Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices. This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use cases Working in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:

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* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

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.
Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below
Getting Started & Optional Python Quick Refresher
Statistics and Probability Refresher and Python Practice
Probability Density Function; Probability Mass Function; Naive Bayes
Predictive Models
Machine Learning with Python
Recommender Systems
KNN and PCA
Reinforcement Learning
Dealing with Real-World Data
Experimental Design / ML in the Real World
Time Permitting: Deep Learning and Neural Networks

1

  • Getting Started

  • Installation- Getting Started and Overview
    LINUX jump start- Installing and Using Anaconda & Course Materials (or reference the default container)
    Python Refresher
    Introducing the Pandas, NumPy and Scikit-Learn Library
2

  • Statistics and Probability Refresher and Python Practice

  • Types of Data
    Mean, Median, Mode
    Using mean, median, and mode in Python
    Variation and Standard Deviation
3

  • Probability Density Function; Probability Mass Function; Naive Bayes

  • Common Data Distributions
    Percentiles and Moments
    A Crash Course in matplotlib
    Advanced Visualization with Seaborn
    Covariance and Correlation
    Conditional Probability
    Naive Bayes- Concepts
    Bayes' Theorem
    Naive Bayes
    Spam Classifier with Naive Bayes
4

  • Predictive Models

  • Linear Regression
    Polynomial Regression
    Multiple Regression, and Predicting Car Prices
    Logistic Regression
    Logistic Regression
5

  • Machine Learning with Python

  • Supervised vs. Unsupervised Learning, and Train/Test
    Using Train/Test to Prevent Overfitting
    Understanding a Confusion Matrix
    Measuring Classifiers (Precision, Recall, F1, AUC, ROC)
    K-Means Clustering
    K-Means- Clustering People Based on Age and Income
    Measuring Entropy
    LINUX- Installing GraphViz
    Decision Trees- Concepts
    Decision Trees- Predicting Hiring Decisions
    Ensemble Learning
    Support Vector Machines (SVM) Overview
    Using SVM to Cluster People using scikit-learn
6

  • Recommender Systems

  • User-Based Collaborative Filtering
    Item-Based Collaborative Filtering
    Finding Similar Movie
    Better Accuracy for Similar Movies
    Recommending movies to People
    Improving your recommendations
7

  • KNN and PCA

  • K-Nearest-Neighbors- Concepts
    Using KNN to Predict a Rating for a Movie
    Dimensionality Reduction; Principal Component Analysis (PCA)
    PCA with the Iris Data Set
8

  • Reinforcement Learning

  • Reinforcement Learning with Q-Learning and Gym
9

  • Dealing with Real-World Data

  • Bias / Variance Tradeoff
    K-Fold Cross-Validation
    Data Cleaning and Normalization
    Cleaning Web Log Data
    Normalizing Numerical Data
    Detecting Outliers
    Feature Engineering and the Curse of Dimensionality
    Imputation Techniques for Missing Data
    Handling Unbalanced Data- Oversampling, Undersampling, and SMOTE
    Binning, Transforming, Encoding, Scaling, and Shuffling
10

  • Experimental Design / ML in the Real World

  • Deploying Models to Real-Time Systems
    A/B Testing Concepts
    T-Tests and P-Values
    Hands-on With T-Tests
    Determining How Long to Run an Experiment
    A/B Test Gotchas
11

  • Capstone Project

  • Group Project & Presentation or Review
12

  • Deep Learning and Neural Networks

  • Deep Learning Prerequisites
    The History of Artificial Neural Networks
    Deep Learning in the TensorFlow Playground
    Deep Learning Details
    Introducing TensorFlow
    Using TensorFlow
    Introducing Keras
    Using Keras to Predict Political Affiliations
    Convolutional Neural Networks (CNN's)
    Using CNN's for Handwriting Recognition
    Recurrent Neural Networks (RNN's)
    Using an RNN for Sentiment Analysis
    Transfer Learning
    Tuning Neural Networks- Learning Rate and Batch Size Hyperparameters
    Deep Learning Regularization with Dropout and Early Stopping
    The Ethics of Deep Learning
    Learning More about Deep Learning

Audience

This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts

Language

English

Prerequisites

TTPS4800 Introduction to Python (3 days)

$2,195

Length: 3.0 days (24 hours)

Level:

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