistic Regression

  • Classification (Binary)
  • Hypothesis representation
  • Decision Boundary
  • Cost Function
  • Optimization
  • Classification (Multi-Class)

Week 4: Model Generalization

  • Data fitting (over fitting vs under fitting)
  • Regularized Linear Regression
  • Regularized Logistic Regression

Week 5: Support Vector Machine

  • Optimization
  • Margin classification
  • Kernels
  • K-means

Role of Consensus in blockchain networks.

  • What is consensus algorithm and why it is necessary.
  • Introduction to different consensus algorithms PoW, PoS, DPoS, PoH, etc.
  • Practical Byzantine Fault Tolerance.

Ethereum – first programable blockchain network.

  • What’s unique about Ethereum networks.
  • Ethereum v1 and V2
  • What’s new in Ethereum v2

Week 6: Dimensionality reduction and Anomaly detection

    • Why to reduce the dimension of a problem
    • Principal component Analysis
    • Principal component selection (Number of PC)
    • Gaussian Distribution
    • Anomaly detection system
    • Multivariate Gaussian distribution

Week 7: Neural Networks

    • Why we need Neural networks (Non-Linear Hypotheses)
    • Model Representation
    • Multiclass Classification
    • Back-propagation algorithm
    • Random initialization
    • Model selection
    • Accuracy Measures(precision and recall)

Week 8: Recommender systems

    • Content Based recommendation
    • Collaborative filtering
    • Vectorization
    • Mean normalization

Week 9: Machine learning for large datasets

  • Stochastic gradient descent
  • Convergence of SGD
  • Introduction to MapReduce

Week 10: Project

  • Project work

Entry Requirements

Basic knowledge of Python programming language is required for this course

Meet Our Trainers

Our experienced Blockchain expert is here to help you understand the concepts of blockchain to apply them practically for your successful career
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Engr. Anjum Nazir

Expertise:
Cyber Security Architect, Blockchain, Linux/DevOps
Doctor of Philosophy - PhD, Computer and
Information Systems Security/Information Assurance
NUCES

Programming languages and tools

Benefits of the course

Conduct specialized research to advance current technologies

Execute Industry-specific data mining and data analysis to create more value

Understand Software design and information architecture

Sign up now for more information

Sign up now for more information

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