Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More!
Machine Learning Engineers earn on average $166,000 - become an ideal candidate with this course!
Solve any problem in your business, job or personal life with powerful Machine Learning models
Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells & more
Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, unsupervised Machine Learning etc
Introduction to Data Science
Learn how to extract information from data using data science methods, with this introduction to data science course.
This free online Introduction to Data Science course from Alison will teach you the basics of data science. You will look into data science processes, receive an introduction to machine learning, and learn about data models for structuring data. You will also be shown how to gain knowledge and insights from data that is both structured and unstructured as well as learn to use scientific methods, processes, algorithms, and data science systems.
Describe what data science is used for
List the stages in the data science process
Explain what machine learning is and the parts that make it up
Discuss the use of regression and the different types of regression
Identify the different types of classification algorithms available for you to use
Describe how the two most popular clustering algorithms work
Discuss why you would use Azure ML for your data science projects
Machine Learning, Data Science and Deep Learning with Python
Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks
Build artificial neural networks with Tensorflow and Keras
Classify images, data, and sentiments using deep learning
Make predictions using linear regression, polynomial regression, and multivariate regression
Data Visualization with MatPlotLib and Seaborn
Implement machine learning at massive scale with Apache Spark's MLLib
Understand reinforcement learning - and how to build a Pac-Man bot
Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
Use train/test and K-Fold cross validation to choose and tune your models
Build a movie recommender system using item-based and user-based collaborative filtering
Clean your input data to remove outliers
Design and evaluate A/B tests using T-Tests and P-Values
Artificial Intelligence Course Online
Take a conceptual look at Artificial Intelligence, covering topics like handling of data, preprocessing, model selection and model evaluation.
Understand the influences of AI
Explore the link between cognitive sciences and neural nets
Build a simple regression problem from scratch
Understand how ensemble learning is implemented and its benefits
Apply machine learning algorithms to visualise data and understand feature correlation
Machine Learning with Javascript
Master Machine Learning from scratch using Javascript and TensorflowJS with hands-on projects.
Assemble machine learning algorithms from scratch!
Build interesting applications using Javascript and ML techniques
Understand how ML works without relying on mysterious libraries
Optimize your algorithms with advanced performance and memory usage profiling
Use the low-level features of Tensorflow JS to supercharge your algorithms
Grow a strong intuition of ML best practices
Data Analytics Certification Course
Learn to analyse data to make better decisions and gain a solid foundation in statistics to better interpret your data in this online data analytics training course.
Basic statistical skills such as fundamental theories and terminology
Introduction to R, data cleaning, data visualisation and packages in R that can be used for data analysis
How to use Excel for descriptive statistics
Introduction to Tableau
How to interpret and present results
Diploma in Data Analytics with Python
This diploma course covers the importance and uses of data analytics in making business driven decisions in industries.
This free online data analytics course explains in great detail, analytics in a wide variety of industries. The course will be of great interest to students and professionals who wish to learn more about the use of analytics in their career and life. The course includes detail on how to develop the in-demand skills and knowledge needed to analyze data with python and drive decision-making to improve business performance in today's industries.
Basics of Python for Beginners
Join a small group of peers and an expert instructor for an engaging introduction to Python. This live online class is designed for students who have no previous experience with Python or coding. During this class, students will learn what Python is, why it is a valuable skill to learn, some basic concepts, and will get to engage with peers their own age who share an interest in computer programming and engineering. This class is the perfect starting point for early elementary students.
Data Science - Regression and Clustering Models
Learn how to create regression models, data classification models, and cluster models in Azure ML, R and Python.
This free online data science course will teach you about Regression and Clustering Models. You will look into what regression modelling and classification modelling are, look at their similarity, and learn how each of these models can be created in Azure ML, R, and Python. This course will also discuss the metrics for evaluating a classification model's performance. You will also examine unsupervised learning models, and more!
R Programming for Data Science And Machine Learning
DataQrious Academy, Make You Data Curious
R is one of the most popular and widely used tools for statistical programming.
It is a powerful, versatile, and easy to use tool for data analytics, and data visualization.
It is the first choice for thousands of data analysts working in both companies and academia.
Learn to program in R at a good level and how to use R Studio
Learn the core principles of R programming
Learn how to create vectors in R
Learn how to create variables
Learn Data types in R
Decision Making in R
Learn how to create a while() loop and a for() loop in R
Learn how to build and use matrices in R
Learn how to use Functions in R
Learn the matrix() function, learn rbind() and cbind()
Learn to use Factors in R
Learn to use Data Frames in R
Learn how to install packages in R
Learn how to use charts and Graphs in R
Learn to read data from CSV files
Learn Data Analysis in R
Data Manipulation using dplyr
Data Imputation
Learn how to use charts in R for data visualization
Shiny for Interactive dashboards in R
Analysis of Covariance
Tableau 2020 A-Z: Hands-On Tableau Training for Data Science
Learn Tableau 2020 for data science step by step. Real-life data analytics exercises & quizzes included. Learn by doing!
Install Tableau Desktop 2020
Connect Tableau to various Datasets: Excel and CSV files
Create Barcharts
Create Area Charts
Create Maps
Create Scatterplots
Create Piecharts
Create Treemaps
Create Interactive Dashboards
Create Storylines
Understand Types of Joins and how they work
Work with Data Blending in Tableau
Create Table Calculations
Work with Parameters
Create Dual Axis Charts
Create Calculated Fields
Create Calculated Fields in a Blend
Export Results from Tableau into Powerpoint, Word, and other software
Work with Timeseries Data (two methods)
Creating Data Extracts in Tableau
Understand Aggregation, Granularity, and Level of Detail
Adding Filters and Quick Filters
Create Data Hierarchies
Adding Actions to Dashboards (filters & highlighting)
Assigning Geographical Roles to Data Elements
Advanced Data Preparation (including latest updates in Tableau)
Data Analytics - Introduction to Machine Learning
Understand machine learning and its use in data analytics with this free online introduction to machine learning course.
This free online Machine Learning course will introduce you to machine learning. Machine learning is an essential part of data analytics. With this free course, you will get up-to-date with the most important machine learning topics today. Aside from teaching you about automation, the course also covers supervised and unsupervised learning and will introduce you to important computing methods to help you find hidden information within your data.
The Ultimate Hands-On Hadoop: Tame your Big Data!
Hadoop tutorial with MapReduce, HDFS, Spark, Flink, Hive, HBase, MongoDB, Cassandra, Kafka + more! Over 25 technologies.
Design distributed systems that manage "big data" using Hadoop and related technologies.
Use HDFS and MapReduce for storing and analyzing data at scale.
Use Pig and Spark to create scripts to process data on a Hadoop cluster in more complex ways.
Analyze relational data using Hive and MySQL
Analyze non-relational data using HBase, Cassandra, and MongoDB
Query data interactively with Drill, Phoenix, and Presto
Choose an appropriate data storage technology for your application
Understand how Hadoop clusters are managed by YARN, Tez, Mesos, Zookeeper, Zeppelin, Hue, and Oozie.
Publish data to your Hadoop cluster using Kafka, Sqoop, and Flume
Consume streaming data using Spark Streaming, Flink, and Storm
LaTeX for Data Scientists 2021
For the class project, I want you to first choose any topic related to Data Science.
One of the most valuable skills a Data Scientist can have is to communicate correctly. Data Scientists have to provide insights to stakeholders. Remember, sometimes, we don't have to explain things with difficult math. So, the project is the following:
Write a document (keep it short) where you explain the math behind any ML or DL model to me (I am a mathematician, so you can write complex math if you want!)
Now suppose I am a stakeholder. Can you explain to me how this ML or DL model will help my business to earn more money?
Both approaches are important for a Data Scientist. Your written and verbal skills should be good for becoming a Data Scientists and this is a good exercise for this.
Data Analytics - Mining and Analysis of Big Data
Learn how to analyse big data using mining and clustering techniques, in this free online big data analytics course.
This free online Big Data Analytics course from Alison will teach you how to mine and analyse big data. This process of studying and evaluating data is widely used in business and commercial industries and even in the government sector. With the help of this course, you will also learn how to create clustering data models which in turn can help you make more informed decisions. Make this a part of your business practice now!
Statistics for Business Analytics and Data Science A-Z
Learn The Core Stats For A Data Science Career. Master Statistical Significance, Confidence Intervals And Much More!
Understand what a Normal Distribution is
Understand standard deviations
Explain the difference between continuous and discrete variables
Understand what a sampling distribution is
Understand the Central Limit Theorem
Apply the Central Limit Theorem in practice
Apply Hypothesis Testing for Means
Apply Hypothesis Testing for Proportions
Use the Z-Score and Z-Tables
Use the t-Score and t-Tables
Understand the difference between a normal distribution and a t-distribution
Understand and apply statistical significance
Create confidence intervals
Understand the potential pitfalls of overusing p-Values
Spark and Python for Big Data with PySpark
Learn how to use Spark with Python, including Spark Streaming, Machine Learning, Spark 2.0 DataFrames and more!
Use Python and Spark together to analyze Big Data
Learn how to use the new Spark 2.0 DataFrame Syntax
Work on Consulting Projects that mimic real world situations!
Classify Customer Churn with Logisitic Regression
Use Spark with Random Forests for Classification
Learn how to use Spark's Gradient Boosted Trees
Use Spark's MLlib to create Powerful Machine Learning Models
Learn about the DataBricks Platform!
Get set up on Amazon Web Services EC2 for Big Data Analysis
Learn how to use AWS Elastic MapReduce Service!
Learn how to leverage the power of Linux with a Spark Environment!
Create a Spam filter using Spark and Natural Language Processing!
Use Spark Streaming to Analyze Tweets in Real Time!
Python A-Z™: Python For Data Science With Real Exercises!
Programming In Python For Data Analytics And Data Science. Learn Statistical Analysis, Data Mining And Visualization
Learn to program in Python at a good level
Learn how to code in Jupiter Notebooks
Learn the core principles of programming
Learn how to create variables
Learn about integer, float, logical, string and other types in Python
Learn how to create a while() loop and a for() loop in Python
Learn how to install packages in Python
Understand the Law of Large Numbers
Apache Kafka Series - Learn Apache Kafka for Beginners v2
Learn Apache Kafka 2.0 Ecosystem, Core Concepts, Real World Java Producers/Consumers & Big Data Architecture
Understand Apache Kafka Ecosystem, Architecture, Core Concepts and Operations
Master Concepts such as Topics, Partitions, Brokers, Producers, Consumers
Start a personal Kafka development environment
Learn major CLIs: kafka-topics, kafka-console-producer, kafka-console-consumer, kafka-consumer-groups, kafka-configs
Create your Producers and Consumers in Java to interact with Kafka
Program a Real World Twitter Producer & ElasticSearch Consumer
Extended APIs Overview (Kafka Connect, Kafka Streams), Case Studies and Big Data Architecture
