Applied Data Science and Machine Learning Training
Applied Data Science and Machine Learning Training is a 3-day piratical course covering the practical data science and applied machine learning.
Participants will learn how to apply data science methodologies to popular machine learning and deep learning by applying datasets and principles of training data, prediction algorithms using data. We will cover application of machine learning to sensor data and sensor fusion using component analysis, and regularization to predict intelligent decisions.
You will learn about several popular machine learning algorithms and how to use a set of data and training data, to discover predictive relationships and intelligence.
Machine learning serves to automate the data analysis process by enabling computers and machines to learn from data through experience applied to specific tasks without explicit programming.
For systems where the mathematical techniques are too computationally complex or are undetermined, machine learning can serve as an input to algorithms in order to control complex dynamical systems.
Participants will learn, comprehend and master ideas on machine learning concepts, key principles, and techniques including: supervised and unsupervised learning, mathematical and heuristic aspects of data analysis, modeling to describe key algorithms such as linear regression, clustering, classification, and prediction.
What you’ll learn
- The basics of data science and its application
- Principles of machine learning
- Machine learning algorithms and tools
- Data science application in context of machine learning
- Tools to use data science techniques to analyze and manipulate data
- Methods for working with applied data science and machine learning
- Methods and tools to uncover valuable insights from the dataset using Python
- Apply SAS, R, Scala to Data Science and Machine Learning
- Data science process including data preparation, feature engineering and selection, exploratory data analysis, data visualization, machine learning, model evaluation and optimization
- Applied data science and machine learning for sensor fusion and cybersecurity
- Computational methods to deal with data science and big data, tools, theory, visualization
Who Should Take this Course
Anyone who wishes to incorporate data analysis, machine learning, automation and data science into their work environment.
Course Outline:
The Basics Of Data Science
- Introduction to Data Science
- Big data 101
- Data cleansing, preparation, and analysis
- Data Mining vs. Statistics
- Data Preparation
- Data Science Techniques to Analyze and Manipulate Data
- Exploratory Data Analysis
- Data Analysis
- Data Visualization
- Power Laws and Distributions
- Cognitive Bias Key Points
- Data Wrangling
- MapReduce
- Introduction to Big Data and MapReduce
- Data Science Process
- Computational Methods to Deal with Data Science and Big Data
- Principles of Tools, Theory, and Visualization
Principles Of Machine Learning
- What is Machine Learning?
- Emergence and applications of Artificial Intelligence and Machine Learning
- Basics of Artificial Intelligence
- Basics of Machine Learning
- Basics of Data Mining
- Machine Learning Algorithms and Tools
- Supervised and unsupervised learning algorithms: K-Means Clustering to analyze sensor data
- Random Forests
- Naive Bayes
- K-Nearest Neighbors (K-NN)
- Vector Machines (SVM)
- Machine learning API calls to merge datasets
- Machine Learning With Python
- Visualizing and data simulation using Python
Applied Machine Learning
- Tools to apply machine learning, predictive analytics, and sentiment analysis to extract critical information from the collected data sets
- Introduction to probability and statistics
- Data modeling and evaluation applied to Machine Learning
- Applied Python, SAS, R, Scala to Data Science and Machine Learning
- Data Science Application In Context of Machine Learning
- Applied Data Science and Machine Learning
- Sensor Fusion and Cybersecurity
- Creating good machine learning systems
- Applying supervised learning and unsupervised learning
- Trained using labeled examples
- Classification, regression, prediction and gradient boosting
- Predicting the values of the label on additional unlabeled data
- Using historical data to predict likely future events
- Unsupervised learning and unlabeled data
- Unsupervised learning against data that has no historical labels
- Semi supervised learning
- Using both labeled and unlabeled data for training
- Classification, regression and prediction
- Reinforcement learning
- Discovery through trial and error
- The agent (the learner or decision maker)
- The environment (everything the agent interacts with)
- Actions (what the agent can do)
Python for Data Science
- Python basics
- Data structures in Python
- NumPy for scientific computing
- Pandas for data analysis
Exploratory Data Analysis
- Data cleaning and preprocessing
- Data visualization
- Statistical analysis
Supervised Learning
- Linear regression
- Logistic regression
- Decision trees
- Random forests
Unsupervised Learning
- Clustering
- Dimensionality reduction
- Association rule mining
Case Studies and Group Projects
Participants will work in group on a project(s) to complete an applied data science and machine learning project:
- Generalization and Classification
- Training and Training Set
- Validation
- Representation
- Regularization
- Logistic Regressions
- Neutral Nets
- Multi class Neutral Nets
- Sigmoid function
- Tensor and tensor rank
- Building, applying and evaluating machine learning algorithms to sensor data and sensor fusion
- Applied Data Structures and Visualization
- Applied Regression Analysis
- Applied Classification, Clustering and Association
- Automating the process of learning, tuning and optimizing machine learning models
Preprocessing raw sensor data for machine learning