Description

Duration

3 Days

18 CPD hours

This course is intended for

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

Overview

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

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 casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms

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

Statistics and Probability Refresher and Python Practice

Types of Data

Mean, Median, Mode

Using mean, median, and mode in Python

Variation and Standard Deviation

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

Predictive Models

Linear Regression

Polynomial Regression

Multiple Regression, and Predicting Car Prices

Logistic Regression

Logistic Regression

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

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

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

Reinforcement Learning

Reinforcement Learning with Q-Learning and Gym

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

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

Capstone Project

Group Project & Presentation or Review

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

Additional course details:

Nexus Humans Machine Learning Essentials with Python (TTML5506-P) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward.

This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts.

Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.

While we feel this is the best course for the Machine Learning Essentials with Python (TTML5506-P) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.

Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

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Machine Learning Essentials with Python (TTML5506-P)

By Nexus Human

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