Created by: Osama Ajmal
Learn Data Science and Machine Learning using Python. Data Science Practical Applications and Machine Learning Projects!
What you’ll learn
Data Science using Python
Data Science Applications
Python libraries such as NumPy, Pandas, Matplotlib, Pyplot for Data Science
Analyzing real world data using Python for Data Science and Machine Learning
Fundamental Python skills
Machine Learning Mathematics and Statistics in detail
Machine Learning applications
Requirements
Passion!
Good internet
Description
Data Science and Machine Learning
28% Demand Increase by 2020 || 4,524 Number of Job Openings || $120,931 Average Base Salary || #1 Best Job in America 2016, 2017, 2018
A Big YES, Data Science is a good career option.
The U.S. Bureau of Labor Statistics reports that the rise of data science needs will create 11.5M job openings by 2026. According to IBM the demand for Data Scientists will increase up to 28% by the year 2020
What is Data Science?
Use of the term Data Science is increasingly common, but what does it exactly mean? What skills do you need to become Data Scientist? What is the difference between BI and Data Science? How are decisions and predictions made in Data Science? These are some of the questions that will be answered further.
First, let’s see what is Data Science. Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. How is this different from what statisticians have been doing for years?
The answer lies in the difference between explaining and predicting.
Data scientists are analytical data experts who have the technical skills to solve complex problems – and the curiosity to explore what problems need to be solved.
Data science is a multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems.
Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations.
Effective data scientists are able to identify relevant questions, collect data from a multitude of different data sources, organize the information, translate results into solutions, and communicate their findings in a way that positively affects business decisions. These skills are required in almost all industries, causing skilled data scientists to be increasingly valuable to companies.
Glassdoor ranked data scientist as the #1 Best Job in America in 2018 for the third year in a row. As increasing amounts of data become more accessible, large tech companies are no longer the only ones in need of data scientists. The growing demand for data science professionals across industries, big and small, is being challenged by a shortage of qualified candidates available to fill the open positions.
The need for data scientists shows no sign of slowing down in the coming years. LinkedIn listed data scientist as one of the most promising jobs in 2017 and 2018, along with multiple data-science-related skills as the most in-demand by companies.
Unlike some other programming languages, in Python, there is generally a best way of doing something. The three best and most important Python libraries for data science are NumPy, Pandas, and Matplotlib.
NumPy and Pandas are great for exploring and playing with data. Matplotlib is a data visualization library that makes graphs like you’d find in Excel or Google Sheets.
Why learn Python?
Python is an object-orientated language that closely resembles the English language which makes it a great language to learn for beginners as well as seasoned professionals.
Examples sites that use Python are Instagram, YouTube, Reddit, NASA, IBM, Nokia, etc.
Python is one of the most widely used programming languages in the AI field of Artificial Intelligence thanks to its simplicity. It can seamlessly be used with the data structures and other frequently used AI algorithms.
Advantages of Python
GUI based desktop applications
Image processing and graphic design applications
Scientific and computational applications
Games
Web frameworks and web applications
Enterprise and business applications
Operating systems
Language development
Prototyping
Whenever you’re faced with a problem and are figuring out how to do it, there will be multiple well-documented ways.
You can become productive in Python fairly quickly even as a beginner, yet it will serve you in industry like a champ too!
1) Python can be used to develop prototypes, and quickly because it is so easy to work with and read.
2) Most automation, data mining, and big data platforms rely on Python. This is because it is the ideal language to work with for general purpose tasks.
3) Python allows for a more productive coding environment than massive languages like C# and Java. Experienced coders tend to stay more organized and productive when working with Python, as well.
4) Python is easy to read, even if you’re not a skilled programmer. Anyone can begin working with the language, all it takes is a bit of patience and a lot of practice. Plus, this makes it an ideal candidate for use among multi-programmer and large development teams.
5) Python powers Django, a complete and open source web application framework. Frameworks – like Ruby on Rails – can be used to simplify the development process.
Do you want to become a Data Scientist? Are you willing to learn Machine Learning? Well you’re at the right place!!
The average salary for a Machine Learning Engineer is $138,920 per year in the United States by Indeed.
Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to “learn” (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed ~ by Wikipedia.
Machine learning can easily consume unlimited amounts of data with timely analysis and assessment. This method helps review and adjusts your message based on recent customer interactions and behaviors. Once a model is forged from multiple data sources, it has the ability to pinpoint relevant variables. This prevents complicated integrations, while focusing only on precise and concise data feeds.
Machine learning algorithms tend to operate at expedited levels. In fact, the speed at which machine learning consumes data allows it to tap into burgeoning trends and produce real-time data and predictions
1. Churn analysis – it is imperative to detect which customers will soon abandon your brand or business. Not only should you know them in depth – but you must have the answers for questions like “Who are they? How do they behave? Why are They Leaving and What Can I do to keep them with us?”
2. Customer leads and conversion – you must understand the potential loss or gain of any and all customers. In fact, redirect your priorities and distribute business efforts and resources to prevent losses and refortify gains. A great way to do this is by reiterating the value of customers in direct correspondence or via web and mail-based campaigns.
3. Customer defections – make sure to have personalized retention plans in place to reduce or avoid customer migration. This helps increase reaction times, along with anticipating any non-related defections or leaves.
Many hospitals use this data analysis technique to predict admissions rates. Physicians are also able to predict how long patients with fatal diseases can live.
Insurance agencies across the world are also able to do the following:
Predict the types of insurance and coverage plans new customers will purchase.
Predict existing policy updates, coverage changes and the forms of insurance (such as health, life, property, flooding) that will most likely be dominant.
Predict fraudulent insurance claim volumes while establishing new solutions based on actual and artificial intelligence.
Machine learning is proactive and specifically designed for “action and reaction” industries. In fact, systems are able to quickly act upon the outputs of machine learning – making your marketing message more effective across the board.
So in this course Machine Learning, Data Science and Neural Networks + AI we will discover topics:
Introduction
Supervised Learning
Bayesian Decision Theory
Parametric Methods
Multivariate Methods
Dimensionality Reduction
Clustering
Nonparametric Methods
Decision Trees
McNemar’s Test
Hypothesis Testing
Bootstrapping
Temporal Difference Learning
Reinforcement Learning
Stacked Generalization
Combining Multiple Learners
d-Separation
Undirected Graphs: Markov Random Fields
Hidden Markov Models
Regression
Kernel Machines
Multiple Kernel Learning
Normalized Basis Functions
The Perceptron
and much more!!
Who this course is for:
People who are starting their careers in Data Science
Who wants to learn Data Science with Python
Who wants to jump start their career in Machine Learning
Who want to learn Python
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