Machine Learning, Data Science and Neural Networks, Learn Machine Learning, Data Science, Neural Networks, Artificial Intelligence, Deep Learning and much more!
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- 1.0 (1 rating)
- Created by Osama Ajmal
- English
- English [Auto-generated]
- 16 hours on-demand video
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
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What you'll learn
- Students will learn Introduction to Machine Learning
- They will learn what is Supervised and Unsupervised Learning
- They will learn Regression
- They will learn Bayesian Decision Theory
- They will learn Parametric Methods
- They will learn The Bayes’ Estimator
- They will learn Clustering
- They will learn Expectation-Maximization Algorithm and much more!
Description
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 is the target audience?
- Anyone who want to learn Machine Learning
- Anyone who want to become a Data Scientist
- Anyone who is interested in Artificial Intelligence
- Anyone who want to start their career in Data Science
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