Hands-on Data Science course with Python Machine Learning
About Data science
Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science professionals apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights that analysts and business users can translate into tangible business value.
Day by day more and more companies are coming to realize the importance of data science, AI, and machine learning. Regardless of industry or size, organizations that wish to remain competitive in the age of big data need to efficiently develop and implement data science capabilities or risk being left behind.
What is Machine learning?
Machine learning is a form of artificial intelligence that allows computer systems to learn from examples, data, and experience. Through enabling computers to perform specific tasks intelligently, Machine learning systems can carry out complex processes by learning from data, rather than following pre-programmed rules.
Recent years have seen exciting advances in machine learning, which have raised its capabilities across a suite of applications. Many people now interact with systems based on machine learning every day, for example in image recognition systems, which are being used on social media and also voice recognition systems, used by virtual personal assistants, and recommender systems, which are being used by online retailers.
Why Python for data science & machine learning?
Python offers a concise and readable code. While complex algorithms and versatile workflows stand behind machine learning and AI, Python’s simplicity allows developers to write reliable systems. Developers get to put all their effort into solving a Machine Learning problem instead of focusing on the technical nuances of the language.
From deployment to development and maintenance, Python helps developers be productive and confident about the software they’re building. Benefits that make Python the best fit for machine learning and AI-based projects include simplicity and consistency with access to great libraries and frameworks for AI and machine learning (ML), flexibility, platform independence, and a wide community. These add to the overall popularity of the language.
In many scenarios, Python is the programming language of choice for the daily tasks that data scientists and AI tackle, and is one of the top data science tools used across all industries. For data scientists who need to incorporate statistical code to integrate or production database data with web-based applications, Python is often the ideal choice. It is also exquisite for implementing algorithms, which is something that data scientists need to do often.
About the Hands-on Data Science course
If you have got some programming or scripting experience, this course will teach you the techniques used by real data scientists in the tech industry and prepare you for a move into this hot career path. This comprehensive course includes 68 lectures spanning almost 9 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice.
In this course each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon and also you can keep notes for future reference. You won’t find academic, deeply mathematical coverage of these algorithms in this course, The overall focus is on practical understanding and application of them.
This hands-on data science course covers:
- Regression analysis and Multivariate Regression
- K-Means Clustering and Principal Component Analysis
- Bayesian Methods and Decision Trees and Random Forests
- Multi-Level Models and Support Vector Machines
- Reinforcement Learning
- Collaborative Filtering
- Bias/Variance Tradeoff
- Ensemble Learning
- Term Frequency / Inverse Document Frequency
- Experimental Design and A/B Tests
There is 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.
If you are new to Python, don’t worry – the course starts with a crash course. If you’ve done some programming before, you should pick it up quickly.
If you’re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this hands-on data science course will teach you the basic techniques used by real-world industry data scientists.
- Introduction to Python basics, Pandas, Types of Data, Variation and Standard Deviation
- Probability Density Function; Probability Mass Function and Common Data Distributions
- Percentiles and Moments
- Data Visualization with Seaborn and Covariance and Correlation
- Exercise: Conditional Probability
- Bayes’ Theorem
- Linear, Polynomial and Multiple Regression
- Concepts: Multi-Level Models and Bayesian Methods
- Income and Age
- Measuring Entropy
- Decision Trees: Concepts and Decision Trees: Predicting Hiring Decisions
- Overview: Ensemble Learning and Support Vector Machines (SVM)
- Using SVM to Cluster People and User-Based Collaborative Filtering and Item-Based Collaborative Filtering, Concept: K-Nearest-Neighbors
- Dimensionality Reduction; Principal Component Analysis
- PCA Example with the Iris Data Set
- Data Warehousing; ETL and ELT and Reinforcement Learning and Hands-On data science with Q-Learning
- Bias / Variance Tradeoff and K-Fold Cross Validation with Data Cleaning and Normalization, Cleaning Web Log Data
- Normalizing Numerical Data and Detecting Outliers, Spark Introduction and the Resilient Distributed Dataset (RDD)
- .Introducing MLLib and Decision Trees in Spark, K-Means Clustering in Spark TF / IDF, A/B Testing Concepts, T-Tests and P-Values and Hands-On with T-Tests
- Using the Spark 2 DataFrame API for MLLib Deploying Models to Production
As a final project, you have to apply machine learning to classifying masses found in mammograms as benign or malignant.
Note: Your review matters
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- One can start without any programming background.
- Concise implementation of basic algorithms.
- Very informative lessons on regression & Reinforcement.
- This course needs to have in-depth exercises.
- Need more description on models (Specifically hyperparameters).
Specification: Hands-on Data Science and Python Machine Learning