Machine Learning for Beginners to Advanced

The MOST in-depth look at Machine Learning theory, and how to code one with pure Python Machine Learning using libraries and from scratch.

What you'll learn

  • Learn all Machine Learning libraries and modules.

  • Know about Machine Learning IDEs like Jupiter notebook, Google collab, etc.

  • Real-life problem using the, Supervised Learning, Unsupervised Learning (intro) and Reinforcement learning

  • Introduction to neural networks

  • Learn about basics of statistics and concepts of Machine Learning

  • In-depth knowledge of data collection and data preprocessing for Machine Learning Linear Regression problem

  • Know-how fit data into the model using algorithms 

  • Know how to do, KNN, Decision Tree, Random Forests, Support Vector Machine, Textual analysis, PCA, etc.

₹ 12, 000

  • This course includes

  • 10.5 hours on-demand video

  • Full lifetime access

  • Access on mobile and TV

  • Certificate of Completion

Course Content

ML  – An Introduction

  • Machine Learning: An Overview

  • History of Machine Learning

  • What are  Numpy, pandas?

  • Overview of sklearn, matplotlib.

  • Overview of different ML models

  • Data collection

Data Preprocessing

  • Data conversions, bivariant analysis /variable conversions

  • Non – usable variables, outliers

  • EDA, Missing value treatment

  • Dummy value creation, handling

  • Correlation Analysis, Test train split, Bias Variance trade-off

Feature Engineering

  • feature extraction

  • catagorical data encoding

  • onehotencoding

Artificial Neural Networks(ANN)

  • Convolutional Neural Networks (intro)

  • Recurrent Neural Networks(intro)

  • The neuron, Tensors, Perceptron, Back propagation, activation functions, Gradient descent

Natural Language Processing

  • Preprocessing

  •  Embeddings

  •  Tokenization

  •  Vectorization

  •  Stemming

  •  Lemmatization

  •  Named entity recognition

  •  Chatbots (intro

Machine Translation

  • Recurrent Neural Networks

  • Bidirectional RNNs

  • Sequence to sequence

  • Google Translate

ML Models

  • Supervised Learning

  • Unsupervised Learning (intro)

  • Reinforcement Learning

Data Visualization

  • Data visualization using pie-chart 

  • Know about the bar plot.

  • Scatter plot, box plot, heat plot and more others

Bonus Projects

  • Recommendation system

  • Image classification

  • Text classification

  • Sentiment analyzer

  • Facial image classification

Requirements

  • Basic math, statistics.

  • Install Jupyter notebook or other IDEs.

  • Install-Module and Python

  • Don't worry about installing Jupyter notebook, we will do that in the lectures.

  • Being familiar with the content of my Machine learning algorithms(Linear Regression, Logistic Regression, Random forest, KNN, K-mean, Naive Bayes, and more others)

Description

This course will get you started in building your Machine learning Expert for beginners to learn Machine learning algorithms. Following my previous course on Python Machine Learning, we take this basic building block and build full-on machine learning basics right out of the gate using Python and python modules. All the materials for this course are FREE.

Before starting, first, we provides basic lecturers on python basics: Environment setup of python, Setting up machine learning environment (python), Jupiter notebook, anaconda, other local environments, Arithmetic operators in Python: Python, Strings in Python: Python Basics, Lists, Tuples, and Dictionaries: Python Basics, working with Numpy, working with pandas, working with matplotlib, overview of sklearn, overview of different ML models and Data collection.

You should take this course if you are interested in starting your journey toward becoming a master at machine learning, or if you are interested in machine learning and data science in general. We go beyond basic models like logistic regression and linear regression and I show you something that automatically learns features.

This course provides you with many practical examples so that you can really see how machine learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.

Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone's emotions just based on a picture!

After getting your feet wet with the fundamentals, I provide a brief overview of some of the newest developments in neural networks - slightly modified architectures and what they are used for.

 

NOTE:

If you already know about softmax and backpropagation, and you want to skip over the theory and speed things up using more advanced techniques along with TPU and GPU-optimization, check out my follow-up course on this topic, Machine Learning: Practical Machine Learning basics.

I have other courses that cover more advanced topics, such as Convolutional Neural Networks, Restricted Boltzmann Machines, Autoencoders, and more! But you want to be very comfortable with the material in this course before moving on to more advanced subjects.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

Suggested Prerequisites:

  • calculus (taking derivatives)

  • matrix arithmetic

  • probability

  • statistics
  • Python coding: if/else, loops, lists, dicts, sets

  • Numpy coding: matrix and vector operations, loading a CSV file

  • Be familiar with basic linear models such as linear regression and logistic regression

 

TIPS (for getting through the course):

  • Watch it at 2x.

  • Take handwritten notes. This will drastically increase your ability to retain the information.

  • Write down the equations. If you don't, I guarantee it will just look like gibberish.

  • Ask lots of questions on the discussion board. The more the better!

  • Realize that most exercises will take you days or weeks to complete.

  • Write code yourself, don't just sit there and look at my code.

 

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

  • Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)

 

Who this course is for:

  • Students interested in machine learning - you'll get all the tidbits you need to do well in this course

  • Professionals who want to use algorithms in their machine learning and data science pipeline. Be able to apply more powerful models, and know its drawbacks.

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