Machine Learning, Data Science and Deep Learning with Python
Frank Kane
  • Course duration: 12h 59m
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


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


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


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.

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