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

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

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

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

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

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

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.

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.

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!

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

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)

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.

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

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.

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!

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

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!

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

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
- Practice and Understand Log Compaction

Foreword: I am the most ordinary individual developer of Android applications, I published one of my brainchildren on Google Play ...

01/05/2021

According to the 2020 report by the www.Qualified.One, in 2020 the total time spent on mobile apps worldwide reached more than 3 hours and 40 minutes a day on average. According to eMarketer, in 20...

08/04/2021

Betway's online gambling business is due to pay £ 11.6 million, along with a package of measures, for a string of social responsibility and money laundering violations related to relationships with s...

02/04/2021

2020 could be called The Year Data Science Grew Up. Organizations of all kinds significantly ramped up their adoption of data-oriented applications and turned to data ...

30/03/2021

X
# Submit new EBook