MACHINE LEARNING MASTER CLASS, AI MADE EASY (Zero to Hero!!)

In-depth approach to ML easing you into the basics of ML and making you a pro out of it in no time. Grab this course now

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Description

Welcome to the MACHINE LEARNING MASTER CLASS, AI MADE EASY (Zero to Hero!!)

In this course, we will take you on a journey from a beginner to a proficient practitioner in the exciting field of Machine Learning. Whether you are a beginner or have prior programming experience, this course is designed to equip you with the knowledge and skills needed to excel in machine learning and data science. Whether you're interested in data science, or statistics, or simply want to kick-start your Machine Learning journey, this course covers all the essential theory and practical techniques you need to succeed. With step-by-step tutorials and real-life examples, you'll not only gain knowledge but also get hands-on practice building your own models.

Here's a breakdown of what you'll learn in each section of the course:

Course Overview:

Section 1 - Python Basics and Advanced Concepts:

  • Learn the fundamentals of Python programming, including decorators and generators.

  • Explore essential libraries such as NumPy and Pandas for efficient data manipulation and analysis.

Section 2 - Machine Learning Concepts:

  • Understand the core concepts of Unsupervised and Supervised learning.

  • Dive into statistical measures like standard deviation, percentiles, and quantiles.

  • Master descriptive statistics such as mean, mode, and median.

Section 3 - Data Preprocessing:

  • Learn how to split data into test and train sets for model evaluation.

  • Handle missing data and explore techniques like under and oversampling.

Section 4 - Regression:

  • Build a strong foundation in regression analysis, including simple linear regression, multiple linear regression, SVR, decision tree regression, random forest regression, and polynomial regression.

Section 5 - Classification:

  • Gain expertise in classification algorithms, including logistic regression, K-nearest neighbors (K-NN), support vector machines (SVM), naive Bayes, decision tree classification, and random forest classification.

Section 6 - Clustering:

  • Master the art of clustering with K-means clustering and learn to determine the optimal number of clusters.

Section 7 - Reinforcement Learning:

  • Explore reinforcement learning algorithms, focusing on the Upper Confidence Bound (UCB) approach.

Section 8 - Natural Language Processing (NLP):

  • Gain an introduction to NLP and its applications in text classification using machine learning.

  • Build your own text classifier using the techniques learned.

Section 9 - Deep Learning:

  • Delve into the fascinating world of deep learning, including neural networks, backpropagation, data representation using numbers, and activation functions.

Section 10 - Model Selection & Boosting:

  • Discover techniques for model selection and optimization, such as k-fold cross-validation, parameter tuning, and grid search.

  • Learn about the powerful XGBoost algorithm for boosting performance.

Section 11 - Web Application using Flask and Model Deployment:

  • Get hands-on experience in building a basic web application using Flask.

  • Learn how to deploy your machine learning models in a web application.

You'll also cover essential topics like feature selection, visualization, evaluation techniques, and many more.

Moreover, the course is packed with practical exercises that are based on real-life examples to reinforce your learning and enable you to build your own models confidently. So not only you will learn the theory, but you will also get some hands-on practice building your models.

Are you aware of the current high demand for skills in Data Science and Machine Learning? These fields are undoubtedly challenging to master. Have you ever found yourself wishing for a comprehensive course that covers aspects of Data Science and Machine Learning, including Math for Machine Learning, Data Processing, Machine Learning A-Z, Deep Learning, and much more?

Well, you have come to the right place.

Why Choose This Course?

  • Comprehensive Coverage: Our course covers everything from Python basics to advanced machine learning techniques, ensuring you have a solid foundation in the subject.

  • Practical Approach: We provide hands-on practice and real-life examples to help you apply the concepts you learn.

  • Experienced Instructor: With eight years of teaching experience to over 140,000+ students and industry expertise, I will guide you through the course with clarity and simplicity.

  • Clear Doubt Resolution: If you find any course content confusing, our instructor is readily available to answer your questions and clarify doubts.

  • High-Quality Teaching: Our unique teaching style focuses on simplicity and step-by-step learning, making complex concepts easy to understand.

  • Valuable Skill Set: Machine learning is in high demand across various industries, and mastering it will enhance your career prospects as a data scientist, machine learning engineer, or computer vision specialist.

This course stands out due to its unique teaching style, breaking down complex topics into easy-to-understand explanations and following a step-by-step approach. If you ever find the content confusing or need clarification, our experienced instructor will be available to address your doubts promptly.

Topics 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 Matplotlib for Python plotting

  • Learn to utilize Seaborn for measurable plots

  • Learn Advance Mathematics for Machine Learning

  • Understand matrix multiplication, Matrix operations, and scalar operations

  • Use Pair plot 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

  • Model Deployment on Flask WebApplication

  • Natural Language Processing(NLP)

  • Deep Learning

  • And many more interesting topics.


Why is Machine Learning Important?

Machine learning has become crucial in today's data-driven world. With the availability of vast amounts of data, combined with advancements in computational power and affordable storage, machine learning algorithms play a vital role in extracting valuable insights and making data-driven decisions. Machine learning enables businesses to identify opportunities and risks quickly, gain a competitive edge, and drive innovation in various industries such as retail, healthcare, transportation, and more.

Learning Made Accessible:

This course provides a unique opportunity to learn machine learning from the comfort of your home. We understand that practical application is essential to master machine learning, so we offer hands-on exercises and real-world examples to enhance your skills. By completing this course, you will gain valuable experience and become a sought-after professional in the field of machine learning.


If you’re optimistic about reaping the benefits of having Machine Learning skills under your belt, then this course is for you!


No Question Asked – Money Back Guarantee!

The main barrier to 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, so 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.

Join Us Today:

Don't miss the chance to acquire powerful Machine Learning skills that are in high demand. Enroll now and embark on your journey to becoming a Machine Learning expert. Whether you are a beginner or an experienced programmer, this course will equip you with the knowledge and practical skills necessary to excel in the field of 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.

Learning enthusiasts will find this course appealing and would furnish their skill sets as well as provide weightage to their resumes.

Enroll now and unlock the power of machine learning from the comfort of your home!

                                          Join me on this adventure today! See you on the course.

What You Will 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 Matplotlib for Python Plotting
  • Learn to utilise Seaborn for measurable plots
  • Understand matrix multiplication, Matrix operations and scalar operations
  • Use Pair plot 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

Who Should Attend!

  • Curious individuals, who are interested in exploring the field of Machine Learning and AI.
  • Individuals with minimal math understanding who want to learn Machine Learning will benefit from this course, providing a solid foundation to build their knowledge.
  • Individuals with an interest in Machine Learning but lacking coding skills can access the course, as it presents the material in an accessible manner for those struggling with programming concepts.
  • College students aiming for a Data Science career can use this course as a stepping stone to gain the necessary skills and knowledge for success.
  • Data analysts looking to enhance their skill set with Machine Learning techniques will find valuable insights and practical techniques in this course for their data analysis tasks.
  • Software developers or programmers seeking a smooth transition into Machine Learning can leverage their existing programming skills and expand their knowledge in this exciting field.