What you'll learn
The most effective method to dodge issues with Machine Learning, to effectively execute it without losing your brain!
To realise what issues Machine Learning can illuminate, and how the Machine Learning Process functions
Use Python for Machine Learning
Percentiles, moment and Quantiles
Learn to utilise MatDescriptionlib for Python Descriptionting
Learn to utilise Seaborn for measurable Descriptions
Understand matrix multiplication, Matrix operations and scalar operations
Use Pair Description and limitations
Implement Identity matrix, matrix inverse properties, transpose of matrix, Vector multiplication
Implement Linear Regression, Multiple Linear Regression, Polynomial Regression, Decision Tree Regression, Random Forest Regression
AdaBoost and XGBoost regressor, SVM (regression) Background, SVR under Python
ML Concept-k-Fold validation, GridSearch
Classification-k-nearest neighbours' algorithm (KNN)
Gaussian Naive Bayes under python & visualization of models
Learn evaluation techniques using curves (ROC, AUC, PR, CAP)
Implement machine learning algorithms
More topics coming soon
Description
In this course, we will cover the fundamentals of Machine Learning, such as complex algorithms, calculations, and coding libraries, in a simple, straightforward manner. Each segment of Machine Learning would be broken down and would be easy to grasp. Throughout this course, you would understand the basics of Machine Learning, and by the end of the course, you would have gained enough knowledge to be able to call yourself an expert in Machine Learning. Step by step, you will build up new skills as well as further improving your understanding of machine learning. Those with prior programming experience would find that this course might clarify some concepts and aid in fully mastering Machine Learning.
Machine Learning - An Introduction
Machine learning is a field of artificial intelligence (AI) and computer science that focuses on using data and algorithms to mimic the way people learn, intending to steadily improve accuracy. Machine Learning is quite an exciting field. Machine Learning allows software applications to make predictions or decisions based on models and algorithms; as the software comes across more data, it is able to adapt accordingly without being programmed to do so. Initially, Machine Learning was time-consuming, tedious, and inefficient, that it was regarded as unfeasible for any practical use. However, major breakthroughs in the 90s paved the way for Machine Learning to perform efficiently and eventually made machine learning feasible and be able to be used in many software services and applications. Nowadays, Machine Learning is used in various industries and organizations, including government, retail, transportation, and health care.
Deep Learning
Machine learning is a computer's ability to execute tasks without being explicitly programmed yet thinking and acting like machines. Their capacity to do some complicated tasks - such as collecting data from an image or video - is still far behind that of humans. Because they've been particularly patterned after the human brain, deep learning models bring an exceptionally complex approach to machine learning and are prepared to solve these challenges. Data is transferred between nodes (like neurons) in highly linked ways using complex, multi-layered "deep neural networks."
Deep learning is a part of machine learning methods that are based on artificial neural networks (ANN) and representation learning. It can be either supervised, semi-supervised or unsupervised. Deep learning connects advancements in computer power with specialized neural networks to learn complex patterns from massive amounts of data. Deep learning's influence on the industry started back in the early 2000s when CNN's processed an approximated 10% to 20% of all checks made in the entire United States. Deep learning applications for large-scale voice recognition emerged around 2010.
Why is Machine Learning So Important?
The resurging interest in machine learning can be attributed to the fact the vast volumes and varieties of data are now available more than ever, combined with cheaper computational processing and more affordable data storage. All businesses rely on data to function. Data-driven choices are increasingly determining whether a company keeps up with the competition or falls further behind. Many sectors are now working to develop more robust machine learning models capable of evaluating larger and more complex data while delivering faster, more accurate answers on massive sizes. Machine learning algorithms help businesses identify valuable opportunities and potential risks more quickly. It has the power to unveil the value of corporate and consumer data, which enables companies to make decisions that keep them ahead of the competition. Machine learning is the ideal approach to develop models, strategize, and plan in industries that rely on large amounts of data and need a system to evaluate it rapidly and effectively. Machine Learning (ML) is applied in almost every type of industry, including retail, healthcare, life sciences, travel and hospitality, feedstock, and manufacturing. As there are many applications for Machine Learning, you would find multiple career opportunities in Machine Learning without fear of it becoming saturated.
If you're optimistic about reaping the benefits of having Machine Learning skills under your belt, then this course is for you!
Learn a Powerful Skill from the Comfort of Your Home
This course is one of the best courses available for Machine Learning. Theoretical knowledge, although important, is not sufficient to succeed in Machine Learning. Often companies would require that you have some experience in Machine Learning first. Practical application is required if you are to master Machine Learning skills. This course will empower you by providing a way to practice Machine Learning concepts from the comfort of your home, granting you some worthwhile experience with Machine Learning. Mastering Machine Learning would make you a valuable asset for many companies to have as it is in high demand. People who are well-versed in Machine Learning can choose to become either Data Scientists, Machine Learning Engineers, or Computer Vision Specialists.
Why You Should Choose this Course?
Machine Learning is a very complex and challenging field of study. It can be very daunting to some due to this fact. However, my course does well to break down Machine Learning in a way that is easier to grasp. Backed by my experience as an application developer combined with seven years of experience of teaching IT to over 140,000 satisfied students, I believe that my in-depth knowledge of the industry and vast teaching experience would be an immense help to you in grasping the fundamentals of Machine Learning as well as mastering it.
Assisting you in mastering Machine Learning free of issues is my top priority. You would find that I adopt a unique teaching style that is distinct from others in that it is highly straightforward and simple to understand, following a step-by-step approach. If for any reason, you find the course content confusing or difficult to understand, you can contact me to clear your doubts. I will gladly be available to tend to your doubts.
What You'll Learn:
· Effective and efficient machine learning methods which are executed devoid of any issues
· Issues that can be solved through Machine Learning
· How Machine Learning can be used to process functions
· Use Python for Machine Learning
· Percentiles, moment and quantiles
· Learn to utilize MatDescriptionlib for Python Descriptionting
· Learn to utilize Seaborn for measurable Descriptions
· Learn Advance mathematics for Machine Learning
· Understand matrix multiplication, Matrix operations, and scalar operations
· Use Pair Description and limitations
· Implement Identity matrix, matrix inverse properties, transpose of a matrix, and Vector multiplication
· Implement Linear Regression, Multiple Linear Regression, Polynomial Regression, Decision Tree Regression, Random Forest Regression
· AdaBoost and XGBoost regressor, SVM (regression) Background, SVR under Python
· ML Concept-k-Fold validation, GridSearch
· Classification-k-nearest neighbours algorithm(KNN)
· Gaussian Naive Bayes under python & visualization of models
· Learn evaluation techniques using curves (ROC, AUC, PR, CAP)
· Implement machine learning algorithms
· More topics coming soon
No Question Asked - Money Back Guarantee!
The main barrier towards people paying for a course to learn a daunting, challenging skill is whether it is suitable for them or whether they would be able to benefit from it. However, you can be at peace with the fact that you can opt out of this Machine Learning tutorial whenever you want to within 30 days. Basically, there is minimal risk involved with purchasing this course as it comes with a 30-day money-back guarantee. Once you purchase the course and later find that for any reason you are not satisfied with the course, you are entitled to a full refund, no questions asked.
Now that you know that you've got nothing to lose, what are you waiting for? Purchase this course now and get access to a Machine Learning master class that gives you a step-by-step approach to Machine Learning.
By the end of this course, you would have Machine Learning at the tip of your fingers, along with the skills necessary to enter the high-paying and in-demand field of Data Science.
Join me on this adventure today! See you on the course.
Who this course is for:
Anyone curious about Machine Learning or AI
Students who have a minimum of high school knowledge in math and who need to begin learning Machine Learning
People with a vested interest in studying Machine Learning but have trouble understanding how to code
College Students looking to build up a career in Data Science
Data Analysts looking to upskill themselves through Machine learning
Software developers or programmers looking to transition into the Machine Learning career path

Homepage

https://www.udemy.com/course/machine-learning-tutorial/