Master Machine Learning And Data Science With Python

Learn Pandas, Scikit-Learn, Seaborn, Matplotlib, Machine Learning, NLP, Dealing with practical problems and more!

What you'll learn
Understand Python programming concepts: Variables, lists, tuples, sets and Dictionaries.
Comfortably deal with Python programming concepts: If statements, loops, custom functions, built-in functions, comprehensions, lambda functions and more..
Comfortably create, evaluate and improve the performance of famous machine learning models with the help of Python
Identify the most suitable machine learning algorithm to practically deal with the problem you are solving.
Be comfortable with the theoretical elements of each machine learning model.
Broad understanding of each machine learning concepts and their practice implementation with Python programming language.
Be comfortable with Exploratory data analysis.
Distinguish the different algorithms and capable of selecting the best.
Parameter tuning and model improvements.
Be comfortable dealing with Outliers, Missing Values, Feature Scaling, Imbalanced data and feature selection.
Understand the idea behind the boosting techniques and how to implement them effectively.
Be a pro who can deal with machine learning algorithms by your own.
Requirements
We have included a Python training kit for beginners, so, NO programming knowledge is required.
There is NO prerequisite knowledge of Machine Learning or Data Science. Everything will be taught to you from the ground up.
You should have a computer/tablet and time to learn.. That's all.
Description
Welcome to the best Machine Learning and Data Science with Python course in the planet. Are you ready to start your journey to becoming a Data Scientist?In this comprehensive course, you’ll begin your journey with installation and learning the basics of Python. Once you are ready, the introduction to Machine Learning section will give you an overview of what Machine Learning is all about, covering all the nitty gritty details before landing on your very first algorithm. You'll learn a variety of supervised and unsupervised machine learning algorithms, ranging from linear regression to the famous boosting algorithms. You’ll also learn text classification using Natural Language processing where you’ll deal with an interesting problem.Data science has been recognized as one of the best jobs in the world and it’s on fire right now. Not only it has a very good earning potential, but also it facilitates the freedom to work with top companies globally. Data scientists also gets the opportunity to deal with interesting problems, while being invaluable to the organization and enjoy the satisfaction of transforming the way how businesses make decisions. Machine learning and data science is one of the fastest growing and most in demand skills globally and the demand is growing rapidly. Parallel to that, Python is the easiest and most used programming language right now and that’s the first language choice when it comes to the machine learning. So, there is no better time to learn machine learning using python than today.I designed this course keeping the beginners and those who with some programming experience in mind. You may be coming from the Finance, Marketing, Engineering, Medical or even a fresher, as long as you have the passion to learn, this course will be your first step to become a Data Scientist.I have over 19 hours of best quality video contents. There are over 90 HD video lectures each ranging from 5 to 20 minutes on average. I’ve included Quizzes to test your knowledge after each topic to ensure you only leave the chapter after gaining the full knowledge. Not only that, I’ve given you many exercises to practice what you learn and solution to the exercise videos to compare the results. I’ve included all the exercise notebooks, solution notebooks, data files and any other information in the resource folder.Now, I'm gonna answer the most important question. Why should you choose this course over the other courses?I cover all the important machine learning concepts in this course and beyond.When it comes to machine learning, learning theory is the key to understanding the concepts well. We’ve given the equal importance to the theory section which most of the other courses don’t.We’ve used the graphical tools and the best possible animations to explain the concepts which we believe to be a key factor that would make you enjoy the course.Most importantly, I’ve a dedicated section covering all the practical issues you’d face when solving machine learning problems. This is something that other courses tend to ignore.I’ve set the course price to the lowest possible amount so that anyone can afford the course.Here a just a few of the topics we will be learning:Install Python and setup the virtual environmentLearn the basics of Python programming including variables, lists, tuples, sets, dictionaries, if statements, for loop, while loop, construct a custom function, Python comprehensions, Python built-in functions, Lambda functions and dealing with external libraries.Use Python for Data Science and Machine LearningLearn in-dept theoretical aspects of all the machine learning modelsOpen the data, perform pre-processing activities, build and evaluate the performance of the machine learning models Implement Machine Learning AlgorithmsLearn, Visualization techniques like Matplotlib and SeabornUse SciKit-Learn for Machine Learning TasksK-Means ClusteringDBSCAN ClusteringK-Nearest Neighbors Logistic RegressionLinear RegressionLasso and Ridge - Regularization techniquesRandom Forest and Decision Trees and Extra TreeNaïve Bayes ClassifierSupport Vector MachinesPCA - Principal Component AnalysisBoosting Techniques - Adaboost, Gradient boost, XGBoost, Catboost and LightGBMNatural Language ProcessingHow to deal with the practical problems when dealing with Machine learning

Overview

Section 1: Introduction

Lecture 1 Welcome Message and Important Instructions

Lecture 2 Download Resources

Lecture 3 Python Installation

Lecture 4 Access Notebook Files with Jupyter Notebook

Lecture 5 Jupyter Notebook Walkthrough Tutorial

Section 2: Python Basics - Starter Kit

Lecture 6 Getting started with Python

Lecture 7 Variables - Types

Lecture 8 Variables - Usage

Lecture 9 Variables - Strings

Lecture 10 Variables - Integers, Floats and Booleans

Lecture 11 Lists

Lecture 12 Tuples

Lecture 13 Dictionaries and Sets

Lecture 14 If Statements

Lecture 15 for loop

Lecture 16 while loop

Lecture 17 Custom Functions

Lecture 18 List Comprehensions

Lecture 19 Lambda Function

Lecture 20 Built-in Functions

Lecture 21 External Libraries

Lecture 22 Python Exercise Overview

Lecture 23 Python Exercise Solution - Part 1

Lecture 24 Python Exercise Solution - Part 2

Section 3: Introduction to Machine Learning

Lecture 25 Introduction to Machine Learning

Lecture 26 Machine Learning Life-Cycle

Lecture 27 Introduction to Performance Evaluation - Classification

Lecture 28 Confusion Matrix

Lecture 29 Main Classification Metrics

Lecture 30 Performance Evaluation - Regression

Lecture 31 Introduction to Sklearn

Lecture 32 One Hot encoding

Lecture 33 Split the Data

Lecture 34 What is Fit?

Section 4: Linear Regression

Lecture 35 Linear Regression Theory

Lecture 36 Linear Regression - Salary Prediction - Practical - Part 1

Lecture 37 Linear Regression - Salary Prediction - Practical - Part 2

Lecture 38 Linear Regression - House Price Prediction - Practical - Part 1

Lecture 39 Linear Regression - House Price Prediction - Practical - Part 2

Section 5: Logistic Regression

Lecture 40 Logistic Regression - Theory

Lecture 41 Logistic Regression - Iris Flower - Practical

Lecture 42 Logistic Regression - Gender Classification - Exercise Overview

Lecture 43 Logistic Regression - Exercise Solution - Gender Classification - Part 1

Lecture 44 Logistic Regression - Exercise Solution - Gender Classification - Part 2

Section 6: Lasso and Ridge Regression / Regularizations

Lecture 45 Lasso and Ridge Regression - Theory

Lecture 46 Lasso and Ridge Regression - Melbourne Housing - Practice - Part 1

Lecture 47 Lasso and Ridge Regression - Melbourne Housing - Practice - Part 2

Lecture 48 Lasso and Ridge Regression - Melbourne Housing - Practice - Part 3

Lecture 49 Lasso and Ridge - Insurance - Exercise overview

Lecture 50 Lasso and Ridge - Insurance - Solution to the Exercise

Section 7: Dealing with Practical Issues

Lecture 51 Bias Variance Trade-off

Lecture 52 Dealing with Imbalanced Data

Lecture 53 Dealing with Missing Values

Lecture 54 Dealing with Outliers - Theory

Lecture 55 Dealing with Outliers - Practical

Lecture 56 Feature Scaling of Data - Theory

Lecture 57 Feature Scaling - Practical

Section 8: Naïve Bayes Classifier (Gaussian)

Lecture 58 Gaussian Naïve Bayes Classifier - Theory

Lecture 59 Gaussian Naïve Bayes Classifier - Titanic - Practical - Part 1

Lecture 60 Gaussian Naïve Bayes Classifier - Titanic - Practical - Part 2

Section 9: Decision Trees

Lecture 61 Decision Tree - Theory

Lecture 62 Decision Tree - Penguin - Practical

Lecture 63 Decision Tree - Wine Quality - Exercise - Overview

Lecture 64 Decision Tree - Wine Quality - Exercise Solution

Section 10: Random Forest

Lecture 65 Random Forest - Theory

Lecture 66 Random Forest - Practical - Bike Sharing - Part 1

Lecture 67 Random Forest - Practical - Bike Sharing - Part 2

Lecture 68 Random Forest - WeatherAUS - Exercise Overview

Lecture 69 Random Forest - weatherAUS - Solution Part 1

Lecture 70 Random Forest - weatherAUS - Solution Part 2

Lecture 71 Extra Tree - Theory

Section 11: Boosting Techniques

Lecture 72 Introduction to Boosting Techniques

Lecture 73 Boosting Techniques Theory - Adaboost

Lecture 74 Boosting Techniques Theory - Gradient Boosting

Lecture 75 Boosting Techniques - Adult - Practical Implementation

Section 12: Support Vector Machines

Lecture 76 SVM Theory

Lecture 77 SVM - Practical - Heart Disease Classification

Lecture 78 SVM - Water Potability - Exercise Overview

Lecture 79 SVM - Water Potability - Exercise Solution

Section 13: K-Nearest Neighbors

Lecture 80 KNN Theory

Lecture 81 KNN - Practical - Classified Data

Section 14: Unsupervised Machine Learning Algorithms

Lecture 82 K-Means Clustering Theory

Lecture 83 K-Means Clustering - Practice - Iris

Lecture 84 DBSCAN Clustering - Theory

Lecture 85 DBSCAN Clustering - Practical

Section 15: PCA - Principal Component Analysis

Lecture 86 Principal Component Analysis - Theory

Lecture 87 PCA - Practical - Airline Passenger - Part 1

Lecture 88 PCA - Practical - Airline Passenger - Part 2

Section 16: Natural Language Processing

Lecture 89 NLP - Natural Language Processing - Introduction - Theory

Lecture 90 NLP - Naïve Bayes Multinomial Classification - Theory

Lecture 91 NLP - Practical - Amazon Reviewer Classification - Part 1

Lecture 92 NLP - Practical - Amazon Reviewer Classification - Part 2

Lecture 93 NLP - Practical - Amazon Reviewer Classification - Part 3

Anyone who is curious about data science.,Anyone who wants to properly understand and learn both theoretical and practice aspects of Machine learning.,Those who expect quizzes and practices to improve their skills while learning machine learning.,If you are someone who expects the real world challenges in the journey of machine learning.,You know machine learning but you prefer to improve both theoretical and practical aspect of it.