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 will 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.

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.


Subscribe to latest coupons on our Telegram channel.

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?

  1. I cover all the important machine learning concepts in this course and beyond.
  2. 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.
  3. 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.
  4. 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.
  5. 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 environment
  • Learn 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 Learning
  • Learn in-dept theoretical aspects of all the machine learning models
  • Open the data, perform pre-processing activities, build and evaluate the performance of the machine learning models Implement Machine Learning Algorithms
  • Learn, Visualization techniques like Matplotlib and Seaborn
  • Use SciKit-Learn for Machine Learning Tasks
  • K-Means Clustering
  • DBSCAN Clustering
  • K-Nearest Neighbors
  • Logistic Regression
  • Linear Regression
  • Lasso and Ridge – Regularization techniques
  • Random Forest and Decision Trees and Extra Tree
  • Naïve Bayes Classifier
  • Support Vector Machines
  • PCA – Principal Component Analysis
  • Boosting Techniques – Adaboost, Gradient boost, XGBoost, Catboost and LightGBM
  • Natural Language Processing
  • How to deal with the practical problems when dealing with Machine learning
English
language

Content

Introduction

Welcome Message and Important Instructions
Download Resources
Python Installation
Access Notebook Files with Jupyter Notebook
Jupyter Notebook Walkthrough Tutorial

Python Basics – Starter Kit

Getting started with Python
Variables – Types
Variables – Usage
Variables – Strings
Variables – Integers, Floats and Booleans
Lists
Tuples
Dictionaries and Sets
If Statements
for loop
while loop
Custom Functions
List Comprehensions
Lambda Function
Built-in Functions
External Libraries
Python Exercise Overview
Python Exercise Solution – Part 1
Python Exercise Solution – Part 2

Introduction to Machine Learning

Introduction to Machine Learning
Introduction to Machine Learning
Machine Learning Life-Cycle
Machine Learning Life-Cycle
Introduction to Performance Evaluation – Classification
Introduction to Performance Evaluation – Classification Metrics
Confusion Matrix
Confusion Matrix
Main Classification Metrics
Main Classification Metrics
Performance Evaluation – Regression
Performance Evaluation – Regression
Introduction to Sklearn
One Hot encoding
Split the Data
What is Fit?

Linear Regression

Linear Regression Theory
Linear Regression – Theory
Linear Regression – Salary Prediction – Practical – Part 1
Linear Regression – Salary Prediction – Practical – Part 2
Linear Regression – House Price Prediction – Practical – Part 1
Linear Regression – House Price Prediction – Practical – Part 2
Linear Regression – Practical

Logistic Regression

Logistic Regression – Theory
Logistic Regression – Theory
Logistic Regression – Iris Flower – Practical
Logistic Regression – Gender Classification – Exercise Overview
Logistic Regression – Exercise Solution – Gender Classification – Part 1
Logistic Regression – Exercise Solution – Gender Classification – Part 2

Lasso and Ridge Regression / Regularizations

Lasso and Ridge Regression – Theory
Lasso and Ridge Regression – Theory
Lasso and Ridge Regression – Melbourne Housing – Practice – Part 1
Lasso and Ridge Regression – Melbourne Housing – Practice – Part 2
Lasso and Ridge Regression – Melbourne Housing – Practice – Part 3
Lasso and Ridge – Insurance – Exercise overview
Lasso and Ridge – Insurance – Solution to the Exercise

Dealing with Practical Issues

Bias Variance Trade-off
Bias Variance Trade-off
Dealing with Imbalanced Data
Dealing with Imbalanced Data
Dealing with Missing Values
Dealing with Missing Values
Dealing with Outliers – Theory
Dealing with Outliers – Practical
Dealing with Outliers
Feature Scaling of Data – Theory
Feature Scaling – Practical
Feature Scaling of Data

Naïve Bayes Classifier (Gaussian)

Gaussian Naïve Bayes Classifier – Theory
Gaussian Naïve Bayes Classifier
Gaussian Naïve Bayes Classifier – Titanic – Practical – Part 1
Gaussian Naïve Bayes Classifier – Titanic – Practical – Part 2

Decision Trees

Decision Tree – Theory
Decision Tree – Penguin – Practical
Decision Tree – Wine Quality – Exercise – Overview
Decision Tree – Wine Quality – Exercise Solution

Random Forest

Random Forest – Theory
Random Forest – Theory
Random Forest – Practical – Bike Sharing – Part 1
Random Forest – Practical – Bike Sharing – Part 2
Random Forest – WeatherAUS – Exercise Overview
Random Forest – weatherAUS – Solution Part 1
Random Forest – weatherAUS – Solution Part 2
Extra Tree – Theory

Boosting Techniques

Introduction to Boosting Techniques
Boosting Techniques Theory – Adaboost
Boosting Techniques Theory – Gradient Boosting
Boosting Techniques – Adult – Practical Implementation
Boosting Techniques

Support Vector Machines

SVM Theory
SVM – Practical – Heart Disease Classification
SVM – Water Potability – Exercise Overview
SVM – Water Potability – Exercise Solution

K-Nearest Neighbors

KNN Theory
KNN – Practical – Classified Data
K-Nearest Neighbor

Unsupervised Machine Learning Algorithms

K-Means Clustering Theory
K-Means Clustering – Practice – Iris
K-Means Clustering
DBSCAN Clustering – Theory
DBSCAN Clustering – Practical
DBSCAN Clustering

PCA – Principal Component Analysis

Principal Component Analysis – Theory
PCA – Practical – Airline Passenger – Part 1
PCA – Practical – Airline Passenger – Part 2
PCA – Principal Component Analysis

Natural Language Processing

NLP – Natural Language Processing – Introduction – Theory
NLP – Naïve Bayes Multinomial Classification – Theory
NLP – Practical – Amazon Reviewer Classification – Part 1
NLP – Practical – Amazon Reviewer Classification – Part 2
NLP – Practical – Amazon Reviewer Classification – Part 3
NLP – Natural Language Processing

Enroll for Free

Share This Course on:
Ads Blocker Image Powered by Code Help Pro

Ads Blocker Detected!!!

We have detected that you are using extensions to block ads. Please support us by disabling these ads blocker.

Powered By
Best Wordpress Adblock Detecting Plugin | CHP Adblock