Machine Learning Specialist Certificate

1.Learning Methodology

• Instructor-Led Classroom Training (ILT).

2.Prerequisites:

• Basic skills with at least one programming language are desirable
• Familiar with the basic math and statistic concepts

3.Training Program Description:

• AI is revolutionizing the way we live, work and communicate. At the heart of AI is Machine Learning. Once a domain of researchers and  PhDs only, Machine Learning has now gone mainstream thanks to its practical applications and availability in terms of consumable technology and affordable hardware.
• The demand for Machine Learning professionals is booming, far exceeding the supply of personnel skilled in this field. The industry is clearly embracing AI, embedding it within its fabric. The demand for Machine Learning skills by employers — and the job salaries of       Machine Learning practitioners — are only bound to increase over time, as AI becomes more pervasive in society. Machine Learning is a future-proof career.
• Throughout this program, you will practice your Machine Learning skills through a series of hands-on labs, assignments, and projects inspired by real-world problems and data sets from the industry. You’ll also complete the program by preparing a Machine Learning capstone project that will showcase your applied skills to prospective employers.
• This program is intended to prepare learners and equip them with the skills required to become successful AI practitioners and start a career in applied Machine Learning.

• In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed!
• Duration of Program: 8 weeks

• Who is this class for?
  • This course is primarily for individuals who are passionate about the field of data science and data analysts and who are aspiring to apply machine learning in their business, industry or research.
  • Developers and Software Engineers
  • Analytics Managers and Professionals
  • Statisticians with an interest in Machine

What you will learn

• Use Python to access and analyze data from several different data sources.
• Build predictive models using a variety of unsupervised and supervised machine learning techniques.
• Perform feature engineering to improve the performance of machine learning models.
• Optimize, tune, and improve algorithms according to specific metrics like accuracy and speed.
• Compare the performances of learned models using suitable metrics.
• concepts of Machine Learning, including supervised and unsupervised learning.
• Use of popular Machine Learning libraries applied to industry problems.
• Application of Machine Learning to real-world scenarios

4.Projects

•  This program is comprised of many career-oriented projects. Each project you build will be an opportunity to demonstrate what you’ve learned in the lessons. Your completed projects will become part of a career portfolio that will demonstrate to potential employers that you have skills in data analysis and feature engineering, machine learning algorithms, and training and evaluating models.
• One of our main goals at EAII is to help you create a job-ready portfolio of completed projects. Building a project is one of the best ways to test the skills you’ve acquired and to demonstrate your newfound abilities to future employers or colleagues. Throughout this program, you’ll have the opportunity to prove your skills by building the following projects
•  Building a project is one of the best ways both to test the skills you’ve acquired and to demonstrate your newfound abilities to future employers. Throughout this program, you’ll have the opportunity to prove your skills by building the following projects:

• Project 1: Exploring the Titanic Survival Data
• Project 2: Housing Price Prediction
• Project 3: Market Segmentation
• Project 4: Credit Card Fraud Detection Prediction
• Project 5: Movie Recommendation Engine
• Project 6: Human Activity Recognition
• Project 7: Kaggle Competition Project
• Capstone Project

5.Training Program Curriculum:

I-PYTHON 3 TOPICS

• INTRODUCTION
  • SETTING UP PYTHON
  • SYNTAX
  • FIRST PYTHON PROGRAM
  • VARIABLES & DATA TYPES
  • NUMBERS AND MATH
  • STRINGS
  • OPERATORS AND BITWISE
  • DATA STRUCTURES OF PYTHON
    • TUPLES
    • LISTS
    • SETS
    • DICTIONARIES
  • CONDITIONAL LOGIC AND CONTROL FLOW
  • FUNCTIONS AND LOOPS
• OBJECT-ORIENTED PROGRAMMING (OOP)
  • CLASSES
  • INHERITANCE
  • PYTHON GENERATORS
  • PYTHON DECORATORS

II-DATA ANALYSIS TOPICS

• DATA ANALYSIS
  • ANACONDA / JUPYTER NOTEBOOKS
  • DATABASE WITH SQLITE
  • NUMPY AND MATRIX OPERATIONS
  • PANDAS
  • DATA VISUALIZATION

III-MACHINE LEARNING TOPICS

• LINEAR ALGEBRA
  • VECTORS
  • MATRICES
  • OPERATIONS ON MATRICES
  • DOT PRODUCT
  • EIGEN VALUES AND EIGEN VECTORS
• CALCULUS
  • FUNCTIONS
  • DERIVATIVES AND GRADIENTS
  • STEP FUCNTION, SIGMOID FUNCTION, RELU
  • COST FUNCTION
  • MINIMUN AND MAXIMUM VALUES
• STATISTICS AND PROBABILITY
  • DESCRIPTIVE STATISTICS
    • INTRODUCTION
    • SAMPLING TECHNIQUES
    • MEASURES OF CENTRAL TENDENCY
    • MEASURES OF VARIABILITY
    • SKEWNESS AND OUTLIERS
  • INFERENTIAL STATISTICS
    • T-TEST AND ANOVA
    • CHI-SQUARE TEST
    • SPEARMAN CORRELATION COEFFICIENT
    • PEARSON CORRELATION COEFFICIENT
    • REGRESSION ANALYSIS
  • PROBABILITY
    • PROBABILITY LAWS
    • BAYESIAN THEOREM
    • PROBABILITY DISTRIBUTION
    • GAUSSIAN DISTRIBUTION
    • SAMPLING DISTRIBUTION
    • CENTRAL LIMIT THEOREM
  • INTRODUCTION TO ML AND BUSINESS CASES
    • THE DIFFERENCE BETWEEN ML, BIG DATA, DATA ANALYSIS AND DEEP LEARNING
  • DATA PREPROCESSING
    • IMPORTING LIBRARIES
    • DATA ACQUISITION
    • DATA CLEANING
    • HANDLING MISSING DATA
    • CATEGORICAL DATA
    • DATA SPLITTING
    • FEATURE SCALING
  • REGRESSION PROBLEM
    • LINEAR REGRESSION
    • POLYNOMIAL REGRESSION
    • REGRESSION EVALUATION METRICS
  • CLASSIFICATION PROBLEM
    • LOGISTIC REGRESSION
    • NAIVE BAYES
    • K-NEAREST NEIGHBOUR CLASSIFIER
    • SUPPORT VECTOR MACHINE (SVM)
    • DECISION TREE CLASSIFIER
    • ENSEMBLE LEARNING
    • CLASSIFICATION EVALUATION METRICS
  • INTRO TO BUILDING MACHINE LEARNING API
  • CLUSTERING PROBLEMS
    • DIMENSIONALITY REDUCTION
    • K-MEANS
    • HIERARCHICAL CLUSTERING
    • ASSOCIATION RULES
    • CLUSTERING EVALUATION
  • MODEL SELECTION AND EVALUATION
    • CROSS-VALIDATION
    • HYPERPARAMETER TUNING
  • RESULT COMMUNICATION AND REPORT
  • INTRODUCTION TO DEEP LEARNING / NLP / COMPUTER VISION
    • INTRO TO DEEP LEARNING WITH KERAS
      • INTRO TO ANN – ARTIFICIAL NEURAL NETWORK
      • INTRO TO CNN _CONVOLUTION NEURAL NETWORK
      • INTRO TO RNN_RECURRENT NEURAL NETWORK
      • INTRO TO AUTOENCODER
    • INTRO TO REINFORCEMENT LEARNING / INVERSE REINFORCEMENT LEARNING
    • INTRO TO NLP
    • INTRO TO COMPUTER VISION

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MACHINE LEARNING COURSE – EAII