Download Complete Machine Learning and Data Science: Zero to Mastery Udemy

What you would learn in Complete Machine Learning and Data Science: Zero to Mastery course?

Learn to become a full Data Scientist or Machine Learning engineer! This brand-new Machine Learning and Data Science course were announced on January 20, 2020! Join an online community of more than 180,000 developers, and learn from experts from the industry who have worked for major corporations in areas such as Silicon Valley and Toronto. Students of Andrei's classes are now employed at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, and other top tech companies.

Get a head start on learning Data Science and Machine Learning from scratch, be hired, and have fun along your way. Take advantage of the most up-to-date, modern Data Science course on Udemy (we employ the most recent version of Python, Tensorflow 2.0, and other libraries). This course is designed to be efficient. Do not waste your time reading complicated, outdated, and insufficient Machine Learning tutorials anymore. We're pretty sure that it's the most complete and current course you'll get on the subject (a bold claim, we're aware).

This extensive and project-based course will teach you all the capabilities required of an influential Data Scientist. Along the process, we'll create several real-world projects that you can include in your portfolio. You'll have access to all of the template, code, and workbooks (Jupyter Notebooks) on Github which means you can include them in your portfolio as soon as you are ready! This course will solve the greatest obstacle to entry into the Data Science and Machine Learning field: having all the needed resources in one place and learning about the most recent trends and capabilities that employers require.

If you're already familiar with programming, you can jump straight into the course and skip the part where we introduce you to Python entirely from beginning to finish. If you're brand new, We take you right from the beginning and then show you Python and how you can apply it in real-world situations in our projects. But don't worry, after we've gone through the basic concepts such as Machine Learning 101 and Python, We then move into more advanced topics like Neural networks, Deep Learning and Transfer Learning so that you can gain practical experience and get ready for real-world scenarios (We present fully-fledged Data Science and Machine Learning projects, and provide you with Programming Resources and Cheatsheets)! high course covers two tracks. The program will be highly hands-on, and we will guide you from beginning to finish to become an experienced Machine Learning and Data Science engineer.

The subjects taught 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

is a Data Visualization using Python with MatPlotLib as well as Seaborn

- Transfer Learning

Classification and recognition of images

- - Train/Test, cross-validation

Supervised learning: Classification Regression, and Time Series

-- Decision Trees as well as Random Forests

- Ensemble Learning

"Hyperparameter Tuning"

- Making use of Pandas Data Frames to tackle difficult tasks

Make use of Pandas to manage CSV Files

(HTML0) – Deep Learning / Neural Networks using TensorFlow 2.0 and Keras

- Utilizing Kaggle and participating in Machine Learning competitions

How do you present your results and make your boss jealous

How do you clean the data to be analyzed

KK Nearest Neighbours

- - Support Vector Machines

- Regression analysis (Linear Regression/Polynomial Regression)

How Hadoop, Apache Spark, Kafka, and Apache Flink are utilized

- Configuring your environment using Conda, MiniConda, and Jupyter Notebooks

- Utilizing GPUs using Google Colab

When you finish this course, you'll become a full Data Scientist that big companies can hire. We'll utilize the information we've learned during the class to create professional, real-world projects such as Heart Disease Detection, Bulldozer Price Predictor, Dog Breed Image Classifier, and others. When the course is over, you'll be able to showcase the projects that you've created that you can showcase to your friends and colleagues.

This is the truth: Most courses will teach students Data Science and do just what they say. They teach you how to start. However, you're not sure what to do next or how to construct projects of your own. They display lots of math and code on the screen; however, they fail to provide enough information to work on your own and work out real-world machine learning challenges.

If you're new to programming or are looking to improve your Data Science skills, or come from a different profession, this course is designed ideal for you. This course isn't about creating code without understanding the concepts and concepts so that after you're finished with the course, you're not sure what you should do other than watch another video tutorial. No! The course is designed to test and push you to transform from a complete beginner without any Data Science experience to an individual who can go off without thinking the names Daniel and Andrei and develop your 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, Patients Diagnosis and Fraud Detection, and anomaly Detection of anomalies in Manufacturing, Government, Academia/Research, Recommendation Systems and so and so. The knowledge gained in this course will provide you with lots of possibilities for your job.

You will hear phrases like Artificial Neural Network or Artificial Intelligence (AI), and at the end of this class, you'll know what AI is all about!

Requirements

• There is no prior knowledge required (not at all Math or Statistics). We start with the fundamentals.
• Computer (Linux/Windows/Mac) with an internet connection.
• Two options for those who are proficient in programming and for those who do not.
• All the tools that are used in this course are free to make use of.

Who is this course intended for:

• Anyone with no previous experience (or novice or junior) who is eager to learn Machine Learning, Data Science, and Python
• You are a computer programmer who would like to expand your knowledge in Data Science and Machine Learning to make yourself more effective.
• Anyone who would like to learn these subjects from experts who don't just teach but work in the field.
• You're searching for a course that will educate you all about Machine learning and Data Science and bring you up on the latest developments in the current trends in the field.
• You'd like to understand the basics of programming and to be able to grasp the concepts instead of just watching someone code on their screen for hours but not really "getting it."
• You'd like to know how the use of Deep Learning and Neural Networks in your projects.
• You'd like to increase the value of your company or business you manage by using the most powerful Machine Learning tools.

Course Content:

• Learn to become a Data Scientist and be hired
• Learn Machine Learning and use it in the workplace
• Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0
• Make use of modern tools that major tech companies such as Google, Apple, Amazon, and Facebook employ.
• Then present Data Science projects to management and other stakeholders
• Find out the best Machine Learning method to use for every type of issue
• Real-world projects and case studies to learn how things work daily
• Know the best practices in the field of Data Science Workflow
• Install Machine Learning algorithms
• Learn to program using Python with the latest Python 3
• How can you enhance Your Machine Learning Models
• Learn to process data, clean data, and analyze large amounts of datasets.
• Create a portfolio of your work to include on your resume
• Developer Environment set up to support Data Science and Machine Learning
• Unsupervised and Supervised Learning
• Machine Learning on Time Series data
• Explore massive datasets with visualization tools such as Matplotlib and Seaborn
• Discover large datasets and organize data with Pandas
• Learn about NumPy as well as how it can be utilized in Machine Learning
• A collection consisting of Data Science and Machine Learning projects can be used to submit for jobs in the field, complete with notebooks and codes included.
• Learn how to utilize the well-known library Scikit-learn to help you with your projects.
• Find out more all you can learn about Data Engineering and how tools such as Hadoop, Spark, and Kafka are used in the field.
• Learn how to do Classification and Regression modeling
• Learn to use Transfer Learning

Download Complete Machine Learning and Data Science: Zero to Mastery from below links NOW!