Course description
Machine Learning Essentials With Python
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:
- Popular machine learning algorithms, their applicability and limitations
- Practical application of these methods in a machine learning environment
- Practical use cases and limitations of algorithms
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
NOTE: Students who want a more math-centric, deeper dive into detailed statistics and algorithm review might consider this course as an alternative: TTML5504 Machine Learning Foundation: Working with Statistics, Algorithms and Neural Networks.
Trivera offers hundreds of end-to-end skills-focused courses that provide participants with the job-ready skills they require to be truly productive in a modern IT business enterprise. Our courses are available for individuals, their teams, or across their organization, for students of all skill levels and roles. We offer an extensive online Public Course Schedule, deep catalog for Private Courses, flex-hour Mini-Camp short courses, self-paced QuickSkills courses, free webinars and more. Trivera’s unique EveryCourse Extras and AfterCourse Extras programs, included with every course, ensure our students can put their newly-learned skills right to work, while providing them with a solid platform for continued skills-development, support and long-term growth. For more information about our dedicated training services, public course offerings, collaborative coaching services, new hire or enterprise upskilling programs, or to see our complete list of course offerings and special offers please call us toll free at 844-475-4559. Our pricing and services are always satisfaction guaranteed.
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Who should attend?
This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts.
Pre-Requisites: Students should have attended or have incoming skills equivalent to those in this course:
- Basic Python Skills. Attendees without Python background may view labs as follow along exercises or team with others to complete them. (NOTE: This course is also offered in R or Scala – please inquire for details)
- Good foundational mathematics skills in Linear Algebra and Probability, to start learning about and using basic machine learning algorithms and concepts
- Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su
Take Before: Attending students should have incoming skills equivalent to those in the course(s) below:
- TTPS4800 Introduction to Python (3 days)
Related Courses: We offer courses with similar topics coverage that offer an alternative focus or depth:
- TTML5502 Introduction to AI & Machine Learning for the Enterprise - Hands-on Overview (light, very basic labs – 2 days)
- TTML5503AI / ML JumpStart | introduction to AI, AI Programming & Machine Learning (3 days)
- TTML5504Machine Learning Foundation (Math Emphasis) | Working with Statistics, Algorithms & Neural Networks (3 days)
- TTML5506-PMachine Learning Essentials with Python (3 days)
Take Next / Follow-on Courses: This course is a core component of our AI & Machine Learning Skills Path, designed to trainer participants of all skill levels in modern AI, Machine Learning and Analytics skills across the enterprise. We offer courses in next level AI and Machine Learning, Deep Learning, Natural Language Processing, Applied Machine Learning (Chatbots, Intelligent Web) and many more related titles. Please contact us for details and next step recommendations based on your specific roles and. goals.
Training content
Day 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
- 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
Day 2
- Predictive Models
- Linear Regression
- Polynomial Regression
- Multiple Regression, and Predicting Car Prices
- Logistic Regression
- Logistic Regression
- LDA : Linear Discriminant Analysis
- 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
Day 3
- 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
Optional: Time Permitting
- 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
Course delivery details
Student Materials: Each student will receive a Student Guide with course notes, code samples, setp-by-step written lab instructions, software tutorials, diagrams and related reference materials and links (as applicable). Students will also receive related (as applicable) project files, code files, data sets and solutions required for any hands-on work.
Lab Setup Made Simple. All course labs and solutions, data sets, software, detailed courseware, lab guides and resources (as applicable) are provided for attendees in our easy access, no installation required, remote lab environment. Our tech team will help set up, test and verify lab access for each attendee prior to the course start date, ensuring a smooth start to class and successful hands-on course experience for all participants.
Costs
- Price: $2,195.00
- Discounted Price: $1,426.75
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Trivera Technologies
Trivera Technologies is a IT education services & courseware firm that offers a range of wide professional technical education services including: end to end IT training development and delivery, skills-based mentoring programs,new hire training and re-skilling services, courseware licensing and...