What you would learn in Master Machine Learning and Data Science with Python course?
We are pleased to present the most comprehensive Machine Learning and Data Science with Python course in the world. Do you want to begin your journey toward being a Data Scientist?
In this thorough course, your journey begins with the installation process and learning the fundamentals of Python. When you're done, you'll be introduced to the Machine Learning section will give you a brief overview of the basics of what Machine Learning is all about and will cover all the essentials before settling on the first algorithm you've ever encountered. Learn a range of unsupervised and supervised machine learning algorithms, from linear regression to the famous increasing algorithms. Also, you'll be taught the process of recognizing text with Natural Language Processing, where you'll solve a challenging issue.
Data science has been acclaimed by many as being among the top jobs in the world, and it's booming at the moment. It's not just an extremely high potential for earnings; it also gives the possibility of working with leading companies worldwide. Data scientists also have the chance to tackle challenging issues, be indispensable to their employers, and be satisfied with changing the way companies make their decisions. Data science and machine learning are among the most rapidly growing and demanded skills worldwide, and demand for it proliferates. Additionally, Python is the most accessible and most popular programming language currently, and it's the first language option for Machine Learning. There isn't a better time to begin learning machine learning with Python than right now.
I designed this course with students who are beginners or already have previous programming experience in mind. You could be in the fields of Finance, Marketing, or Engineering fields, or perhaps a newbie in any of these fields; so long as you're eager to learn the basics of programming, this course is the first step towards becoming Data Scientist.
I can access more than 19 hours of the highest quality video content. There are more than 90 HD video lectures that range between 5 and 20 minutes in length on average. I've included quizzes to assess your knowledge following each subject to ensure you only leave the chapter after mastering the subject. In addition, I've also provided you with many exercises to help you practice what you've learned and solutions to the video exercises for you to evaluate the results. I've also included all exercises notebooks and solution notebooks data files and any other data in the folder of resources.
Here, I'm going to address the most crucial question. What are the reasons for selecting this course instead of others?
I will cover this course's most critical machine learning principles and beyond.
In the case of machine learning, studying theories is essential to comprehending the concepts properly. We've given equal weight to the theory portion, which many other courses do not.
We've employed graphic tools, and the most effective animations that could be used to convey the concepts we believe are essential factors to allow you to take pleasure in the course.
In addition, I've got an entire section covering the various issues you'll encounter when solving machine learning issues. This is something other courses tend to overlook.
I've set the price of the course to the lowest price possible to ensure that everyone can afford to attend the course.
Here are just a handful of topics we'll be studying:
Install Python and set up the virtual environment
Learn Python programming fundamentals with variables lists, tuples sets, dictionaries, and if statements, for loops and while loops, create custom functions, Python comprehensions, Python built-in Lambda functions, and work with external libraries to the Python programming language.
Make use of Python to perform Data Science and Machine Learning.
Understand the theoretical components of all machine learning models
Access the information, complete pre-processing tasks, then build and test the effectiveness of the machine-learning models. Implement Machine Learning Algorithms
Visualization, learning, and teaching techniques such as Matplotlib and Seaborn
Utilize SciKit-Learn for Machine Learning tasks
Lasso and Ridge Lasso and Ridge - Techniques for regularization
Random Forests and Decision Trees and an Extra Tree
Naive Bayes Classifier
Support Vector Machines
PCA"Principal Component Analysis
Techniques to Boost Performance - Adaboost Gradient boost, Catboost, and LightGBM
Natural Language Processing
How do you solve practical application problems in the context of Machine learning?
Content of the Course:
- Learn Python programming concepts: lists, variables sets, tuples, and Dictionaries.
- It is easy to handle Python programming concepts such as Loops, If statements, customized functions, integrated functions, comprehensions, lambda functions, and more.
- You can easily create, analyze and enhance the performance of well-known machine learning models by using Python.
- Choose the most appropriate machine learning algorithm that can effectively tackle your issue.
- Be familiar with the theoretical components of every machine learning model.
- A broad understanding of all machine learning concept and their practical application using Python Programming language.
- You should be comfortable with exploratory data analysis.
- Learn to distinguish the different algorithms and can select the most effective.
- Model improvements and parameter tuning.
- You should be comfortable handling Outliers Missing Values, Outliers, Data imbalance, feature scaling, and selecting features.
- Know the idea behind techniques for boosting your performance and learn how to use them efficiently.
- Learn to master machine learning algorithms on your own.
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