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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 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