Python Build Machine Learning Models In 6 Hours

What you'll learn
Learn the core concepts of machine learning in Python
Clean your data to optimize how it feeds into your machine learning models
Perform regression in a supervised learning setting, so that you can predict numbers, prices, and conversion rates
Perform classification in a supervised-learning setting, teaching the model to distinguish between different plants, discussion topics, and objects
Measure and evaluate your Machine-Learning pipeline, so that you can improve your solution over time
Read, explore, clean, and prepare your data using Pandas, the most popular library for analyzing data tables
Use the Scikit-Learn library to deploy ready-built models, train them, and see results in just a few lines of code
Use hyper-parameter optimization to get the best possible version of each model for your specific application
Requirements
Some knowledge of mathematics and Python is assumed.
Description
Given the constantly increasing amounts of data they're faced with, programmers and data scientists have to come up with better solutions to make machines smarter and reduce manual work along with finding solutions to the obstacles faced in between. Python comes to the rescue to craft better solutions and process them effectively.This comprehensive 2-in-1 course teaches you how to perform different machine learning tasks along with fixing common machine learning problems you face in your day-to-day tasks. You will learn how to use labeled datasets to classify objects or predict future values, so that you can provide more accurate and valuable analysis. You will also use unlabelled datasets to do segmentation and clustering, so that you can separate a large dataset into sensible groups. Further to get a complete hold on the technology, you will work with tools using which you can build predictive models in Python.This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.In the first course, Getting Started with Machine Learning in Python, you will learn how to use labeled datasets to classify objects or predict future values, so that you can provide more accurate and valuable analysis. You will then use unlabelled datasets to do segmentation and clustering, so that you can separate a large dataset into sensible groups. You will also learn to understand and estimate the value of your dataset. Next, you will learn how to clean data for your application, and how to recognize which machine learning task you are dealing with.The second course, Building Predictive Models with Machine Learning and Python, will introduce you to tools with which you can build predictive models with Python, the core of a Data Scientist's toolkit. Through some really interesting examples, the course will take you through a variety of challenges: predicting the value of a house in Boston, the batting average of a baseball player, their survival chances had they been on the Titanic, or any other number of other interesting problems.By the end of this course, you will be able to take the Python machine learning toolkit and apply it to your own projects to build and deploy machine learning models in just a few lines of code. Meet Your Expert(s):We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:Colibri Digital is a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help its clients navigate the rapidly changing and complex world of emerging technologies, with deep expertise in areas such as big data, data science, Machine Learning, and cloud computing. Over the past few years, they have worked with some of the world's largest and most prestigious companies, including a tier 1 investment bank, a leading management consultancy group, and one of the world's most popular soft drinks companies, helping each of them to make better sense of its data, and process it in more intelligent ways. The company lives by its motto: Data -> Intelligence -> Action.Rudy Lai is the founder of QuantCopy, a sales acceleration startup using AI to write sales emails to prospects. By taking in leads from your pipelines, QuantCopy researches them online and generates sales emails from that data. It also has a suite of email automation tools to schedule, send, and track email performance—key analytics that all feed-back into how our AI generates content. Prior to founding QuantCopy, Rudy ran HighDimension.IO, a Machine Learning consultancy, where he experienced firsthand the frustrations of outbound sales and prospecting. As a founding partner, he helped startups and enterprises with HighDimension.IO's Machine-Learning-as-a-Service, allowing them to scale up data expertise in the blink of an eye. In the first part of his career, Rudy spent 5+ years in quantitative trading at leading investment banks such as Morgan Stanley. This valuable experience allowed him to witness the power of data, but also the pitfalls of automation using data science and Machine Learning. Quantitative trading was also a great platform from which to learn about reinforcement learning in depth, and supervised learning topics in a commercial setting. Rudy holds a Computer Science degree from Imperial College London, where he was part of the Dean's List, and received awards such as the Deutsche Bank Artificial Intelligence prize.
Overview
Section 1: Getting Started with Machine Learning in Python
Lecture 1 The Course Overview
Lecture 2 Machine Learning versus Rule-Based Programming
Lecture 3 Understanding What Machine Learning Can Do Using the Tasks Framework
Lecture 4 Creating Machine-Learned Models with Python and scikit-learn
Lecture 5 Supervised Versus Unsupervised Learning
Lecture 6 Fix your machine learning models by understanding your data source
Lecture 7 Dealing with Missing Values – An Example
Lecture 8 Standardization and Normalization to Deal with Variables with Different Scales
Lecture 9 Eliminating Duplicate Entries
Lecture 10 How Do We Learn Rules to Classify Objects?
Lecture 11 Understanding Logistic Regression – Your First Classifier
Lecture 12 Applying Logistic Regression to the Iris Classification Task
Lecture 13 Closing Our First Machine Learning Pipeline with a Simple Model Evaluator
Lecture 14 Creating Formulas That Predict the Future – A House Price Example
Lecture 15 Understanding Linear Regression – Your First Regressor
Lecture 16 Applying Linear Regression to the Boston House Price Task
Lecture 17 Evaluating Numerical Predictions with Least Squares
Lecture 18 Exploring Unsupervised Learning and Its Usefulness
Lecture 19 Finding Groups Automatically with K-means Clustering
Lecture 20 Reducing the Number of Variables in Your Data with PCA
Lecture 21 Smooth out Your Histograms with Kernel Density Estimation
Lecture 22 Create Explainable Models with Decision Trees
Lecture 23 Automatic Feature Engineering with Support Vector Machines
Lecture 24 Deal with Nonlinear Relationships with Polynomial Regression
Lecture 25 Reduce the Number of Learned Rules with Regularization
Section 2: Building Predictive Models with Machine Learning and Python
Lecture 26 The Course Overview
Lecture 27 Introduction to Machine Learning
Lecture 28 Meet the Python Machine Learning Stack
Lecture 29 Making Sure It Works in Your Computer
Lecture 30 Exploring Your First Dataset
Lecture 31 Building Your First Model
Lecture 32 Assessing Your Model
Lecture 33 Finding Issues with Your Data
Lecture 34 Using Pandas to Get Your Data Ready for Modeling
Lecture 35 Building a Model to Assess Your Chances of Surviving the Titanic
Lecture 36 What Makes Models Truly Different?
Lecture 37 Understanding the Advantages and Shortcomings of the Most Popular Models
Lecture 38 Trying (and Failing) to Use an SVM, a Random Forest and a Linear Model
Lecture 39 Fixing Our Issues with Our SVM Model
Lecture 40 Fixing Our Issues with the Random Forest Model
Lecture 41 What Does it Mean to Tune a Model (Theory)?
Lecture 42 Grid Search – Just Try Everything!
Lecture 43 Tune a Linear Model to Predict House Prices
Lecture 44 Tune an SVM to Predict a Politician's Party Based on Their Voting Record
Lecture 45 Advanced Libraries for Machine Learning
Lecture 46 Good Next Steps – Kaggle, Hackathons, YouTube Channels, and More
This course is aimed at novice data scientists and developers who want to get started with machine learning in Python. Developers who are curious about building and deploying machine learning-based models will find that this course will guide them to understand why some models are better than others at tackling certain challenges.


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