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Prerequisites:

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

 

Training Program Description:

  • Build expertise in data manipulation, visualization, predictive analytics, machine learning, and data science. With the skills you learn in a program, you can launch or advance a successful data career. Start acquiring valuable skills right away, create a project portfolio to demonstrate your abilities, and get support from mentors, peers, and experts in the field.
  • The demand for Machine Learning and Data Science 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 And Data science skills by employers — and the job salaries of Machine Learning and Data Science practitioners — are only bound to increase over time, as AI becomes more pervasive in society. Machine Learning and Data Science are a future-proof career.
  • Gain real-world data science experience with projects designed by industry experts. Build your portfolio and advance your data science and machine learning career.
  • Throughout this program, you will practice your Data Science and 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 Data Science and Machine Learning capstone project that will showcase your applied skills to prospective employers.

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: Predicting Housing Prices
  • Project 3: Creating Customer Segments
  • Project 4: Credit Card Fraud Detection
  • Project 5: Design a Recommendation Engine
  • Project 6: Intro to Big Data Project with Spark
  • Project 7: freelance Projects (Kaggle Competitions)
  • Capstone Project

 

program outcomes:

  • Use Python and SQL 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.
  • demonstrate critical thinking skills in the field of data analytics.
  • analyze, design and document a system component using appropriate data analysis techniques and models.
  • demonstrate the ability to incorporate various data analytics elements.
  • demonstrate an understanding of the fundamental principles of data analytics systems and technologies.
  • Able to use standard techniques of mathematics, probability, and statistics in order to address problems typical of a career in data science
  • Apply appropriate modeling techniques to conduct quantitative analyses of complex big data sets
  • Use statistical software packages such as Python to solve data science problems
  • Know standard resources that can be used to stay up to date with current data science issues, practices, and professional developments
  • Summarize and communicate, orally and in writing, data science problems to specialized and non-specialized audiences
  • Communicate results effectively to stakeholders.
  • Use principles of statistics and probability to design and execute A/B tests and recommendation
  • engines to assist businesses in making data-automated decisions.

 

Program Duration: 12 Weeks

Program Language: English / Arabic

Location: EPSILON AI INSTITUTE | Head Office

Participants will be granted a completion certificate from Epsilon AI Institute, USA if they attend a minimum of 80 percent of the direct contact hours of the Program and after fulfilling program requirements (passing both Final Exam and Project to obtain the Certificate)

 

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-SQL TOPICS

    • BASIC SQL
    • SQL JOINS
    • SQL AGGREGATIONS
    • SQL QUERIES

 

III-DATA ANALYSIS TOPICS

  • DATA ANALYSIS
    • ANACONDA / JUPYTER NOTEBOOKS
    • NUMPY AND MATRIX OPERATIONS
    • PANDAS
    • DATA VISUALIZATION
  • DATA WRANGLING
    • INTRO TO DATA WRANGLING
    • GATHERING DATA
    • ASSESSING DATA
    • CLEANING DATA
    • POWER BI & EXCEL

 

IV-MACHINE LEARNING TOPICS

  • LINEAR ALGEBRA
    • VECTORS
    • MATRICES
    • OPERATIONS ON MATRICES
    • DOT PRODUCT
    • EIGEN VALUES AND EIGEN VECTORS
  • CALCULUS
    • FUNCTIONS
    • DERIVATIVES AND GRADIENTS
    • STEP FUNCTION, SIGMOID FUNCTION, RELU
    • COST FUNCTION
    • MINIMUM 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 DS AND BUSINESS CASES
      • THE DIFFERENCE BETWEEN ML, BIG DATA, DATA ANALYSIS, DATA SCIENCE 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 RULE
    • DBSCAN CLUSTERING
    • CLUSTERING EVALUATION
  • MODEL SELECTION AND EVALUATION
    • LOSS FUNCTIONS
    • GRADIENT DESCENT
    • BIAS-VARIANCE TRADEOFF
    • CROSS-VALIDATION
    • HYPERPARAMETER TUNING
  • RESULT COMMUNICATION AND REPORT
  • INTRODUCTION TO DEEP LEARNING / NLP / COMPUTER VISION
    • INTRODUCTION TO NEURAL NETWORKS
    • NEURAL NETWORKS IMPLEMENTATION
    • 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 DEEP REINFORCEMENT LEARNING
    • INTRO TO NLP/COMPUTER VISION

 

V-DATA SCIENTIST TOPICS

  • SOLVING PROBLEMS WITH DATA SCIENCE
    • THE DATA SCIENCE PIPELINE
    • COMMUNICATING WITH STAKEHOLDERS
  • DATA ENGINEERING FOR DATA SCIENTISTS
    • ETL PIPELINES
    • MACHINE LEARNING PIPELINES
  • DESIGN A RECOMMENDER SYSTEM
    • COLLABORATIVE-FILTERING
    • CONTENT-BASED
  • EXPERIMENT DESIGN
    • EXPERIMENT DESIGN
    • A/B TESTING
  • DATA LAKE VS DATA WAREHOUSE
  • INTRODUCTION TO BIG DATA WITH SPARK AND INTEGRATION WITH ML

 

 

Download DATA SCIENTIST & MACHINE LEARNING WITH PYTHON SPECIALIST CERTIFICATE Brochure PDF

Course Curriculum

PYTHON 3 TOPICS
INTRODUCTION
OBJECT-ORIENTED PROGRAMMING (OOP)
SQL TOPICS
DATA ANALYSIS TOPICS
DATA ANALYSIS
DATA WRANGLING
MACHINE LEARNING TOPICS
LINEAR ALGEBRA
CALCULUS
STATISTICS AND PROBABILITY
INTRODUCTION TO DS AND BUSINESS CASES
DATA PREPROCESSING
REGRESSION PROBLEM
CLASSIFICATION PROBLEM
INTRO TO BUILDING MACHINE LEARNING API
CLUSTERING PROBLEMS
MODEL SELECTION AND EVALUATION
RESULT COMMUNICATION AND REPORT
INTRODUCTION TO DEEP LEARNING / NLP / COMPUTER VISION
DATA SCIENTIST TOPICS
SOLVING PROBLEMS WITH DATA SCIENCE
DATA ENGINEERING FOR DATA SCIENTISTS
EXPERIMENT DESIGN
DATA LAKE VS DATA WAREHOUSE
INTRODUCTION TO BIG DATA WITH SPARK AND INTEGRATION WITH ML

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