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.