Machine Learning In Python - Complete Course & Projects

Learn Machine Learning Algorithms and their Python Implementations. Learn the core concepts in Machine Learning.

What you'll learn

Learn Data Science

Learn the theories behind the Machine Learning Algorithms

Learn applying the Machine Learning Algorithms in Python

Learn feature engineering

Learn Python fundamentals

Learn Data Analysis

Requirements

No requirements. Just willingness to learn is enough.

Description

Welcome to the Machine Learning in Python - Theory and Implementation course. This course aims to teach students the machine learning algorithms by simplfying how they work on theory and the application of the machine learning algorithms in Python. Course starts with the basics of Python and after that machine learning concepts like evaluation metrics or feature engineering topics are covered in the course. Lastly machine learning algorithms are covered. By taking this course you are going to have the knowledge of how machine learning algorithms work and you are going to be able to apply the machine learning algorithms in Python. We are going to be covering python fundamentals, pandas, feature engineering, machine learning evaluation metrics, train test split and machine learning algorithms in this course. Course outline isPython FundamentalsPandas LibraryFeature EngineeringEvaluation of Model PerformancesSupervised vs Unsupervised LearningMachine Learning AlgorithmsThe machine learning algorithms that are going to be covered in this course is going to be Linear Regression, Logistic Regression, K-Nearest Neighbors, Support Vector Machines, Decision Tree, Random Forests and K-Means Clustering. If you are interested in Machine Learning and want to learn the algorithms theories and implementations in Python you can enroll into the course. You can always ask questions from course Q&A section. Thanks for reading the course description, have a nice day.

Overview

Section 1: Helpful Statistics Concepts (Optional)

Lecture 1 General Concepts in Statistics

Lecture 2 Mean - Mode - Median

Lecture 3 Mean - Mode - Median Calculation Practice

Lecture 4 Standard Deviation - Variance

Lecture 5 Standard Deviation - Variance Calculation Practice

Lecture 6 Confidence Interval

Lecture 7 Confidence Interval Practice

Section 2: Pandas

Lecture 8 Pandas part 1

Lecture 9 Pandas part 2

Lecture 10 Pandas Coding 1

Lecture 11 Pandas Coding 2

Section 3: Numpy

Lecture 12 Numpy - Introduction to Arrays

Lecture 13 Array Indexing

Lecture 14 Array Slicing and Array Iterating

Section 4: Feature Engineering

Lecture 15 Feature Scaling

Lecture 16 Feature Scaling in Python

Lecture 17 Label Encoding

Lecture 18 One Hot Encoding

Lecture 19 Outlier Detection

Section 5: Evaluation of the Model Performances

Lecture 20 Train-Test Split

Lecture 21 MSE - RMSE

Lecture 22 Confusion Matrix - Accuracy Score

Section 6: Machine Learning - Supervised vs Unsupervised

Lecture 23 Supervised vs Unsupervised Machine Learning

Section 7: Data Set Analysis & Feature Engineering for Regression Tasks

Lecture 24 Data Set

Lecture 25 EDA

Lecture 26 Feature Engineering

Section 8: Data Set Analysis & Feature Engineering for Classification Tasks

Lecture 27 Data Set

Lecture 28 EDA

Lecture 29 Feature Engineering

Section 9: Supervised Learning

Lecture 30 Linear Regression

Lecture 31 Linear Regression 2

Lecture 32 Linear Regression 3

Lecture 33 Linear Regression Coding

Lecture 34 Logistic Regression

Lecture 35 Logistic Regression Coding

Lecture 36 K Nearest Neighbors

Lecture 37 K-Nearest Neighbors Coding (Elbow Method)

Lecture 38 K-Nearest Neighbors Coding

Lecture 39 Support Vector Machines

Lecture 40 Support Vector Classifier Coding

Lecture 41 Support Vector Regression Coding

Lecture 42 Decision Tree

Lecture 43 Decision Tree Coding

Lecture 44 Random Forest

Lecture 45 Random Forest Regression Coding

Lecture 46 Random Forest Classification Coding

Section 10: Unsupervised Learning

Lecture 47 K-means Clustering

Lecture 48 K-means Clustering Coding

Section 11: Lets apply what we learned - Machine Learning Project: Classification

Lecture 49 Data Set

Lecture 50 Data Analysis

Lecture 51 Data Analysis II & Feature Engineering

Lecture 52 Machine Learning

Section 12: Lets apply what we learned - Machine Learning Project: Regression

Lecture 53 Data Set

Lecture 54 Data Analysis

Lecture 55 Feature Engineering

Lecture 56 Machine Learning

Section 13: Bonus Section

Lecture 57 bonus lecture

People who wants to learn Machine Learning,People who wants to learn Python