What you'll learn
Use pre-written libraries in python to work with powerful algorithms.
Tips and tricks to speed up your modeling process and obtain better results.
Make predictions using advanced regression analysis with Python.
Modern techniques for solving supervised learning problems.
Build your own recommendation engine and perform collaborative filtering.
Eliminate common data wrangling problems in Pandas and scikit-learn.
Troubleshoot advanced models such as Random Forests and SVMs.
Wrangling with unsupervised learning and the curse of dimensionality.
Solving prediction visualization issues with Matplotlib.
Perform common natural language processing featuring engineering tasks.
Requirements
Prior Python programming experience is a requirement, whereas experience with Machine Learning concepts will be helpful.
Description
Machine learning is one of the most sought-after skills in the market giving you powerful insights into data. Today, implementations of Machine Learning are adopted throughout Industry and its concepts are many. Python makes this easier with its huge set of libraries that can be used for Machine Learning. The effective blend of Machine Learning with Python helps in implementing solutions to real-world problems as well as automating analytical model.This comprehensive 4-in-1 course follows a step-by-step practical approach to building powerful Machine Learning models using Python. Initially, you'll use pre-written libraries in python to work with powerful algorithms and get an intuitive understanding of where to use which machine learning approach. You'll explore Tips and tricks to speed up your modeling process and obtain better results. Moving further, you'll learn modern techniques for solving supervised learning problems. Finally, you'll eliminate common data wrangling problems in Pandas and scikit-learn as well as perform common natural language processing featuring engineering tasks.By the end of the course, you'll explore practical and unique solutions to common Machine Learning problems to avoid any roadblocks while working with the Python data science ecosystem.Contents and OverviewThis training program includes 4 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Python Machine Learning in 7 Days, covers building powerful Machine Learning models using Python with hands-on practical examples in just a week. In this course, you will be introduced to a new machine learning aspect in each section followed by a practical assignment as homework to help you in efficiently implement the learnings in a practical manner. With the systematic and fast-paced approach to this course, learn machine learning using Python in the most practical and structured way to develop machine learning projects in Python in a week. This course is structured to unlock the potential of Python machine learning in the shortest amount of time. If you are looking to upgrade your machine learning skills using Python in the quickest possible time, then this course is for you!The second course, Python Machine Learning Projects, covers hands-on Supervised, unsupervised learning, and more. This video is a unique blend of projects that teach you what Machine Learning is all about and how you can implement machine learning concepts in practice. Six different independent projects will help you master machine learning in Python. The video will cover concepts such as classification, regression, clustering, and more, all the while working with different kinds of databases. By the end of the course, you will have learned to apply various machine learning algorithms and will have mastered Python's packages and libraries to facilitate computation. You will be able to implement your own machine learning models after taking this course.The third course, Python Machine Learning Tips, Tricks, and Techniques, covers transforming your simple machine learning model into a cutting edge powerful version. In this course, you will learn from a top Kaggle master to upgrade your Python skills with the latest advancements in Python.It is essential to keep upgrading your machine learning skills as there are immense advancements taking place every day. In this course, you will get hands-on experience of solving real problems by implementing cutting-edge techniques to significantly boost your Python Machine Learning skills and, as a consequence, achieve optimized results in almost any project you are working on. Each technique we cover is itself enough to improve your results. However; combining them together is where the real magic is. Throughout the course, you will work on real datasets to increase your expertise and keep adding new tools to your machine learning toolbox. By the end of this course, you will know various tips, tricks, and techniques to upgrade your machine learning algorithms to reduce common problems, all the while building efficient machine learning models.The fourth course, Troubleshooting Python Machine Learning, covers quick fixes for all your Python Machine Learning frustrations. We have systematically researched common ML problems documented online around data wrangling, debugging models such as Random Forests and SVMs, and visualizing tricky results. We leverage statistics from Stack Overflow, Medium, and GitHub to get a cross-section of what data scientists struggle with. We have collated for you the top issues, such as retrieving the most important regression features and explaining your results after clustering, and their corresponding solutions. We present these case studies in a problem-solution format, making it very easy for you to incorporate this into your knowledge. Taking this course will help you to precisely debug your models and research pipelines, so you can focus on pitching new ideas and not fixing old bugs.By the end of the course, you'll explore practical and unique solutions to common Machine Learning problems to avoid any roadblocks while working with the Python data science ecosystem.About the AuthorsArish Ali started his machine learning journey 5 years ago by winning an all India machine learning competition conducted by the Indian Institute of Science and Microsoft. He was a data scientist at Mu Sigma, one of the biggest analytics firms in India. He has also worked on some of the cutting edge problems of Multi-Touch Attribution Modelling, Market Mix Modelling, and Deep Neural Networks. He has also been an Adjunct faculty for Predictive Business Analytics at Bridge School of Management, which offers its course in Predictive Business Analytics along with North-western University (SPS). Currently, he is working at a mental health startup called Bemo as an AI developer where his role is to help automate the therapy provided to users and make it more personalized.Alexander T. Combs is an experienced data scientist, strategist, and developer with a background in financial data extraction, natural language processing, and generation, and quantitative and statistical modeling. He is currently a full-time lead instructor for a data science immersive program in New York City.Valeriy Babushkin has done an M. Sc. and has 5+ years' experience in industrial data science and academia. He is a Kaggle competition master and a 2018 IEEE SP Cup finalist. He has been a Data Science Team Lead at Yandex (the largest search engine in Russia; it outperforms Google) and runs an online taxi service (he acquired Uber in Russia and 15 other countries) and the biggest e-commerce platform in Russia. He was also a Head of Data Science at Monetha. Monetha is creating a universal, transferable, immutable trust, and reputation system combined with a payment solution. Finally, he is decentralized and empowered by the Ethereum Blockchain.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 feedback into how our AI generated content. Prior to founding QuantCopy, Rudy ran High Dimension.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 High Dimension.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 to learn deeply about reinforcement learning 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: Python Machine Learning in 7 Days
Lecture 1 The Course Overview
Lecture 2 Setting Up Your Machine Learning Environment
Lecture 3 Exploring Types of Machine Learning
Lecture 4 Using Scikit-learn for Machine Learning
Lecture 5 Assignment – Train Your First Pre-built Machine Learning Model
Lecture 6 Supervised Learning Algorithm
Lecture 7 Architecture of a Machine Learning System
Lecture 8 Machine Learning Model and Its Components
Lecture 9 Linear Regression
Lecture 10 Predicting Weight Using Linear Regression
Lecture 11 Assignment – Predicting Energy Output of a Power Plant
Lecture 12 Review of Predicting Energy Output of a Power Plant
Lecture 13 Logistic Regression
Lecture 14 Classifying Images Using Logistic Regression
Lecture 15 Support Vector Machines
Lecture 16 Kernels in a SVM
Lecture 17 Classifying Images Using Support Vector Machines
Lecture 18 Assignment – Start Image Classifying Using Support Vector Machines
Lecture 19 Review of Classifying Images Using Support Vector Machines
Lecture 20 Model Evaluation
Lecture 21 Better Measures than Accuracy
Lecture 22 Understanding the Results
Lecture 23 Improving the Models
Lecture 24 Assignment – Getting Better Test Sample Results by Measuring Model Performance
Lecture 25 Review of Getting Better Test Sample Results by Measuring Model Performance
Lecture 26 Unsupervised Learning
Lecture 27 Clustering
Lecture 28 K-means Clustering
Lecture 29 Determining the Number of Clusters
Lecture 30 Assignment – Write Your Own Clustering Implementation for Customer Segmentation
Lecture 31 Review of Clustering Customers Together
Lecture 32 Why Neural Network
Lecture 33 Parts of a Neural Network
Lecture 34 Working of a Neural Network
Lecture 35 Improving the Network
Lecture 36 Assignment – Build a Sentiment Analyzer Based on Social Network Using ANN
Lecture 37 Review of Building a Sentiment Analyser ANN
Lecture 38 Decision Trees
Lecture 39 Working of a Decision Tree
Lecture 40 Techniques to Further Improve a Model
Lecture 41 Random Forest as an Improved Machine Learning Approach
Lecture 42 Weekend Task – Solving Titanic Problem Using Random Forest
Section 2: Python Machine Learning Projects
Lecture 43 The Course Overview
Lecture 44 Sourcing Airfare Pricing Data
Lecture 45 Retrieving the Fare Data with Advanced Web Scraping Techniques
Lecture 46 Parsing the DOM to Extract Pricing Data
Lecture 47 Sending Real-Time Alerts Using IFTTT
Lecture 48 Putting It All Together
Lecture 49 The IPO Market
Lecture 50 Feature Engineering
Lecture 51 Binary Classification
Lecture 52 Feature Importance
Lecture 53 Creating a Supervised Training Set with the Pocket App
Lecture 54 Using the embed.ly API to Download Story Bodies
Lecture 55 Natural Language Processing Basics
Lecture 56 Support Vector Machines
Lecture 57 IFTTT Integration with Feeds, Google Sheets, and E-mail
Lecture 58 Setting Up Your Daily Personal Newsletter
Lecture 59 What Does Research Tell Us about the Stock Market?
Lecture 60 Developing a Trading Strategy
Lecture 61 Building a Model and Evaluating Its Performance
Lecture 62 Modeling with Dynamic Time Warping
Lecture 63 Machine Learning on Images
Lecture 64 Working with Images
Lecture 65 Finding Similar Images
Lecture 66 Building an Image Similarity Engine
Lecture 67 The Design of Chatbots
Lecture 68 Building a Chatbot
Section 3: Python Machine Learning Tips, Tricks, and Techniques
Lecture 69 The Course Overview
Lecture 70 Using Feature Scaling to Standardize Data
Lecture 71 Implementing Feature Engineering with Logistic Regression
Lecture 72 Extracting Data with Feature Selection and Interaction
Lecture 73 Combining All Together
Lecture 74 Build Model Based on Real-World Problems
Lecture 75 Support Vector Machines
Lecture 76 Implementing kNN on the Data Set
Lecture 77 Decision Tree as Predictive Model
Lecture 78 Tricks with Dimensionality Reduction
Lecture 79 Combining All Together
Lecture 80 Random Forest for Classification
Lecture 81 Gradient Boosting Trees and Bayes Optimization
Lecture 82 CatBoost to Handle Categorical Data
Lecture 83 Implement Blending
Lecture 84 Implement Stacking
Lecture 85 Memory-Based Collaborative Filtering
Lecture 86 Item-to-Item Recommendation with kNN
Lecture 87 Applying Matrix Factorization on Datasets
Lecture 88 Wordbatch for Real-World Problem
Lecture 89 Validation Dataset Tuning
Lecture 90 Regularizing Model to Avoid Overfitting
Lecture 91 Adversarial Validation
Lecture 92 Perform Metric Selection on Real Data
Section 4: Troubleshooting Python Machine Learning
Lecture 93 The Course Overview
Lecture 94 Splitting Your Datasets for Train, Test, and Validate
Lecture 95 Persist Your Hard Earned Models by Saving Them to Disk
Lecture 96 Calculate Word Frequencies Efficiently in Good ol' Python
Lecture 97 Transform Your Variable Length Features into One-Hot Vectors
Lecture 98 Finding the Most Important Features in Your Classifier
Lecture 99 Predicting Multiple Targets with the Same Dataset
Lecture 100 Retrieving the Best Estimators after Grid Search
Lecture 101 Regress on Your Pandas Data Frame with Simple Statsmodels OLS
Lecture 102 Extracting Decision Tree Rules from scikit-learn
Lecture 103 Finding Out Which Features Are Important in a Random Forest Model
Lecture 104 Classifying with SVMs When Your Data Has Unbalanced Classes
Lecture 105 Computing True/False Positives/Negatives after in scikit-learn
Lecture 106 Labelling Dimensions with Original Feature Names after PCA
Lecture 107 Clustering Text Documents with scikit-learn K-means
Lecture 108 Listing Word Frequency in a Corpus Using Only scikit-learn
Lecture 109 Polynomial Kernel Regression Using Pipelines
Lecture 110 Visualize Outputs Over Two-Dimensions Using NumPy's Meshgrid
Lecture 111 Drawing Out a Decision Tree Trained in scikit-learn
Lecture 112 Clarify Your Histogram by Labelling Each Bin
Lecture 113 Centralizing Your Color Legend When You Have Multiple Subplots
This course is perfect for:,Data Scientists, Developers who are familiar with basic Python programming and want to build efficient, faster, and progressive Machine Learning models to tackle real data.


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