ml

Machine Learning Engineer: Become an ML Practitioner

  • 15 Case Studies
  • 22 Live: Programming Examples
  • 10 Quizzes
  • 15 Tests: Programming Projects
  • 2 Complete Real-world Applications

Instructor: Dr. Raju Pandey

https: //academy.izen.ai

Email: info@izen.ai

NASSCOM FutureSkills Ecosystem Partner

Certificate from Webster University

The world is undergoing significant digital‐transformations, drastically changing the way we conduct business and carry out daily transactions. We are in the cognitive era. Enterprises and even nations are vying for a leadership position in AI/ML technology and applications.

With a great deal of attention given to Machine Learning (ML) and a wide spectrum of applications in real life, many are beginning to seek a better understanding of Machine Learning and the many benefits it offers to organizations globally. Machine Learning (ML) and Deep Learning (DL), and Natural Language Processing (NLP) are at the forefront of major innovations, some examples being image identification, marketing campaign customization, genomics, self-driving car navigation, and thousands of other applications from every possible domain.

HIGH DEMAND
Corporations are aggressively adapting to AI wave and hence hiring a large number of ML engineers.

HUGE SUPPLY GAP
Demand for qualified ML engineers far exceeds supply. That means less competition and more pay for trained-experts.

BRIGHT FUTURE
According to World Economic Forum, automation will generate 133 million new jobs by 2022 and these jobs are not going away anytime soon

This is an “Outcome-based Machine Learning course” with the primary focus on employability. At the end of this course, you will be able to analyze and transform data of different kinds, design and implement scalable applications using a wide variety of Machine Learning algorithms, analyze the performance characteristics of these applications, re-engineer them so that they meet performance requirements, and deploy them in a production environment. The course will build a solid technical foundation in Machine Learning algorithms, train in the state-of-the-art Machine Learning tools, and develop deep engineering expertise in building and analyzing real-world applications.

  • Kick-start or re-ignite your career by completing this course. The salaries of ML Engineers will only keep rising as the world can’t keep up with the enormous demand
  • Kick-start or re-ignite your career by completing this course. The salaries of ML Engineers will only keep rising as the world can’t keep up with the enormous deman
  • Learn by doing:
    1. Get hands-on experience by doing several industry-specific projects
    2. Access our “AI-Experience-Lab” on the cloud to learn by doing
  • Real-life case-studies and use-cases to give you practical insights
  • Get a certificate as “Machine Learning Engineer”
  • If you are a student seeking employment to kickstart your career, this is a great opportunity for you to learn ML and build a rewarding, future-proof, and meaningful caree
  • If you are already employed but are trying to rekindle your career in the exciting world of AI/ML, this program is perfect for you
  • Regardless of your background, this program can give you thought leadership in machine learning adoption, tools, techniques, and hands-on implementation

PREREQUISITES:

  • Basic knowledge of programming
  • Basic understanding of statistics would be helpful, though not mandatory

Machine Learning (ML) has become a foundational tool for building the next generation of applications in industries starting from healthcare to financials to retail. No industry or sector is untouched by the potential of Machine Learning algorithms & tools. This course is an in-depth practitioner’s program in Machine-Learning.

  • This course will train students in ML algorithms, data analysis techniques, performance analysis techniques, and ML development tools
  • The course will expose students to a wide variety of ML applications and how they can be implemented using different ML algorithms and tools
  • The emphasis will be on how the students can engineer ML applications and systems, analyze them, and improve their performance
  • This is a very hands-on course in that students will build several ML applications as a part of course projects, using Python
  • Upon completing this course, you will be ready for an ML-Engineer job and to apply your knowledge for using ML in your organization:
    1. You will know how to collect, prepare and analyze data for building ML applications
    2. Based on data and application characteristics, you will be able to select a suite of algorithms that is suitable for your application
    3. You will know how to use ML-tools to implement these algorithms
    4. You will be able to analyze your implementation for its performance, optimize the implementation and deploy in production environment

This certificate program consists of 2 main courses, to help you develop expertise in Machine Learning.

Course-1: Foundations of AI, Data-Science, Stat, Python and Tools for ML

Python has become the de facto standard for developing ML applications. This course provides a system and design-driven view of Python, data analysis, and visualization tools, and the ML learning framework. This course will also provide the necessary background on statistics.

  • Natural intelligence and Types of Intelligence
  • Definitions
  • Types and Goals of AI
  • History of AI
  • Elements and classification of AI
  • Applications of AI and Why should you learn AI?
  • Natural Learning and Machine Learning
  • ML Vs
  • Traditional Systems
  • Evolution
  • Implications and Future of AI
  • Matrices
  • Functions and Data Analysis
  • Data and Types
  • Control Flow
  • Functions
  • Object Oriented Programming
  • Modules & Packages
  • Representation of data as Matrices
  • Vectors and Sequences
  •  Indexing & Slicing
  • Transformation
  • Operations on Arrays
  • Read and store data in different formats (CSV, XLS, TXT, JSON, etc).
  • Series and Data Frames
  • Indexing
  • Operations
  • Handling Unknowns
  • Sorting
  • Storage
  • Introduction to Data visualization
  • Charts
  • 3D Plots and Contours
  • Introduction to ML-framework
  • Estimators
  • Dataset libraries
  • Basics and Types of Data
  • Data Preparation
  • Data Preparation
  • Categorical Data
  • Data Normalization
  • Data Analysis
  • Feature Engineering
  • Cost and Error Analysis
Course-2: Machine Learning Engineer

Machine Learning enables predictions based on data. This course will provide an overview of Machine Learning, the different forms of predictions and associated applications, classes of algorithms, and software architecture of different Machine Learning systems and applications.

This module will provide a high-level view of what machine learning is, and how it is being used to build novel applications.
  • Machine Learning: An overview
  • Machine Learning Models and Applications
  • Machine Learning System Architecture
  • Machine Learning Challenges
Discuss the intuition behind regression algorithms, how they enable predictions, and analysis techniques.
  • Introduction
    1. Regression
    2. Notation and Conventions
    3. Solution Framework
  • Univariate Regression
    1. Linear Regression
    2. Gradient Descent Algorithm
  • Regression Metrics
  • SciKit Regression Model
    1. SciKit for Linear Regression
    2. Case study 1: Horizontal Line
    3. Case study 2: Predict Fahrenheit Equation
    4. Case study 3: Predict Height of Building
    5. Case study 4: Predict House Prices (Univariate Model)
  • Multivariate Regression
    1. Model
    2. GDA for multi-feature applications
    3. Normal Equation
    4. Case study 1: Predict House Prices (Multivariate Model)
    5. Case study 2: Predict House Prices (Multivariate Model with Scaling)
  • Polynomial Regression:
    1. Model
    2. Case study: Predict House Prices (Polynomial Regression)
  • Bias and Variance
  • Regression with Regularization
    1. Regularization with SciKit
    2. Case study: Predict House Prices (Regularization)
  • Introduction
    1. Classification schemes: Single Class, Multi-Class, Multi-label and Multi-output classifications
    2. Metrics
    3. Classification vs. Regression
    4. Multi-class Classification
  • Logistic Regression (Classification)
    1. Introduction
    2. Metrics
    3. Cost Function
    4. Gradient Descent Algorithm
    5. Logistic Regression Model (SciKit)
      1. Case Study 1: Classification Model for IRIS Data Set
      2. Case Study (Live Lab) 2: Classification of MNIST images
    6. Summary and Assumptions
  • Support Vector Machines (Classification)
    1. Introduction
    2. SVM Cost Model: Distance Constraint, Hard Constraint and Soft Constraint
    3. Gradient Descent Algorithm for SVM
    4. SVM Kernels
    5. SciKit SVM Model
    6. Case study 1: Classification Model for IRIS Data Set
    7. Case study 2: Classification Model for Cancer Tumor Data Set
  • Decision Tree (Classification)
    1. Introduction
    2. Classification Using Decision Tree
    3. Decision Tree Algorithm
    4. SciKit Decision Tree Model
    5. Case study: Classification Model for Diabetes Data Set
  • Introduction to Clustering
  • K-Means:
    1. Algorithm
    2. Algorithm Analysis
    3. SciKit Clustering Model
    4. Case Study 1: Clustering of IRIS Flower Dataset
    5. Case Study 2: Image Compression
  • Hierarchical Clustering:
    1. Algorithm
    2. SciKit Clustering Model
    3. Case Study: Hierarchical Clustering of Digits Image Dataset
  • Feature Engineering: Dimensionality Reduction (PCA)
    1. Introduction to Principal Component Analysis (PCA)
    2. PCA using Eigen Vectors and Eigen Values
    3. Singular Value Decomposition
    4. SciKit Model for Dimensionality Reduction
      1. Case Study 1: PCA on Synthetic Dataset
      2. Case Study 2: PCA for Image Compression
      3. Case Study 3: PCA for Noise Reduction
  • Validation and Testing
    1. Evaluation Methods
    2. Testing ML applications
    3. Validating ML Applications:
      1. Random Subsampling
      2. Leave 1 or p-out Cross Validation
      3. K-Fold Cross Validation
    4. SciKit Model for Cross Validation
  • Ensemble Learning
    1. Introduction
    2. Bagging
      1. SciKit Model for Bagging
      2. Case Study: Using Bagging Classifiers and Regressors on Synthetic Datasets
    3. Boosting
      1. SciKit Model for Boosting
      2. Case Study: Using Boosting Classifier sand Regressors on Synthetic Datasets
    4. Random Forest
      1. SciKit Model for Random Forests
      2. Case Study: Using Random Forest Classifiers and Regressors on Synthetic Datasets
    5. Stacking
      1. SciKit Model for Stacking
  • Application 1: Sentiment Analysis
    1. Introduction
    2. Input Data (Text) Analysis and Transformation
    3. Feature Engineering
    4. Case Study: Sentiment Analysis of Movie Review Data
  • Application 2: Stock Prices Prediction
    1. Introduction to Time Series Data
    2. Support for Time Series Data in Python and Pandas
    3. Analysis of Time Series Data
    4. Prediction Algorithms
      1. Traditional Regression Algorithms (Linear, SVM, Decision Tree, etc.)
      2. ARIMA
      3. Case Study: Prediction of Apple Stock Prices
PROGRAM FACULTY

Dr. Raju Pandey is a Professor Emeritus in the Computer Science department at the University of California at Davis, where he developed and taught graduate and undergraduate courses in programming languages, operating systems, distributed systems, Internet of Things, Wireless sensor networks, Web-based systems, and compilers. He is also the CEO and founder of Thinking Books, a software Infrastructure and Tools company. Dr. Pandey has a deep interest in math and computer science education and has developed novel interactive methods and tools for teaching both algorithmic and system aspects of Computer Science courses.

  • Dr. Pandey’s first startup, SynapSense, was a pioneering IoT company, later acquired by Panduit.
  • His research and entrepreneurial interests lie in AI, Programming Languages, Blockchain, Internet of Things, Cloud, Security, and Privacy. Specifically, his interests are driven by the need to build software systems that are easier to build, analyze and deploy.
  • In this regard, he has developed a novel software platform for building multi-platform AI, Blockchain, Mobile, and IoT applications. The platform includes a next-generation programming language, Ankur, that Dr. Pandey has designed and implemented. The platform will enable development of AI applications in which both algorithm-driven (deterministic) and data-driven (non-deterministic) components of AI applications can be integrated seamlessly.
  • In addition, he consults extensively with companies on AI, Blockchain, IoT, Cloud, Mobile Computing, and Distributed Systems.
  • He has published 40+ papers in conferences and journals and holds 16+ patents in software, visualization, wireless networks, data analytics, security, and control systems.
  • Dr. Pandey holds a B.Tech. degree in Computer Science from IIT (Indian Institute of Technology), Kharagpur, and Ph.D. in Computer Science from the University of Texas at Austin.
  • Online using desktop, laptop or mobile devices
  • Learn at your own convenient time, and pace
  • Video lectures delivered from a cloud LMS platform
  • Quizzes are given remotely
  • Hands-on projects, and industry case studies for the reinforcement of the learning
  • 12 weeks, around 10 hours per week, or a total of 120 hours
  • Rolling enrolment allows you to start any time. The duration can be aligned to your requirements

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