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.