Practical Supervised And Unsupervised Learning With Python

What you'll learn
Explore various Python libraries, including NumPy, Pandas, scikit-learn, Matplotlib, seaborn and Plotly.
Gain in-depth knowledge of Principle Component Analysis and use it to effectively manage noisy datasets.
Discover the power of PCA and K-Means for discovering patterns and customer profiles by analyzing wholesale product data
Visualize, interpret, and evaluate the quality of the analysis done using Unsupervised Learning.
Work with model families like recommender systems, which are immediately applicable in domains such as e-commerce and marketing.
Expand your expertise using various algorithms like regression, decision trees, clustering and many to become a much stronger Python developer.
Understand the concept of clustering and how to use it to automatically segment data.
Requirements
Prior Python programming experience is a requirement, whereas experience with Data Analysis and Machine Learning analysis will be helpful.
Description
Are you looking forward to developing rich Python coding practices with Supervised and Unsupervised Learning? Then this is the perfect course for you!Supervised Machine Learning is used in a wide range of industries across sectors such as finance, online advertising, and analytics, and it's here to stay. Supervised learning allows you to train your system to make pricing predictions, campaign adjustments, customer recommendations, and much more. Unsupervised Learning is used to find a hidden structure in unlabeled and unstructured data. On the other hand, supervised learning is used for analyzing structured data making use of statistical techniques. Python makes this easier with its libraries that can be used for Machine Learning. This Course covers modern tools and algorithms to discover and extract hidden yet valuable structure in your data through real-world examples. This course explains the most important Unsupervised Learning algorithms using real-world examples of business applications in Python code.This comprehensive 3-in-1 course follows a step-by-step approach to entering the world of Artificial Intelligence and developing Python coding practices while exploring Supervised Machine Learning. Initially, you'll learn the goals of Unsupervised Learning and also build a Recommendation Engine. Moving further, you'll work with model families like recommender systems, which are immediately applicable in domains such as e-commerce and marketing. Finally, you'll understand the concept of clustering and how to use it to automatically segment data.By the end of the course, you'll develop rich Python coding practices with Supervised and Unsupervised Learning through real-world examples.Contents and OverviewThis training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Hands-On Unsupervised Learning with Python, covers clustering and dimensionality reduction in Deep Learning using Python. This course will allow you to utilize Principal Component Analysis, and to visualize and interpret the results of your datasets such as the ones in the above description. You will also be able to apply hard and soft clustering methods (k-Means and Gaussian Mixture Models) to assign segment labels to customers categorized in your sample data sets.The second course, Hands-on Supervised Machine Learning with Python, covers developing rich Python coding practices while exploring supervised machine learning. This course will guide you through the implementation and nuances of many popular supervised machine learning algorithms while facilitating a deep understanding along the way. You'll embark on this journey with a quick course overview and see how supervised machine learning differs from unsupervised learning. Next, we'll explore parametric models such as linear and logistic regression, non-parametric methods such as decision trees, and various clustering techniques to facilitate decision-making and predictions. As we proceed, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you'll wrap up with a brief foray into neural networks and transfer learning. By the end of the video course, you'll be equipped with hands-on techniques to gain the practical know-how needed to quickly and powerfully apply these algorithms to new problems.The third course, Supervised and Unsupervised Learning with Python, covers an introduction to the world of Artificial Intelligence. Build real-world Artificial Intelligence (AI) applications to intelligently interact with the world around you, explore real-world scenarios, and learn about the various algorithms that can be used to build AI applications. Packed with insightful examples and topics such as predictive analytics and deep learning, this course is a must-have for Python developers.By the end of the course, you'll develop rich Python coding practices with Supervised and Unsupervised Learning through real-world examples.About the AuthorsStefan Jansen is a data scientist with over 10 years of industry experience in fintech, investment, and as an advisor to Fortune 500 companies and startups, focusing on data strategy, predictive analytics, and machine and deep learning. He has used Unsupervised Learning extensively to segment large customer bases, detects anomalies, apply topic modeling to large volumes of legal documents to automate due diligence, and to facilitate image recognition. He holds master degrees from Harvard University and Free University Berlin, a CFA charter, and has been teaching data science and statistics for several years.Taylor Smith is a machine learning enthusiast with over five years of experience who loves to apply interesting computational solutions to challenging business problems. Currently working as Principal Data Scientist, Taylor is also an active open source contributor and staunch Pythonista.Prateek Joshi is an artificial intelligence researcher, published author of five books, and TEDx speaker. He is the founder of Pluto AI, a venture-funded Silicon Valley start-up that builds analytics platforms for smart water management powered by deep learning. His work in this field has led to patents, tech demos, and research papers at major IEEE conferences. He has been an invited speaker at technology and entrepreneurship conferences including TEDx, AT&T Foundry, Silicon Valley Deep Learning, and Open-Silicon Valley. Prateek has also been featured as a guest author in prominent tech magazines. His tech blog has received more than 1.2-million page views from 200 over countries and has over 6,600+ followers. He frequently writes on topics such as artificial intelligence, Python programming, and abstract mathematics. He is an avid coder and has won many hackathons utilizing a wide variety of technologies. He graduated from the University of Southern California with a master's degree specializing in artificial intelligence. He has worked at companies such as Nvidia and Microsoft Research.
Overview
Section 1: Hands-On Unsupervised Learning with Python
Lecture 1 The Course Overview
Lecture 2 Benefits of Unsupervised Learning
Lecture 3 How Market Basket Analysis Works
Lecture 4 How Market Basket Analysis Works (Continued)
Lecture 5 The Apriori Algorithm – Preparing the Data
Lecture 6 Understanding and Implementing the Apriori Algorithm
Lecture 7 Finding Association Rules
Lecture 8 Visualizing and Interpreting Association Rules
Lecture 9 Unsupervised Learning and the Curse of Dimensionality
Lecture 10 Approaches to Dimensionality Reduction
Lecture 11 The Key Ideas Behind PCA
Lecture 12 The Key Ideas Behind PCA (Continued)
Lecture 13 The Linear Algebra Behind PCA
Lecture 14 The Linear Algebra Behind PCA (Continued)
Lecture 15 PCA in Practice
Lecture 16 PCA in Practice (Continued)
Lecture 17 Clustering – Key Concepts
Lecture 18 Clustering Algorithm in Practice
Lecture 19 Evaluate Clustering Results
Lecture 20 Case Study – K-Means and Wholesale Data
Lecture 21 Case Study – K-Means and Wholesale Data (Continued)
Section 2: Hands-on Supervised Machine Learning with Python
Lecture 22 The Course Overview
Lecture 23 Getting Our Machine Learning Environment Setup
Lecture 24 Supervised Learning
Lecture 25 Hill Climbing and Loss Functions
Lecture 26 Model Evaluation and Data Splitting
Lecture 27 Introduction to Parametric Models and Linear Regression
Lecture 28 Implementing Linear Regression from Scratch
Lecture 29 Introduction to Logistic Regression Models
Lecture 30 Implementing Logistic Regression from Scratch
Lecture 31 Parametric Models –Pros/Cons
Lecture 32 The Bias/Variance Trade-off
Lecture 33 Introduction to Non-Parametric Models and Decision Trees
Lecture 34 Decision Trees
Lecture 35 Implementing a Decision Tree from Scratch
Lecture 36 Various Clustering Methods
Lecture 37 Implementing K-Nearest Neighbors from Scratch
Lecture 38 Non-Parametric Models –Pros/Cons
Lecture 39 Recommender Systems and an Introduction to Collaborative Filtering
Lecture 40 Matrix Factorization
Lecture 41 Matrix Factorization in Python
Lecture 42 Content-Based Filtering
Lecture 43 Neural Networks and Deep Learning
Lecture 44 Neural Networks
Lecture 45 Use Transfer Learning
Section 3: Supervised and Unsupervised Learning with Python
Lecture 46 The Course Overview
Lecture 47 Artificial Intelligence and Its Need
Lecture 48 Applications and Branches of AI
Lecture 49 Defining Intelligence Using Turing Test
Lecture 50 Making Machines Think Like Humans
Lecture 51 General Problem Solver
Lecture 52 Building an Intelligent Agent
Lecture 53 Installing Python 3 and Packages
Lecture 54 Loading Data
Lecture 55 Supervised Versus Unsupervised Learning
Lecture 56 What is Classification?
Lecture 57 Preprocessing Data
Lecture 58 Label Encoding
Lecture 59 Logistic Regression and Naïve Bayes Classifier
Lecture 60 Confusion Matrix
Lecture 61 Support Vector Machines
Lecture 62 Classifying Income Data
Lecture 63 What is Regression?
Lecture 64 Building a Single and Multivariable Regressor
Lecture 65 Estimating Housing Prices
Lecture 66 What is Ensemble Learning?
Lecture 67 What Are Decision Trees
Lecture 68 What are Random and Extremely Random Forests?
Lecture 69 Dealing with Class Imbalance
Lecture 70 Finding Optimal Training Parameters
Lecture 71 Computing Relative Feature Importance
Lecture 72 Predicting Traffic
Lecture 73 Clustering Data with K-Means Algorithm
Lecture 74 Estimating the Number of Clusters
Lecture 75 Estimating the Quality of Clustering
Lecture 76 Building a Classifier
Lecture 77 Segmenting the Market
Lecture 78 Creating a Training Pipeline
Lecture 79 Extracting the Nearest Neighbors
Lecture 80 Building a K-Nearest Neighbors Classifier
Lecture 81 Computing similarity scores
Lecture 82 Finding Similar Users
Lecture 83 Building a Movie Recommendation System
Data Analysts, Data Scientists, Developers who want to understand key applications of Supervised & Unsupervised Learning from both a conceptual and practical point of view.


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