Categories
Data Science Data Sciences Books Machine Learning Machine Learning, AI & Deep Learning Uncategorized

Complete Machine Learning and Data Science: Zero to Mastery

What you’ll learn
  • Become a Data Scientist and get hired
  • Master Machine Learning and use it on the job
  • Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0
  • Use modern tools that big tech companies like Google, Apple, Amazon and Facebook use
  • Present Data Science projects to management and stakeholders
  • Learn which Machine Learning model to choose for each type of problem
  • Real-life case studies and projects to understand how things are done in the real world
  • Learn best practices when it comes to Data Science Workflow
  • Implement Machine Learning algorithms
  • Learn how to program in Python using the latest Python 3
  • How to improve your Machine Learning Models
  • Learn to pre-process data, clean data, and analyze large data.
  • Build a portfolio of work to have on your resume
  • Developer Environment setup for Data Science and Machine Learning
  • Supervised and Unsupervised Learning
  • Machine Learning on Time Series data
  • Explore large datasets using data visualization tools like Matplotlib and Seaborn
  • Explore large datasets and wrangle data using Pandas
  • Learn NumPy and how it is used in Machine Learning
  • A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided
  • Learn to use the popular library Scikit-learn in your projects
  • Learn about Data Engineering and how tools like Hadoop, Spark and Kafka are used in the industry
  • Learn to perform Classification and Regression modelling
  • Learn how to apply Transfer Learning
Requirements
  • No prior experience is needed (not even Math and Statistics). We start from the very basics.
  • A computer (Linux/Windows/Mac) with internet connection.
  • Two paths for those that know programming and those that don’t.
  • All tools used in this course are free for you to use.
Description

Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 200,000+ engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. This is a brand new Machine Learning and Data Science course just launched January 2020! Graduates of Andrei’s courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, + other top tech companies.

Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries). This course is focused on efficiency: never spend time on confusing, out of date, incomplete Machine Learning tutorials anymore. We are pretty confident that this is the most comprehensive and modern course you will find on the subject anywhere (bold statement, we know).

This comprehensive and project based course will introduce you to all of the modern skills of a Data Scientist and along the way, we will build many real world projects to add to your portfolio. You will get access to all the code, workbooks and templates (Jupyter Notebooks) on Github, so that you can put them on your portfolio right away! We believe this course solves the biggest challenge to entering the Data Science and Machine Learning field: having all the necessary resources in one place and learning the latest trends and on the job skills that employers want.

The curriculum is going to be very hands on as we walk you from start to finish of becoming a professional Machine Learning and Data Science engineer. The course covers 2 tracks. If you already know programming, you can dive right in and skip the section where we teach you Python from scratch. If you are completely new, we take you from the very beginning and actually teach you Python and how to use it in the real world for our projects. Don’t worry, once we go through the basics like Machine Learning 101 and Python, we then get going into advanced topics like Neural Networks, Deep Learning and Transfer Learning so you can get real life practice and be ready for the real world (We show you fully fledged Data Science and Machine Learning projects and give you programming Resources and Cheatsheets)!

The topics covered in this course are:

– Data Exploration and Visualizations

– Neural Networks and Deep Learning

– Model Evaluation and Analysis

– Python 3

– Tensorflow 2.0

– Numpy

– Scikit-Learn

– Data Science and Machine Learning Projects and Workflows

– Data Visualization in Python with MatPlotLib and Seaborn

– Transfer Learning

– Image recognition and classification

– Train/Test and cross validation

– Supervised Learning: Classification, Regression and Time Series

– Decision Trees and Random Forests

– Ensemble Learning

– Hyperparameter Tuning

– Using Pandas Data Frames to solve complex tasks

– Use Pandas to handle CSV Files

– Deep Learning / Neural Networks with TensorFlow 2.0 and Keras

– Using Kaggle and entering Machine Learning competitions

– How to present your findings and impress your boss

– How to clean and prepare your data for analysis

– K Nearest Neighbours

– Support Vector Machines

– Regression analysis (Linear Regression/Polynomial Regression)

– How Hadoop, Apache Spark, Kafka, and Apache Flink are used

– Setting up your environment with Conda, MiniConda, and Jupyter Notebooks

– Using GPUs with Google Colab

By the end of this course, you will be a complete Data Scientist that can get hired at large companies. We are going to use everything we learn in the course to build professional real world projects like Heart Disease Detection, Bulldozer Price Predictor, Dog Breed Image Classifier, and many more. By the end, you will have a stack of projects you have built that you can show off to others.

Here’s the truth: Most courses teach you Data Science and do just that. They show you how to get started. But the thing is, you don’t know where to go from there or how to build your own projects. Or they show you a lot of code and complex math on the screen, but they don’t really explain things well enough for you to go off on your own and solve real life machine learning problems.

Whether you are new to programming, or want to level up your Data Science skills, or are coming from a different industry, this course is for you. This course is not about making you just code along without understanding the principles so that when you are done with the course you don’t know what to do other than watch another tutorial. No! This course will push you and challenge you to go from an absolute beginner with no Data Science experience, to someone that can go off, forget about Daniel and Andrei, and build their own Data Science and Machine learning workflows.

Machine Learning has applications in Business Marketing and Finance, Healthcare, Cybersecurity, Retail, Transportation and Logistics, Agriculture, Internet of Things, Gaming and Entertainment, Patient Diagnosis, Fraud Detection, Anomaly Detection in Manufacturing, Government, Academia/Research, Recommendation Systems and so much more. The skills learned in this course are going to give you a lot of options for your career.

You hear statements like Artificial Neural Network, or Artificial Intelligence (AI), and by the end of this course, you will finally understand what these mean!

Click “Enroll Now” and join others in our community to get a leg up in the industry, and learn Data Scientist and Machine Learning. We guarantee this is better than any bootcamp or online course out there on the topic. See you inside the course!

Taught By:

Andrei Neagoie is the instructor of the highest rated Development courses on Udemy as well as one of the fastest growing. His graduates have moved on to work for some of the biggest tech companies around the world like Apple, Google, Amazon, JP Morgan, IBM, UNIQLO etc… He has been working as a senior software developer in Silicon Valley and Toronto for many years, and is now taking all that he has learned, to teach programming skills and to help you discover the amazing career opportunities that being a developer allows in life.

Having been a self taught programmer, he understands that there is an overwhelming number of online courses, tutorials and books that are overly verbose and inadequate at teaching proper skills. Most people feel paralyzed and don’t know where to start when learning a complex subject matter, or even worse, most people don’t have $20,000 to spend on a coding bootcamp. Programming skills should be affordable and open to all. An education material should teach real life skills that are current and they should not waste a student’s valuable time.   Having learned important lessons from working for Fortune 500 companies, tech startups, to even founding his own business, he is now dedicating 100% of his time to teaching others valuable software development skills in order to take control of their life and work in an exciting industry with infinite possibilities.

Andrei promises you that there are no other courses out there as comprehensive and as well explained. He believes that in order to learn anything of value, you need to start with the foundation and develop the roots of the tree. Only from there will you be able to learn concepts and specific skills(leaves) that connect to the foundation. Learning becomes exponential when structured in this way.

Taking his experience in educational psychology and coding, Andrei’s courses will take you on an understanding of complex subjects that you never thought would be possible.

See you inside the course!

Who this course is for:
  • Anyone with zero experience (or beginner/junior) who wants to learn Machine Learning, Data Science, Python
  • You are a programmer that wants to extend their skills into Data Science and Machine Learning to make yourself more valuable
  • Anyone who wants to learn these topics from industry experts that don’t only teach, but have actually worked in the field
  • You’re looking for one single course to teach you about Machine Learning and Data Science and get you caught up to speed with the industry
  • You want to learn the fundamentals and be able to truly understand the topics instead of just watching somebody code on your screen for hours without really “getting it”
  • You want to learn to use Deep Learning and Neural Networks with your projects
  • You want to add value to your own business or company you work for, by using powerful Machine Learning tools.

Categories
Data Science Data Sciences Books Machine Learning, AI & Deep Learning Uncategorized

Data Science & Machine Learning : Hands on Data Science 2020

What you’ll learn
  • You will Learn one of the most in-demand skill of 21st-century Data Science
  • Add Data science skills : python, numpy, pandas, Plotly, tableau, machine learning, statistics, probability in your resume
  • Apply linear regression and logistics regression on real dataset.
  • Crash course on python
  • Apply matrix operation with Numpy – Numerical python library
  • Visualize your data with mother of all visualization library available in Python : MatplotLIb
  • Perform Data analysis, wrangling and cleaning with pandas library
  • Get hands on with interactive visualization library Plotly
  • Getting start with data visualization tool, Tableau
  • Data Pre-processing technique – Missing data, Normalization, one hot encoding,
  • Importing data in Python from different sources, Files
  • Web Scraping to download web page and extract data
  • Data scaling and transformation
  • Exploratory Data analysis
  • Feature engineering process in Machine Learning system design
  • Machine learning theory
  • Apache spark installation : pyspark
  • Getting started with spark session
  • Mathey required for machine learning : Statistics, probability
  • Setup Data Science Virtual machine on Microsoft Azure Cloud
Requirements
  • Basic of Python programming
  • High school mathematics
Description

Welcome to Complete Ultimate course guide on Data Science and Machine learning with Python.

Have you ever thought about

How amazon gives you product recommendation,

How Netflix and YouTube decides which movie or video you should watch next,

Google translate translate one language to another,

How Google knows what is there in your photo,

How  Android speech Recognition or Apple siri understand your speech signal with such high accuracy.

If you would like algorithm or technology running behind that,  This is first course to get started in this direction.

==============================================

This course has more than 100 – 5 star rating.

What previous students have said: 

“This is a truly great course! It covers far more than it’s written in its name: many data science libraries, frameworks, techniques, tips, starting from basics to advanced level topics. Thanks a lot!  ”

This course has taught me many things I wanted to know about pandas. It covers everything since the installation steps, so it is very good for anyone willing to learn about data analysis in python /jupyter environment.”

“learning valuable concepts and feeling great.Thanks for this course.”

Good explanation, I have laready used two online tutorials on data -science and this one is more step by step, but it is good”

“i have studied python from other sources as well but here i found it more basic and easy to grab especially for the beginners. I can say its best course till now . it can be improved by including some more examples and real life data but overall i would suggest every beginner to have this course.”

“The instructor is so good, he helps you in all doubts within an average replying time of one hour. The content of the course and the way he delivers is great.”

==================================================

Why Data Science Now?

Data Scientist: The Sexiest Job of the 21st Century – By Harvard Business review

There is huge sortage of data scientist currently software industry is facing.

The average data scientist today earns $130,000 a year  by  glassdoor.

Want to join me for your journey towards becoming Data Scientist, Machine Learning Engineer.

This course has more than 100+ HD –  quality video lectures and is over 13+ hours in content.

This is first introductory course to get started data analysis, Machine learning and towards  AI algorithm implementation

This course will teach you – All Basic python library required for data analysis process.

  • Python  crash course
  • Numerical Python – Numpy
  • Pandas – data analysis
  • Matplotlib for data visualization
  • Plotly and Business intelligence tool Tableau
  • Importing Data in Python from different sources like .csv, .tsv, .json, .html, web rest Facebook API
  • Data Pre-Processing like normalization, train test split, Handling missing data
  • Web Scraping with python BeautifulSoup – extract  value from structured HTML Data
  • Exploratory data analysis on pima Indian diabetes dataset
  • Visualization of Pima Indian diabetes dataset
  • Data transformation and Scaling Data –  Rescale Data, Standardize Data, Binarize Data, normalise data
  • Basic introduction to What is Machine Learning, and  Scikit learn overview Its type, and comparison with traditional system. Supervised learning vs Unsupervised Learning
  • Understanding of regression, classification and clustering
  • Feature selection and feature elimination technique.
  • And Many Machine learning algorithm yet to come.
  • Data Science Prerequisite : Basics of Probability and statistics
  • Setup Data Science and Machine learning lab in Microsoft Azure Cloud

This course is for beginner and some experienced programmer who want to make career in Data Science and  Machine learning, AI.

Prerequisite:

  • basic knowledge in python programming (will be covered in python)
  • High School mathematics

Enroll in this course, take look at brief curriculum of this course and take first step in the wonderful world of Data.

See you in field.

Sincerely,

Ankit Mistry

Who this course is for:
  • Anyone who is interested in Data Science
  • Anyone who wants to learn – How to analyze data
  • Those who want to make a career in Data Analytics, Machine Learning, Data Science

Take Course

Categories
Academics Data Science Data Sciences Books

Data Science Masterclass Complete Course May-Updated 2020 [PDF]

Content

1- Data science in a big data world 1
2- The data science process 22
3- Machine learning 57
4- Handling large data on a single computer 85
5- First steps in big data 119
6- Join the NoSQL movement 150
7- The rise of graph databases 190
8- Text mining and text analytics 218
9- Data visualization to the end user 253

Whom this book is for

This book is an introduction to the field of data science. Seasoned data scientists will see that we only scratch the surface of some topics. For our other readers, there are some prerequisites for you to fully enjoy the book. A minimal understanding of SQL,
Python, HTML5, and statistics or machine learning are recommended before you dive into the practical examples.

14:Mb PDF

Categories
Academics Business Managment Data Science Data Sciences Books Udemy Courses

Statistics for Data Science and Business Analysis [PDF]

Statistics you need in the office: Descriptive & Inferential statistics, Hypothesis testing, Regression analysis

What you’ll learn

Statistics for Data Science and Business Analysis Course Site

  • Understand the fundamentals of statistics
  • Learn how to work with different types of data
  • How to plot different types of data
  • Calculate the measures of central tendency, asymmetry, and variability
  • Calculate correlation and covariance
  • Distinguish and work with different types of distributions
  • Estimate confidence intervals
  • Perform hypothesis testing
  • Make data-driven decisions
  • Understand the mechanics of regression analysis
  • Carry out regression analysis
  • Use and understand dummy variables
  • Understand the concepts needed for data science even with Python and R!

Requirements

  • Absolutely no experience is required. We will start from the basics and gradually build up your knowledge. Everything is in the course.
  • A willingness to learn and practice

Description

Is statistics a driving force in the industry you want to enter? Do you want to work as a Marketing Analyst, a Business Intelligence Analyst, a Data Analyst, or a Data Scientist?
Well then, you’ve come to the right place!

This is where you start. And it is the perfect beginning!

In no time, you will acquire the fundamental skills that will enable you to understand complicated statistical analysis directly applicable to real-life situations. We have created a course that is:

  • Easy to understand
  • Comprehensive
  • Practical
  • To the point
  • Packed with plenty of exercises and resources
  • Data-driven
  • Introduces you to the statistical scientific lingo
  • Teaches you about data visualization
  • Shows you the main pillars of quant research

Modern software packages and programming languages are automating most of these activities, but this course gives you something more valuable – critical thinking abilities. Computers and programming languages are like ships at sea. They are fine vessels that will carry you to the desired destination, but it is up to you, the aspiring data scientist or BI analyst, to navigate and point them in the right direction.

Teaching is our passion

We worked hard for over four months to create the best possible Statistics course which would deliver the most value to you. We want you to succeed, which is why the course aims to be as engaging as possible. High-quality animations, superb course materials, quiz questions, handouts, and course notes, as well as a glossary with all new terms you will learn, are just some of the perks you will get by subscribing. Statistics for Data Science and Business Analysis Course Site

What makes this course different from the rest of the Statistics courses out there?

  • High-quality production – HD video and animations (This isn’t a collection of boring lectures!)
  • Knowledgeable instructor (An adept mathematician and statistician who has competed at an international level)
  • Complete training – we will cover all major statistical topics and skills you need to become a marketing analyst, a business intelligence analyst, a data analyst, or a data scientist
  • Extensive Case Studies that will help you reinforce everything you’ve learned
  • Excellent support – if you don’t understand a concept or you simply want to drop us a line, you’ll receive an answer within 1 business day
  • Dynamic – we don’t want to waste your time! The instructor sets a very good pace throughout the whole course

Why do you need these skills?

  1. Salary/Income – careers in the field of data science are some of the most popular in the corporate world today.
  2. Promotions – If you understand Statistics well, you will be able to back up your business ideas with quantitative evidence, which is an easy path to career growth
  3. Secure Future – as we said, the demand for people who understand numbers and data, and can interpret it, is growing exponentially;
  4. Growth – this isn’t a boring job. Every day, you will face different challenges that will test your existing skills and require you to learn something new

Please bear in mind that the course comes with Udemy’s 30-day unconditional money-back guarantee. And why not give such a guarantee? We are certain this course will provide a ton of value for you.

Who this course is for:

  • People who want a career in Data Science
  • People who want a career in Business Intelligence
  • Business analysts
  • Business executives
  • Individuals who are passionate about numbers and quant analysis
  • People who want to start learning statistics
  • People who want to learn the fundamentals of statistics

Updated 2/2020

Categories
Business Managment Data Science

Big Data and Hadoop for Beginners – with Hands-on

Everything you need to know about Big Data and Learn Hadoop, HDFS, MapReduce, Hive & Pig by designing Data Pipeline.

What you’ll learn

Big Data and Hadoop for Beginners – with Hands-on Course Site

  • Understand different technology trends, salary trends, Big Data market and different job roles in Big Data
  • Learn what is Hadoop is for, and how it works
  • Understand the complex architectures of Hadoop and its component
  • Hadoop installation on your machine
  • High-quality documents
  • Demos: Running HDFS commands, Hive queries, Pig queries
  • Sample data sets and scripts (HDFS commands, Hive sample queries, Pig sample queries, Data Pipeline sample queries)
  • Start writing your codes in Hive and Pig to process huge volumes of data
  • Design your data pipeline using Pig and Hive
  • Understand modern data architecture: Data Lake
  • Practice with Big Data sets

Requirements

  • Basics knowledge of SQL and RDBMS would be a plus
  • Machine- Mac or Linux/Unix or Windows

Description

The main objective of this course is to help you understand Complex Architectures of Hadoop and its components, guide you in the right direction to start with, and quickly start working with Hadoop and its components.It covers everything that you need as a Big Data Beginner. Learn about the Big Data market, different job roles, technology trends, history of Hadoop, HDFS, Hadoop Ecosystem, Hive, and Pig. In this course, we will see how as a beginner one should start with Hadoop. This course comes with a lot of hands-on examples that will help you learn Hadoop quickly.

The course has 6 sections, and focuses on the following topics:

Big Data at a Glance: Learn about Big Data and different job roles required in the Big Data market. Know big data salary trends around the globe. Learn about the hottest technologies and their trends in the market.

Getting Started with Hadoop: Understand Hadoop and its complex architecture. Learn  Hadoop Ecosystem with simple examples. Know different versions of Hadoop (Hadoop 1.x vs Hadoop 2.x), different Hadoop Vendors in the market and Hadoop on Cloud. Understand how Hadoop uses the ELT approach. Learn to install Hadoop on your machine. We will see running HDFS commands from the command line to manage HDFS.

Getting Started with Hive: Understand what kind of problem Hive solves in Big Data. Learn its architectural design and working mechanism. Know data models in Hive, different file formats supported by Hive, Hive queries, etc. We will see running queries in Hive.

Getting Started with Pig: Understand how Pig solves problems in Big Data. Learn its architectural design and working mechanism. Understand how Pig Latin works in Pig. You will understand the differences between SQL and Pig Latin. Demos on running different queries in Pig.

Use Cases: Real-life applications of Hadoop are important to better understand Hadoop and its components, hence we will be learning by designing a sample Data Pipeline in Hadoop to process big data. Also, understand how companies are adopting modern data architecture i.e. Data Lake in their data infrastructure.

Practice: Practice with huge Data Sets. Learn Design and Optimization Techniques by designing Data Models, Data Pipelines by using real-life applications’ data sets.

Who this course is for:

  • This course assumes everyone as a beginner and teaches all fundamentals of Big Data, Hadoop and its complex architecture.
Categories
Academics Data Science Data Sciences Books

Data Science Masterclass Complete Course May Updated 2020 [PDF]

Content

1- Data science in a big data world 1
2- The data science process 22
3- Machine learning 57
4- Handling large data on a single computer 85
5- First steps in big data 119
6- Join the NoSQL movement 150
7- The rise of graph databases 190
8- Text mining and text analytics 218
9- Data visualization to the end user 253

Whom this book is for

This book is an introduction to the field of data science. Seasoned data scientists will see that we only scratch the surface of some topics. For our other readers, there are some prerequisites for you to fully enjoy the book. A minimal understanding of SQL,
Python, HTML5, and statistics or machine learning are recommended before you dive into the practical examples.

14:Mb PDF

Categories
Data Science Data Sciences Books

Beginning Data Science in R

Book Description:

Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R.Beginning Data Science in R details how data science is a combination of statistics, computational science, and machine learning.

You’ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this.This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming.

What You Will Learn

  • Perform data science and analytics using statistics and the R programming language
  • Visualize and explore data, including working with large data sets found in big data
  • Build an R package
  • Test and check your code
  • Practice version control
  • Profile and optimize your code

Who This Book Is ForThose with some data science or analytics background, but not necessarily experience with the R programming language.

Size : 21 MB

Categories
Data Science Data Sciences Books Development Udemy Courses

Intro to Data Science: Your Step-by-Step Guide To Starting

Learn the critical elements of Data Science, from visualization to databases to Python and more, in just 6 weeks!

What you’ll learn

  • The entire Data Science process
  • Cloud concepts & application in Data Science
  • Database concepts
  • Statistics fundamentals as needed in Data Science
  • Visualizations for data mining and presentation
  • An overview on Statistical Learning
  • The essentials of Machine Learning
  • More advanced Python to apply to Data Science

Requirements

  • Only a passion to build a successful Data Science career

Description

The demand for Data Scientists is immense. In this course, you’ll learn how you can play a part in fulfilling this demand and build a long, successful career for yourself.

The #1 goal of this course is clear: give you all the skills you need to be a Data Scientist who could start the job tomorrow… within 6 weeks.

With so much ground to cover, we’ve stripped out the fluff and geared the lessons to focus 100% on preparing you as a Data Scientist. You’ll discover:

The structured path for rapidly acquiring Data Science expertise

How to build your ability in statistics to help interpret and analyse data more effectively

To perform visualizations using one of the industry’s most popular tools

How to apply machine learning algorithms with Python to solve real world problems

Why the cloud is important for Data Scientists and how to use it

Along with much more. You’ll pick up all the core concepts that veteran Data Scientists understand intimately. Use common industry-wide tools like SQL, Tableau and Python to tackle problems. And get guidance on how to launch your own Data Science projects.

In fact, it might seem like too much at first. And there is a lot of content, exercises, study and challenges to get through. But with the right attitude, becoming a Data Scientist this quickly IS possible!

Once you’ve finished Introduction to Data Science A-Z, you’ll be ready for an incredible career in a field that’s expanding faster than almost anything else in the world.

Complete this course, master the principles, and join the ranks of Data Scientists all around the world.

Who this course is for:

  • Anyone who’s generally interested in Data Science
  • Anyone not satisfied with their job and wanting to transition into Data Science
  • Students in college wanting to start a career in Data Science
  • Students unsure of their future career wanting to see what Data Science is about
  • Junior Data Scientists aiming to boost their career prospects

Size: 4.15 GB

Categories
Data Science Machine Learning Python Python Courses Udemy Courses

Beginning with Machine Learning & Data Science in Python

Fundamentals of Data Science : Exploratory Data Analysis (EDA), Regression (Linear & logistic), Visualization, Basic ML

What you’ll learn

  • You will be able to apply data science algorithms for solving industry problems
  • You will have a clear understanding of industry standards and best practices for predictive model building
  • Will be able to derive key insights from data using exploratory data analysis techniques
  • You will be able to efficiently handle data in a structured way using Pandas
  • Have a strong foundation of linear regression, multiple regression and logistic regression
  • You will be able to use python scikit-learn for building different types of regression models
  • Will be able to use cross validation techniques for comparing models, select parameters
  • You will know about common pitfalls in modeling like over-fitting, bias-variance trade off etc..
  • You will be able to regularize models for reliable predictions

Requirements

  • Basic programming in any language
  • Basic Mathematics
  • Some exposure to Python (but not mandatory)

Description

This course will help you create a solid foundation of the essential topics of data science. With a solid foundation, you will be able to go a long way, understand any method easily, and create your own predictive analytics models.

At the end of this course, you will be able to:

  • Get your hands dirty by building machine learning models
  • Master logistic and linear regression, the workhorse of data science
  • Build your foundation for data science
  • Fast-paced course with all the basic & intermediate level concepts
  • Learn to manage data using standard tools like Pandas

This course is designed to get students on board with data science and make them ready to solve industry problems. This course is a perfect blend of foundations of data science, industry standards, broader understanding of machine learning and practical applications.

Special emphasis is given to regression analysis. Linear and logistic regression is still the workhorse of data science. These two topics are the most basic machine learning techniques that everyone should understand very well. Concepts of over fitting, regularization etc. are discussed in details.

These fundamental understandings are crucial as these can be applied to almost every machine learning methods.

This course also provide an understanding of the industry standards, best practices for formulating, applying and maintaining data driven solutions. It starts off with basic explanation of Machine Learning concepts and how to setup your environment. Learning the industry standard best practices and evaluating the models for sustained development comes next.

Final learning are around some of the core challenges and how to tackle them in an industry setup. This course supplies in-depth content that put the theory into practice.

Who this course is for:

  • Anyone willing to take the first step towards data science
  • Anyone willing to develop a solid foundation for data science
  • Planning to build the first regression / machine learning models
  • Anyone willing to learn exploratory data analysis

Size: 542.72 MB

Categories
Data Science Machine Learning Python Courses

MACHINE LEARNING A-Z™: HANDS-ON PYTHON & R IN DATA SCIENCE

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.

  • Master Machine Learning on Python & R
  • Have a great intuition of many Machine Learning models
  • Make accurate predictions
  • Make powerful analysis
  • Make robust Machine Learning models
  • Create strong added value to your business
  • Use Machine Learning for personal purpose
  • Handle specific topics like Reinforcement Learning, NLP and Deep Learning
  • Handle advanced techniques like Dimensionality Reduction
  • Know which Machine Learning model to choose for each type of problem
  • Build an army of powerful Machine Learning models and know how to combine them to solve any problem

Requirements

  • Just some high school mathematics level.

Description

Interested in the field of Machine Learning? Then this course is for you!

This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.

We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course is fun and exciting, but at the same time we dive deep into Machine Learning. It is structured the following way:

  • Part 1 – Data Preprocessing
  • Part 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
  • Part 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
  • Part 4 – Clustering: K-Means, Hierarchical Clustering
  • Part 5 – Association Rule Learning: Apriori, Eclat
  • Part 6 – Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
  • Part 7 – Natural Language Processing: Bag-of-words model and algorithms for NLP
  • Part 8 – Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
  • Part 9 – Dimensionality Reduction: PCA, LDA, Kernel PCA
  • Part 10 – Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

Moreover, the course is packed with practical exercises which are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.

And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.Who is the target audience?

  • Anyone interested in Machine Learning.
  • Students who have at least high school knowledge in math and who want to start learning Machine Learning.
  • Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
  • Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
  • Any students in college who want to start a career in Data Science.
  • Any data analysts who want to level up in Machine Learning.
  • Any people who are not satisfied with their job and who want to become a Data Scientist.
  • Any people who want to create added value to their business by using powerful Machine Learning tools.

Curriculum For This Course287 Lectures

41:12:35

Size: 6.84G