10 Machine Learning course You Should Know to Become a Data Scientist

      Machine Learning A-Z™: Hands-On Python & R In              Data Science

• 
  
• Learn to create Machine Learning Algorithms in Python and R. This course is packed with practical exercises which are based on live examples. You’ll learn the theory and you’ll get some hands-on practice building your own models. Includes Python and R code templates to use on your own projects.

  Data Science A-Z™: Real-Life Data Science Exercises Included   

  Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more. This course will give you a full overview of the Data Science journey. You’ll develop a good understanding of SQL, SSIS, Tableau, and Gretl.

  Python for Data Science and Machine Learning Bootcamp   

  

  Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more. This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms.

  Neural Networks and Deep Learning

  When you finish this class, you’ll understand the major technology trends driving Deep Learning. Be able to build, train and apply fully connected deep neural networks. Know how to implement efficient neural networks. And understand the key parameters in a neural network’s architecture.

  Machine Learning

  In this class, you’ll learn about the most effective machine learning techniques, and gain practice implementing them. You’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to apply these techniques to new problems.

  Deep Learning A-Z™: Hands-On Artificial Neural Networks

  Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. This training program is filled with intuition tutorials, practical exercises and real-world case studies.

  The Data Scientist’s Toolbox

  

  There are two components to this course. The first is a conceptual introduction to the ideas behind turning   data  into   actionable knowledge. The  second   is     a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.

  The Web Developer Bootcamp

  When learning to program you often have to sacrifice learning exciting and current technologies in favor of the “beginner friendly” classes. In this course, you get the best of both worlds. It’s designed for the beginner,   yet  covers  some  of   the    most exciting topics in the industry. Learn HTML, CSS, JS, Node, and more.

  The Complete Web Developer Course 2.0

  You’ll get access to 12 chapters that dig deep into the nitty gritty of building successful websites. Each chapter is supported with over 40 hours of video tutorials and practical website challenges. Learn to build  25 websites and real mobile apps using HTML, CSS, Javascript, PHP, Python, MySQL and more.

  SQL for Newbs: Data Analysis for Beginners

  If you have no technical background, don’t be afraid. This courses teaches you real-world SQL — not just the theory in abstract, but real skills you can use to get more data-driven in your current job. You’ll have the raw skills to do some real data analysis for your company using SQL.

  The Complete SQL Bootcamp

  In this course, you’ll learn how to read and write complex queries to a database using one of the most in demand skills — PostgreSQL. These skills are also applicable to any other major  SQL database ,    such as MySQL, Microsoft SQL Server, Amazon Redshift, Oracle, and much more.

  R Programming A-Z™: R For Data Science With Real Exercises

  

  Learn Programming In R And R Studio. Data Analytics, Data Science, Statistical Analysis, Packages, Functions, GGPlot2. This training is packed with real-life analytical challenges which you will learn to solve. This course has been designed for all skill levels, no programming or statistical background needed.

  Tableau 10 A-Z: Hands-On Tableau Training For Data Science   

  This course begins with Tableau basics. You will navigate the software, connect it to a data file, and export a worksheet, so even beginners will feel completely at ease. You’ll learn all of the features in Tableau that allow you to explore, experiment with, fix, prepare, and present data easily, quickly, and beautifully.
• 
  


• 
  
• 
  
• 
  
• 
  
• 
  

machine learning history and supervised learning

In the present world machine learning is one of the greatest invention for future.Today’s we can’t think about machine.In 2021 most of the powerful country has reputeted their power in machine learning.

There is a speech that-

“Artificial intellegence , Deep learning,Machine learning-whatever you are doing if you don’t understand it-learn it.Because otherwise you’re going to be a dianosour within 3 years”-Mark cuban

In 1959 Arthur samuel said-

Machine learning is a field science that gives computers the abaility to learn without being explicity programmed.

Samuel claim to fame was that back in the 1950’s he wrote a checkers playing program ,and the amazing thing about this checkers playing ptogram was that arthur samuel himself.but wasn’t good checkers player.But he had to ptogram to play 10’s to 1000’s games against itself.That was a remarkable result.

otherwise TomMitchell said

In 1998-

A computer program is said to learn from experience E with respect to some task T and some performance measure P,if its perfotmance on T as measured by P ,improves with experience E.

well as the most recent milestones.History of machine learning….

1950 — Alan Turing creates the “Turing Test” to determine if a computer has real intelligence. To pass the test, a computer must be able to fool a human into believing it is also human.

1952 — Arthur Samuel wrote the first computer learning program. The program was the game of checkers, and the IBM computer improved at the game the more it played, studying which moves made up winning strategies and incorporating those moves into its program.

1957 — Frank Rosenblatt designed the first neural network for computers (the perceptron), which simulate the thought processes of the human brain.

1967 — The “nearest neighbor” algorithm was written, allowing computers to begin using very basic pattern recognition. This could be used to map a route for traveling salesmen, starting at a random city but ensuring they visit all cities during a short tour.

1979 — Students at Stanford University invent the “Stanford Cart” which can navigate obstacles in a room on its own.

1981 — Gerald Dejong introduces the concept of Explanation Based Learning (EBL), in which a computer analyses training data and creates a general rule it can follow by discarding unimportant data.

1985 — Terry Sejnowski invents NetTalk, which learns to pronounce words the same way a baby does.1990s — Work on machine learning shifts from a knowledge-driven approach to a data-driven approach. Scientists begin creating programs for computers to analyze large amounts of data and draw conclusions — or “learn” — from the results.

1997 — IBM’s Deep Blue beats the world champion at chess.

2006 — Geoffrey Hinton coins the term “deep learning” to explain new algorithms that let computers “see” and distinguish objects and text in images and videos.

2010 — The Microsoft Kinect can track 20 human features at a rate of 30 times per second, allowing people to interact with the computer via movements and gestures.

2011 — IBM’s Watson beats its human competitors at Jeopardy.

2011 — Google Brain is developed, and its deep neural network can learn to discover and categorize objects much the way a cat does.

2012 – Google’s X Lab develops a machine learning algorithm that is able to autonomously browse YouTube videos to identify the videos that contain cats.

2014 – Facebook develops DeepFace, a software algorithm that is able to recognize or verify individuals on photos to the same level as humans can.

2015 – Amazon launches its own machine learning platform.

2015 – Microsoft creates the Distributed Machine Learning Toolkit, which enables the efficient distribution of machine learning problems across multiple computers.

2015 – Over 3,000 AI and Robotics researchers, endorsed by Stephen Hawking, Elon Musk and Steve Wozniak (among many others), sign an open letter warning of the danger of autonomous weapons which select and engage targets without human intervention.

2016 – Google’s artificial intelligence algorithm beats a professional player at the Chinese board game Go, which is considered the world’s most complex board game and is many times harder than chess. The AlphaGo algorithm developed by Google DeepMind managed to win five games out of five in the Go competition.
So are we drawing closer to artificial.

Machine learning algorithm:

• Supervised Algorithm

-Linear regression

-Logistic regression

-Neural nework

-K-nearest neighbour algorithm

-Decision trees and random Forest

-SVM(support vactor machines)

• Unsupervised algorithm

-Clustering

1.k-means

2.HCA

3.Expectation maximization

-Visualization and dimesionality reduction

1.PCA

2.kernal PCA

3.T-SNE

4.Locally linear embadding

-Associate rule learning

1.Apriori

2.Eclat

What is supervised learning-

Superised learning is a problem of taking data sets,gleaning information from it so that you can level new data set,machine learning task to learning a function to input to output pairs.

In one word says Function approximiately.

Supervised learning is prediction variable /features and a target variable.Supervised learning data consist of(x,y) pairs.In supervised learning problems we start with a data set containing training example with associated correct level.

Input - 1 2 3 4 5 6 7

Output-1 4 9 16 25 36 49

input-output square

Their two task of supervised learning-

1.Regression

2.Classification

Regression:Predict a continious numerical value.Target variable to continious.

Classification:Assign a level,target variable consist of categories.

Regression algorithm:Some of the most powerful regression algorithm is-

1.Logistic regression

2.linear regression

3.Polynomial regression

-Classification:The best classification algorithm is

1.Naive Bayes

2.K-nearest algorithm

3.SVM

4.Decision trees.

Example of Supervised learning-

-Automate time consuming or expensive manual task

.Doctor test the diagonoisis

-Need level data

.Historical data with level

.price house prediction

Supervised learning we will use Scikit learn.

Other libraries

1 .Tensorflow

2.Keras