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Introduction to Artificial Intelligence (AI) & Machine Learning (AI & ML JumpStart) is a three-day, foundation-level, hands-on course that explores the fast-changing field of artificial intelligence (AI) programming, logic, search, machine learning, and natural language understanding.

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March 3, 2021

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This “skills-centric” course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. In this course you will learn about:

· Developers aspiring to be a 'Data Scientist' or Machine Learning engineers

· Analytics Managers who are leading a team of analysts

· Business Analysts who want to understand data science techniques

· Information Architects who want to gain expertise in Machine Learning algorithms

· Analytics professionals who want to work in machine learning or artificial intelligence

· Graduates looking to build a career in Data Science and machine learning

· Experienced professionals who would like to harness machine learning in their fields to get more insight about customers

Course Curriculum

    • Installing a Python Data Science Environment 00:00:00
    • Using and understanding IPython (Jupyter) Notebooks 00:00:00
    • Python basics – Part 1 00:00:00
    • Understanding Python code 00:00:00
    • Importing modules 00:00:00
    • Python basics – Part 2 00:00:00
    • Running Python scripts 00:00:00
    • Types of data 00:00:00
    • Mean, median, and mode 00:00:00
    • Using mean, median, and mode in Python 00:00:00
    • Standard deviation and variance 00:00:00
    • Probability density function and probability mass function 00:00:00
    • Types of data distributions 00:00:00
    • Percentiles and moments 00:00:00
    • A crash course in Matplotlib 00:00:00
    • Covariance and correlation 00:00:00
    • Conditional probability 00:00:00
    • Bayes’ theorem 00:00:00
    • Data Prep 00:00:00
    • Linear Algorithms – Simple Linear Algorithms – Multivariate Linear Regression – Logistic Regression – Perceptrons 00:00:00
    • Non-Linear Algorithms – Classification Trees (CARTs) – Naive Bayes – k-Nearest Neighbors 00:00:00
    • Ensembles – Bootstrap Aggregation – Random Forest 00:00:00
    • Linear regression 00:00:00
    • Polynomial regression 00:00:00
    • Multivariate regression and predicting car prices 00:00:00
    • Multi-level models 00:00:00
    • Machine learning and train/test 00:00:00
    • Using train/test to prevent overfitting of a polynomial regression 00:00:00
    • Bayesian methods – Concepts 00:00:00
    • Implementing a spam classifier with Naïve Bayes 00:00:00
    • K-Means clustering – Clustering people based on income and age – Measuring entropy – Decision trees – Concepts – Decision trees – Predicting hiring decisions using Python – Ensemble learning – Support vector machine overview – Using SVM to cluster people by using scikit-learn 00:00:00
    • What are recommender systems? 00:00:00
    • Item-based collaborative filtering 00:00:00
    • How item-based collaborative filtering works? 00:00:00
    • Finding movie similarities 00:00:00
    • Improving the results of movie similarities 00:00:00
    • Making movie recommendations to people 00:00:00
    • Improving the recommendation results 00:00:00
    • K-nearest neighbors – concepts 00:00:00
    • Using KNN to predict a rating for a movie 00:00:00
    • Dimensionality reduction and principal component analysis 00:00:00
    • A PCA example with the Iris dataset 00:00:00
    • Data warehousing overview 00:00:00
    • Reinforcement learning 00:00:00
    • Bias/variance trade-off 00:00:00
    • K-fold cross-validation to avoid overfitting 00:00:00
    • Data cleaning and normalization 00:00:00
    • Cleaning web log data 00:00:00
    • Normalizing numerical data 00:00:00
    • Detecting outliers 00:00:00
    • Installing Spark 00:00:00
    • Spark introduction 00:00:00
    • Spark and Resilient Distributed Datasets (RDD) 00:00:00
    • Introducing MLlib 00:00:00
    • Decision Trees in Spark with MLlib 00:00:00
    • K-Means Clustering in Spark 00:00:00
    • TF-IDF 00:00:00
    • Searching Wikipedia with Spark MLlib 00:00:00
    • Using the Spark 2.0 DataFrame API for MLlib 00:00:00
    • A/B testing concepts 00:00:00
    • T-test and p-value 00:00:00
    • Measuring t-statistics and p-values using Python 00:00:00
    • Determining how long to run an experiment for 00:00:00
    • A/B test gotchas 00:00:00
    • Build a UI for your Models 00:00:00
    • Build a REST API for your Models 00:00:00

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