• Ideal candidates can be a computer science degree or equivalent experience and entering their first IT role with the focus of Data Science and AI.

Should include one or more years of business and/or tech experience and familiarity with the following technologies:

• Understanding of how computers work
• Basic programming experience with Python.

Course Outline:

Python Programming – Fundamentals

• • Set Up
    • Set up development environment – Jupyter notebooks
    • Using python shell
    • Running python script
    • Understanding python strings
    • Print statements in python
  • Data Structures in python
    • Integers
    • Lists
    • Dictionaries
    • Tuple
    • File
    • Mutable and Immutable structures
  • Selection and Looping Constructs
    • If/else/elif statements
    • Boolean type
    • “in” membership
    • For loop
    • While Loop
    • List and Dictionary Comprehension
  • Functions
    • Defining functions
    • Variable scope – Local and Global
    • Arguments
    • Polymorphisms
  • Modules
    • Creating modules
    • Importing Modules
    • Different types of imports
    • Dir and help
    • Examining some built-in modules
  • Classes Exceptions
    • Object Oriented Programming Introduction
    • Classes and Objects
    • Polymorphism – Function and Operator Overloading
    • Inheritance

    Data Science&rdquo

    • Overview of Data Science
    • The Difference Between Business Analytics (BI), Data Analytics and Data Science
    • The Field of Data Science
    • The Data Science Process
      • Define the Problem
      • Get the Data
      • Explore the Data
      • Clean the Data
      • Model the Data
      • Communicate the Findings
    • Identifying a problem and asking good questions

    Descriptive Statistics Fundamentals

    • Central Tendency
    • Mean
    • Median
    • Mode
  • Spread of the Data
    • Variance
    • Standard Deviation
    • Range
  • Relative Standing
    • Percentile
    • Quartile
    • Inter-quartile Range

    Inferential Statistics Fundamentals

    • Inferential Statistics
      • Normal Distribution
      • Central Limit Theorem
      • Standard Error
      • Confidence Intervals
      • Other Distributions
      • Samples
      • Hypothesis Testing
      • Perform statistical analysis on a given data set.

      Data Exploration and Preparation

      • Data Exploration
        • Describe
        • Merging
        • Grouping
        • Evaluating Features
        • Data Visualization
          • Line Chart
          • Scatterplot
          • Pairplot
          • Histogram
          • Density Plot
          • Bar Chart
          • Boxplot
          • Customizing Charts
        • Perform Exploratory Data Analysis
        • Data Cleaning
          • Dropping Rows
          • Imputing Missing Values
          • Feature Evaluating
          • Feature Engineering
          • Data Transformation
            • One-Hot Encoding
            • Standardization
            • Normalization
            • Test/Train Split
            • Model Training

            Machine Learning Overview

            • History and Background of AI and ML
            • Compare AI vs ML vs DL
            • Describe Supervised and Unsupervised learning techniques and usages
            • Machine Learning patterns
            • Classification
            • Clustering
            • Regression
            • Gartner Hype Cycle for Emerging Technologies
            • Machine Learning offerings in Industry
            • Discuss Machine Learning use cases in different domains
            • Understand the Data Science process to apply to ML use cases
            • Understand the relation between Data Engineering and Data Science
            • Identify the different roles needed for successful ML project

            Essential Python Data Science Libraries

            • Numpy
            • Pandas
            • Matplotlib
            • Scikit-learn
            • Statsmodels

            Machine Learning Algorithms

            • Linear Regression
            • Logistic Regression
            • Support Vector Machine
            • Decision Tree
            • K-Means
            • Clustering

            Model selection and tuning

            • Machine learning
            • K-fold cross validation
            • Box plot
            • Measuring performance
            • Model selection
            • Refining the model
            • Hyperparameter tuning
            • Grid search
            • Apply machine learning algorithms, select and refine the best model.

            Big Data Overview

            • History and background of Big Data and Hadoop
            • 5 V’s of Big Data
            • Secret Sauce of Big Data Hadoop
            • Big Data Distributions in Industry
            • Big Data Ecosystem before Apache Spark
            • Big Data Ecosystem after Apache Spark
            • Comparison of MapReduce Vs Apache Spark
            • Big Data Ecosystem after Apache Spark
            • Understand Apache Architecture and Libraries like Streaming, Machine & Deep Learning, GraphX etc.

            Machine learning using Apache Spark

            • Spark ML Overview
            • Introduction to Jupyter notebooks
            • Lab: Working with Jupyter + Python + Spark
            • Lab: Spark ML utilities
            • Simple Linear Regression
            • Multiple Linear Regression
            • Running LR
            • Evaluating LR model performance
            • Use case: House price estimates
            • Theory behind K-Means
            • Running K-Means algorithm
            • Estimating the performance
            • Use case: grouping

            Neural Networks and TensorFlow

            • Introduction to neural networks
              • The math behind neural networks
              • Activation functions
              • Vanishing gradient problem and ReLU
              • Loss functions
              • Gradient descent
              • Back propagation
            • Understanding the intuition behind neural networks
            • Introducing Perceptrons
              • Single Layer linear classifier
              • Step Function
              • Updating the weights
              • Linear separability and XOR problem
              • Hidden Layers: Intro to Deep Neural Networks and Deep Learning
              • Hidden Layers as a solution to XOR problem
              • The architecture of deep learning
              • Introducing Keras/TensorFlow
                • What is Keras?
                • Using Keras with a TensorFlow Backend
                • Lab: Using Keras to implement a neural network
                • Introducing TensorFlow
                  • TensorFlow intro
                  • TensorFlow Features
                  • TensorFlow Versions
                  • GPU and TPU scalability
                  • Lab: Setting up and Running TensorFlow
                  • The Tensor: The Basic Unit of TensorFlow
                    • Introducing Tensors
                    • TensorFlow Execution Model
                    • Lab: Learning about Tensors

                    Image Recognition

                    Convolutional Neural Networks in Keras/TensorFlow

                    • Introducing CNNs
                    • Convolution layer
                    • Pooling layer
                    • Fully connected layer
                    • CNNs in TensorFlow
                    • Lab: Image recognition

                    Image and Video Processing

                    • Image processing elements
                      • Convolutions
                      • Pooling
                      • Edge Detection
                      • De-noising
                    • Video Analysis
                      • Understanding Video
                      • OpenCV and Video
                      • Capturing Video from a Camera
                      • Using Video Files
                      • Optical Flow and Motion Estimation
                      • Deep Learning in Optical Flow Estimation
                      • Visual Object Tracking

                    Natural Language Processing

                    • What is NLP?
                      • Sensory Acuity
                      • Behavioral Flexibility
                      • NLP Techniques
                    • NLP and Deep Learning
                      • Word2vec
                      • Learning word embedding
                      • The Skip-gram Model
                      • Building the graph
                      • Training the model
                      • Visualizing the embeddings
                      • Optimizing the implementation
                      • Text classification with TensorFlow
                      • Automatic translation (seq2seq)
                      • Text generation with RNN
                      • Named entity extraction with RNNs (sequence modeling)
                      • Bidirectional LSTM with attention
                      • Natural Language Processing pipelines
                    • Conversational AI
                      • Introduction to the Rasa framework
                      • Generating natural language
                      • Understanding natural language
                      • Chatbots

                Machine Learning and AI on the Cloud

                Getting Started with GCP

                • • • Security with Google’s Cloud Infrastructure
                    • Understanding resource hierarchy
                    • IAM – Identity and Access Management
                    • Different IAM Roles
                    • Connecting to Google Cloud Platform

                Machine Learning on GCP

                • • • Understanding AI and Machine Learning
                    • Doing machine learning on GCP
                    • Understanding TensorFlow

                Machine Learning APIs on GCP

                • • • Machine Learning APIs
                    • Natural Language Processing
                    • Translation API
                    • Speech – Text to Speech, Speech to Text API
                    • Vision API
                    • Cloud AutoML
                    • BigQuery ML

                Certification

                • • • Certification Overview
                    • Identify the right certification for you
                    • Tips to prepare for certification

                Project Case

                • Project Overview
                • Performing Projects to get experience and practice
                • ML and AI Case Studies

                Training material provided:

                Yes (Digital format)
                Hands-on Lab: Instructions will be provided to create a free tier account on Azure.