Machine Learning, Data Science and Deep Learning with Python covers machine learning, Tensorflow, artificial intelligence, and neural networks—all skills that are in demand from the biggest tech employers. Filled with examples using accessible Python code you can experiment with, this complete hands-on data science tutorial teaches you techniques used by real data scientists and prepares you for a move into this hot career path.
Distributed by Manning Publications
This course was created independently by big data expert Frank Kane and is distributed by Manning through our exclusive liveVideo platform.
About the subject
Skills like machine learning and data science are in high demand. Data scientists are among the highest paid technology professionals, with an average salary of $120,000 according to Glassdoor and Indeed. Easy to learn and simple to use, Python has become the language of choice for most data scientists. This popular language is driving powerful new developments in the field, such as deep learning, and is the perfect choice for data mining, AI, and other analysis techniques prized by businesses and employers.About the video
Machine Learning, Data Science and Deep Learning with Python teaches you the techniques used by real data scientists and machine learning practitioners in the tech industry, and prepares you for a move into this hot career path. Expert instructor Frank Kane draws on 9 years of experience at Amazon and IMDb to guide you through what matters in data science. The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech companies. You’ll cover data science skills employers are looking for, including data visualization in Python with MatPlotLib and Seaborn, transfer learning, sentiment analysis, and experimental design and A/B Tests. Each concept is introduced in plain English, and demonstrated using Python code you can experiment with and build upon. If you're new to Python, don't worry—the course starts with a crash course. When you’re done, you’ll be primed to switch into an exciting new data science career track in the tech industry!
Table of Contents detailed table of contents
Getting started
Introduction
WINDOWS: Installing and using sea & course materials
MAC: Installing and using anaconda & course materials
LINUX: Installing and using Anaconda & course materials
Python basics, Part 1 [Optional]
Python basics, Part 2 [Optional]
Python basics, Part 3 [Optional]
Python basics, Part 4 [Optional]
Introducing the Pandas library [Optional]
Statistics and Probability refresher, and Python practice
Types of data
Mean, median, mode
Using mean, median, and mode in Python
Variation and standard deviation
Probability density function; probability mass function
Common data distributions
Percentiles and moments
Matplotlib crash course
Advanced visualization with Seaborn
Covariance and correlation
Conditional probability exercise
Conditional probability solution
Bayes' theorem
Predictive models
Linear regression
Polynomial regression
Multiple regression, and predicting car prices
Multi-level models
Machine learning with Python
Supervised vs. Unsupervised learning, and train/test
Using train/test to prevent overfitting a polynomial
Bayesian methods: Concepts
Implementing a Spam Classifier with Naive Bayes
K-Means clustering
Clustering people based on income and age
Measuring entropy
WINDOWS: Installing Graphviz
LINUX: Installing Graphviz
MAC: Installing Graphviz
Decision trees: Concepts
Decision trees: Predicting hiring decisions
Ensemble learning
Support Vector Machines (SVM) Overview
Using SVM to cluster people using Scikit-learn
Recommender systems
User-based collaborative filtering
Item-based collaborative filtering
Finding movie similarities
Improving the results of movie similarities
Making movie recommendations to people
Improve the recommender’s results
More data mining and machine learning techniques
K-Nearest-Neighbors: Concepts
Using KNN to predict arating for a movie
Dimensionality reduction; Principal component analysis
Reinforcement learning
PCA example with the Iris data set
Data warehousing overview: ETL and ELT
Reinforcement learning & Q-learning with Gym
Dealing with real-world data
Bias/Variance tradeoff
K-Fold cross-validation to avoid overfitting
Data cleaning and normalization
Cleaning web log data
Normalizing numerical data
Detecting outliers
Apache Spark: Machine learning on big data
Installing Spark, Part 1
Installing Spark, Part 2
Spark introduction
Spark and the Resilient Distributed Dataset (RDD)
Introducing MLLib
Decision trees in Spark
K-Means clustering in Spark
TF / IDF
Searching Wikipedia with Spark
Using the Spark 2.0 DataFrame API for MLLib
Experimental design / ML in the real world
Deploying models to real-time systems
A/B testing concepts
T-Tests and P-Values
Hands-on with T-Tests
Determining how long to run an experiment
A/B test gotchas
Deep Learning and Neural Networks
Deep learning pre-requisites
The history of Artificial Neural Networks
Deep learning in the TensorFlow Playground
Deep learning details
Introducing TensorFlow
Using TensorFlow, Part 1
Using TensorFlow, Part 2
Introducing Keras
Using Keras to predict political affiliations
Convolutional Neural Networks(CNN’s)
Using CNN’s for handwriting recognition
Recurrent Neural Networks (RNN’s)
Using a RNN for sentiment analysis
Transfer learning
The ethics of deep learning
Learning more about deep learning
Final project
Your final project assignment
Final project review
You made it!
More to explore
Prerequisites
For viewers with basic coding or scripting skills, interested in moving into data science in the tech industry.What you will learn
- Build artificial neural networks with Tensorflow and Keras
- Make predictions using linear regression, polynomial regression, and multivariate regression
- Implement machine learning at massive scale with Apache Spark's MLLib
- Build a movie recommender system using item-based and user-based collaborative filtering
- Classify images, data, and sentiments using deep learning
About the instructor
Frank Kane holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. He spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to millions of customers every day. Sundog Software, his own company specializing in virtual reality environment technology and teaching others about big data analysis, is his pride and joy.placing your order...
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