Become a data scientist in the tech industry! Comprehensive data mining and machine learning course with Python and Spark
About This Video
In …
Become a data scientist in the tech industry! Comprehensive data mining and machine learning course with Python and Spark
About This Video
In Detail
This course starts with a Python crash course and then shows you how to get set up on Microsoft Windows-based PCs, Linux desktops, and Macs. After setup, we will cover the machine learning, AI, and data mining techniques real employers are looking for, including deep learning / neural networks with TensorFlow and Keras; generative models with variational auto-encoders and generative adversarial networks; data visualization in Python with Matplotlib and Seaborn; transfer learning, sentiment analysis, image recognition, and classification; regression analysis, K-Means Clustering, Principal Component Analysis, train/test and cross-validation, Bayesian methods, decision trees and random forests.
We will also cover multiple regression, multi-level models, support vector machines, reinforcement learning, collaborative filtering, K-Nearest Neighbor, bias/variance tradeoff, ensemble learning, term frequency / inverse document frequency, experimental design, and A/B tests, feature engineering, hyperparameter tuning, and much more! There’s also an entire section on machine learning with Apache Spark, which lets you scale up these techniques to “big data” analyzed on a computing cluster.
By the end of this course, you will be able to become a professional data scientist.
Audience
Software developers or programmers who want to transition into the lucrative data science career path will learn a lot from this course. Data analysts in finance or other non-tech industries who want to transition into the tech industry can use this course to learn how to analyze data using code instead of tools.
You will need some prior experience in coding or scripting to be successful. If you have no prior coding or scripting experience, you should not take this course as we have covered the introductory Python course in the earlier sections.
Chapter 1 : Getting Started
Introduction
[Activity] Windows: Installing and Using Anaconda and Course Materials
[Activity] MAC: Installing and Using Anaconda and Course Materials
[Activity] Linux: Installing and Using Anaconda and Course Materials
Python Basics, Part 1 [Optional]
[Activity] Python Basics, Part 2 [Optional]
[Activity] Python Basics, Part 3 [Optional]
[Activity] Python Basics, Part 4 [Optional]
Introducing the Pandas Library [Optional]
Chapter 2 : Statistics and Probability Refresher, and Python Practice
Types of Data (Numerical, Categorical, Ordinal)
Mean, Median, Mode
[Activity] Using Mean, Median, and Mode in Python
[Activity] Variation and Standard Deviation
Probability Density Function; Probability Mass Function
Common Data Distributions (Normal, Binomial, Poisson, and So On)
[Activity] Percentiles and Moments
[Activity] A Crash Course in matplotlib
[Activity] Advanced Visualization with Seaborn
[Activity] Covariance and Correlation
[Exercise] Conditional Probability
Exercise Solution: Conditional Probability of Purchase by Age
Bayes’ Theorem
Chapter 3 : Predictive Models
[Activity] Linear Regression
[Activity] Polynomial Regression
[Activity] Multiple Regression and Predicting Car Prices
Multi-Level Models
Chapter 4 : Machine Learning with Python
Supervised Versus Unsupervised Learning, and Train/Test
[Activity] Using Train/Test to Prevent Overfitting a Polynomial Regression
Bayesian Methods: Concepts
[Activity] Implementing a Spam Classifier with Naive Bayes
K-Means Clustering
[Activity] Clustering People Based on Income and Age
Measuring Entropy
[Activity] Windows: Installing GraphViz
[Activity] MAC: Installing GraphViz
[Activity] Linux: Installing GraphViz
Decision Trees: Concepts
[Activity] Decision Trees: Predicting Hiring Decisions
Ensemble Learning
[Activity] XGBoost
Support Vector Machines (SVM) Overview
[Activity] Using SVM to Cluster People Using Scikit-Learn
Chapter 5 : Recommender Systems
User-Based Collaborative Filtering
Item-Based Collaborative Filtering
[Activity] Finding Movie Similarities Using Cosine Similarity
[Activity] Improving the Results of Movie Similarities
[Activity] Making Movie Recommendations with Item-Based Collaborative Filtering
[Exercise] Improve the Recommender’s Results
Chapter 6 : More Data Mining and Machine Learning Techniques
K-Nearest-Neighbors: Concepts
[Activity] Using KNN to Predict a Rating for a Movie
Dimensionality Reduction; Principal Component Analysis (PCA)
[Activity] PCA Example with the Iris Dataset
Data Warehousing Overview: ETL and ELT
Reinforcement Learning
[Activity] Reinforcement Learning and Q-Learning with Gym
Understanding a Confusion Matrix
Measuring Classifiers (Precision, Recall, F1, ROC, AUC)
Chapter 7 : Dealing with Real-World Data
Bias/Variance Tradeoff
[Activity] K-Fold Cross-Validation to Avoid Overfitting
Data Cleaning and Normalization
[Activity] Cleaning Web Log Data
Normalizing Numerical Data
[Activity] Detecting Outliers
Feature Engineering and the Curse of Dimensionality
Imputation Techniques for Missing Data
Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE
Binning, Transforming, Encoding, Scaling, and Shuffling
Chapter 8 : Apache Spark: Machine Learning on Big Data
[Activity] Installing Spark - Part 1
[Activity] Installing Spark - Part 2
Spark Introduction
Spark and the Resilient Distributed Dataset (RDD)
Introducing MLLib
Introduction to Decision Trees in Spark
[Activity] K-Means Clustering in Spark
TF / IDF
[Activity] Searching Wikipedia with Spark
[Activity] Using the Spark DataFrame API for MLLib
Chapter 9 : Experimental Design / ML in the Real World
Deploying Models to Real-Time Systems
A/B Testing Concepts
T-Tests and P-Values
[Activity] Hands-On with T-Tests
Determining How Long to Run an Experiment
A/B Test Gotchas
Chapter 10 : Deep Learning and Neural Networks
Deep Learning Prerequisites
The History of Artificial Neural Networks
[Activity] Deep Learning in the TensorFlow Playground
Deep Learning Details
Introducing TensorFlow
[Activity] Using TensorFlow, Part 1
[Activity] Using TensorFlow, Part 2
[Activity] Introducing Keras
[Activity] Using Keras to Predict Political Affiliations
Convolutional Neural Networks (CNNs)
[Activity] Using CNNs for Handwriting Recognition
Recurrent Neural Networks (RNNs)
[Activity] Using a RNN for Sentiment Analysis
[Activity] Transfer Learning
Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters
Deep Learning Regularization with Dropout and Early Stopping
The Ethics of Deep Learning
Chapter 11 : Generative Models
Variational Auto-Encoders (VAEs) - How They Work
Variational Auto-Encoders (VAE) - Hands-On with Fashion MNIST
Generative Adversarial Networks (GANs) - How They Work
Generative Adversarial Networks (GANs) - Playing with Some Demos
Generative Adversarial Networks (GANs) - Hands-On with Fashion MNIST
Learning More about Deep Learning
Chapter 12 : Final Project
Your Final Project Assignment: Mammogram Classification
Final Project Review
Chapter 13 : You Made It!
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