Complete 2019 Data Science & Machine Learning Bootcamp

[LeeAndro]

Complete 2019 Data Science & Machine Learning Bootcamp | 14.8GB

Welcome to the Complete Data Science and Machine Learning Bootcamp, the only course you need to learn Python and get into data science.

What you'll learn
You will learn how to program using Python through practical projects
Use data science algorithms to analyse data in real life projects such as spam classification and image recognition
Build a portfolio of data science projects to apply for jobs in the industry
Understand how to use the latest tools in data science, including Tensorflow, Matplotlib, Numpy and many more
Create your own neural networks and understand how to use them to perform deep learning

Requirements
No programming experience needed! I'll teach you everything you need to know.
No statistics knowledge required! I'll teach you everything you need to know.
No calculus knowledge required! as long as you've done some high school maths, I'll take you step by step through the difficult parts.
Also, no paid software required - all projects use free and open source software
All you need is Mac or PC computer with access to the internet

In the curriculum, we cover a large number of important data science and machine learning topics, such as:

Data Cleaning and Pre-Processing

Data Exploration and Visualisation

Linear Regression

Multivariable Regression

Optimisation Algorithms and Gradient Descent

Naive Bayes Classification

Descriptive Statistics and Probability Theory

Neural Networks and Deep Learning

Model Evaluation and Analysis

Throughout the course, we cover all the tools used by data scientists and machine learning experts, including:

Python 3

Tensorflow

Pandas

Numpy

Scikit Learn

Keras

Matplotlib

Seaborn

SciPy

SymPy

By the end of this course, you will be fluently programming in Python and be ready to tackle any data science project. We'll be covering all of these Python programming concepts:

Data Types and Variables

String Manipulation

Functions

Objects

Lists, Tuples and Dictionaries

Loops and Iterators

Conditionals and Control Flow

Generator Functions

Context Managers and Name Scoping

Error Handling

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