Python for Machine Learning & Data Science Masterclass

Learn about Python BootCamp & Data Science with Machine Learning! Including Numpy, Pandas, Matplotlib, Scikit-Learn and more!

What you will learn

  • Machine Learning with Python
  • Data Science with Python

Requirements of Python BootCamp & Data Science:

  • Basic Python Knowledge (capable of functions)

Description of Python BootCamp & Data Science:

Python BootCamp & Data Science Prompt riser Release for the full impending 2021 Python for Machine Learning and Data Science Masterclass!

If it’s not too much trouble, note! This is at present in an Early Bird Beta access, which means we are as yet going to be ceaselessly adding substance to the course (despite the fact that we are now at more than 20 hours of substance!) Since we’re actually adding substance and accepting understudy input as we complete the course through the beginning of 2021, understudies who enlist currently will gain admittance to a wide assortment of advantages!

What do you get with Early Bird Access?

You will get select admittance to week by week live video transfers where we will go through intuitive AI projects! You’ll have the option to straightforwardly pose inquiries during the streams that will match with segment dispatches relating to new AI calculations added to the course content! These week after week streams will likewise incorporate live Q&A with the educator of the course, Jose Portilla. We will likewise be taking in understudy input to shape certain forthcoming streaming tasks. These streams might be open to prompt riser understudies, and will be eliminated once the course is completely finished and dispatched!

What is in the course?

Welcome to the most complete seminar on learning Data Science and Machine Learning on the web! In the wake of educating more than 2 million understudies I’ve worked for longer than a year to assemble what I accept to be the most ideal approach from zero to saint for information science and AI in Python!

This extensive course is intended to be comparable to bootcamps that normally cost a great many dollars, the last course will incorporate the accompanying points:

  • Programming with Python
  • NumPy with Python
  • Profound jump into Pandas for Data Analysis
  • Full comprehension of Matplotlib Programming Library
  • Profound jump into seaborn for information representations
  • AI with SciKit Learn, including:
  • Direct Regression
  • Regularization
  • Tether Regression
  • Edge Regression
  • Flexible Net
  • K Nearest Neighbors
  • K Means Clustering
  • Choice Trees
  • Irregular Forests
  • Regular Language Processing
  • Backing Vector Machines
  • Hierarchal Clustering
  • DBSCAN
  • PCA
  • Complex Learning
  • Model Deployment

what’s more, a whole lot more!

As usual, we’re appreciative for the opportunity to show you information science, AI, and python and expectation you will go along with us inside the course to help your range of abilities!

Who this course is for:

  • Beginner Python developers curious about Machine Learning and Data Science with Python

Course content of Python BootCamp & Data Science:

Introduction of Python BootCamp & Data Science:

  • EARLY BIRD INFO
  • COURSE OVERVIEW LECTURE – PLEASE DO NOT SKIP!
  • Anaconda Python and Jupyter Install and Setup
  • Note on Environment Setup – Please read me!
  • Environment Setup

OPTIONAL: Python Crash Course:

  • OPTIONAL: Python Crash Course
  • Python Crash Course

Machine Learning Pathway Overview:

  • Machine Learning Pathway

NumPy6 lectures • 53min

  • Introduction to NumPy
  • NumPy Arrays
  • Coding Exercise Check-in: Creating NumPy Arrays1 question
  • NumPy Indexing and Selection
  • Coding Exercise Check-in: Selecting Data from Numpy Array1 question
  • NumPy Operations
  • Check-In: Operations on NumPy Array1 question
  • NumPy Exercises

Pandas:

  • Introduction to Pandas
  • Series – Part One
  • Check-in: Labeled Index in Pandas Series1 question
  • DataFrames – Part One – Creating a DataFrame
  • DataFrames – Part Two – Basic Properties08:18
  • DataFrames – Part Three – Working with Columns
  • DataFrames – Part Four – Working with Rows
  • Pandas – Conditional Filtering
  • Pandas – Useful Methods – Apply on Single Column
  • Pandas – Useful Methods – Apply on Multiple Columns
  • Pandas – Useful Methods – Statistical Information and Sorting
  • Missing Data – Overview
  • Missing Data – Pandas Operations
  • GroupBy Operations – Part One
  • GroupBy Operations – Part Two – MultiIndex
  • Combining DataFrames – Concatenation
  • Combining DataFrames – Inner Merge
  • Combining DataFrames – Left and Right Merge
  • Combining DataFrames – Outer Merge
  • Pandas – Text Methods for String Data
  • Pandas – Time Methods for Date and Time Data
  • Pandas Input and Output – CSV Files
  • Pandas Input and Output – HTML Tables
  • Pandas Input and Output – Excel Files
  • Pandas Input and Output – SQL Databases
  • Pandas Pivot TablesPreview
  • Pandas Project Exercise Overview
  • Pandas Project Exercise Solutions

Matplotlib:

  • Introduction to Matplotlib
  • Matplotlib Basics
  • Matplotlib – Understanding the Figure Object
  • Matplotlib – Implementing Figures and Axes
  • Matplotlib – Figure Parameters
  • Matplotlib – Subplots Functionality
  • Matplotlib Styling – Legends
  • Matplotlib Styling – Colors and Styles
  • Advanced Matplotlib Commands (Optional)
  • Matplotlib Exercise Questions Overview
  • Matplotlib Exercise Questions – Solutions

Seaborn Data Visualizations:

  • Introduction to Seaborn
  • Scatterplots with Seaborn
  • Distribution Plots – Part One – Understanding Plot Types
  • Distribution Plots – Part Two – Coding with Seaborn
  • Categorical Plots – Statistics within Categories – Understanding Plot Types
  • Categorical Plots – Statistics within Categories – Coding with Seaborn
  • Categorical Plots – Distributions within Categories – Understanding Plot Types
  • Categorical Plots – Distributions within Categories – Coding with Seaborn
  • Seaborn – Comparison Plots – Understanding the Plot Types
  • Seaborn – Comparison Plots – Coding with Seaborn
  • Seaborn Grid Plots

Data Analysis and Visualization Capstone Project Exercise:

  • Capstone Project Overview
  • Capstone Project Solutions –

Machine Learning Concepts Overview:

  • Introduction to Machine Learning Overview Section
  • Why Machine Learning?
  • Types of Machine Learning Algorithms
  • Supervised Machine Learning Process
  • Companion Book – Introduction to Statistical Learning

Linear Regression:

  • Introduction to Linear Regression Section
  • Linear Regression – Algorithm History
  • Linear Regression – Understanding Ordinary Least Squares
  • Linear Regression – Cost Functions
  • Linear Regression – Gradient Descent
  • Python coding Simple Linear Regression
  • Overview of Scikit-Learn and Python
  • Linear Regression – Scikit-Learn Train Test Split
  • Linear Regression – Scikit-Learn Performance Evaluation – Regression
  • Linear Regression – Residual Plots
  • Linear Regression – Model Deployment and Coefficient Interpretation
  • Polynomial Regression – Theory and Motivation
  • Polynomial Regression – Creating Polynomial Features
  • Polynomial Regression – Training and Evaluation
  • Bias Variance Trade-Off
  • Polynomial Regression – Choosing Degree of Polynomial
  • Polynomial Regression – Model Deployment
  • Regularization Overview
  • Feature Scaling
  • Introduction to Cross Validation
  • Regularization Data Setup
  • L2 Regularization – Ridge Regression Theory
  • L2 Regularization – Ridge Regression – Python Implementation
  • L1 Regularization – Lasso Regression – Background and Implementation
  • L1 and L2 Regularization – Elastic Net
  • Linear Regression Project – Data Overview

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