Learn Python For Data Science & Machine Learning From A-Z

Become a professional Data Scientist and learn how to use NumPy, Pandas, Machine Learning and more!

What you'll learn
Become a professional Data Scientist, Data Engineer, Data Analyst or Consultant
Learn data cleaning, processing, wrangling and manipulation
How to create resume and land your first job as a Data Scientist
How to use Python for Data Science
How to write complex Python programs for practical industry scenarios
Learn Plotting in Python (graphs, charts, plots, histograms etc)
Learn to use NumPy for Numerical Data
Machine Learning and it's various practical applications
Supervised vs Unsupervised Machine Learning
Learn Regression, Classification, Clustering and Sci-kit learn
Machine Learning Concepts and Algorithms
K-Means Clustering
Use Python to clean, analyze, and visualize data
Building Custom Data Solutions
Statistics for Data Science
Probability and Hypothesis Testing
Requirements
Students should have basic computer skills
Students would benefit from having prior Python Experience but not necessary
Description
Learn Python for Data Science & Machine Learning from A-ZIn this practical, hands-on course you’ll learn how to program using Python for Data Science and Machine Learning. This includes data analysis, visualization, and how to make use of that data in a practical manner. Our main objective is to give you the education not just to understand the ins and outs of the Python programming language for Data Science and Machine Learning, but also to learn exactly how to become a professional Data Scientist with Python and land your first job.We'll go over some of the best and most important Python libraries for data science such as NumPy, Pandas, and Matplotlib +NumPy —  A library that makes a variety of mathematical and statistical operations easier; it is also the basis for many features of the pandas library.Pandas — A Python library created specifically to facilitate working with data, this is the bread and butter of a lot of Python data science work.NumPy and Pandas are great for exploring and playing with data. Matplotlib is a data visualization library that makes graphs as you’d find in Excel or Google Sheets. Blending practical work with solid theoretical training, we take you from the basics of Python Programming for Data Science to mastery.This Machine Learning with Python course dives into the basics of machine learning using Python. You'll learn about supervised vs. unsupervised learning, look into how statistical modeling relates to machine learning, and do a comparison of each.We understand that theory is important to build a solid foundation, we understand that theory alone isn’t going to get the job done so that’s why this course is packed with practical hands-on examples that you can follow step by step. Even if you already have some coding experience, or want to learn about the advanced features of the Python programming language, this course is for you!Python coding experience is either required or recommended in job postings for data scientists, machine learning engineers, big data engineers, IT specialists, database developers, and much more. Adding Python coding language skills to your resume will help you in any one of these data specializations requiring mastery of statistical techniques.Together we’re going to give you the foundational education that you need to know not just on how to write code in Python, analyze and visualize data and utilize machine learning algorithms but also how to get paid for your newly developed programming skills.The course covers 5 main areas:1: PYTHON FOR DS+ML COURSE INTROThis intro section gives you a full introduction to the Python for Data Science and Machine Learning course, data science industry, and marketplace, job opportunities and salaries, and the various data science job roles.Intro to Data Science + Machine Learning with PythonData Science Industry and MarketplaceData Science Job OpportunitiesHow To Get a Data Science JobMachine Learning Concepts & Algorithms2: PYTHON DATA ANALYSIS/VISUALIZATIONThis section gives you a full introduction to the Data Analysis and Data Visualization with Python with hands-on step by step training.Python Crash CourseNumPy Data AnalysisPandas Data Analysis3: MATHEMATICS FOR DATA SCIENCEThis section gives you a full introduction to the mathematics for data science such as statistics and probability. Descriptive Statistics Measure of VariabilityInferential StatisticsProbabilityHypothesis Testing4:  MACHINE LEARNINGThis section gives you a full introduction to Machine Learning including Supervised & Unsupervised ML with hands-on step-by-step training.Intro to Machine LearningData PreprocessingLinear RegressionLogistic RegressionK-Nearest NeighborsDecision TreesEnsemble LearningSupport Vector MachinesK-Means ClusteringPCA5: STARTING A DATA SCIENCE CAREERThis section gives you a full introduction to starting a career as a Data Scientist with hands-on step by step training.Creating a ResumeCreating a Cover LetterPersonal BrandingFreelancing + Freelance websitesImportance of Having a WebsiteNetworkingBy the end of the course you’ll be a professional Data Scientist with Python and confidently apply for jobs and feel good knowing that you have the skills and knowledge to back it up.

Overview

Section 1: Introduction

Lecture 1 Who is This Course For?

Lecture 2 Data Science + Machine Learning Marketplace

Lecture 3 Data Science Job Opportunities

Lecture 4 Data Science Job Roles

Lecture 5 What is a Data Scientist?

Lecture 6 How To Get a Data Science Job

Lecture 7 Data Science Projects Overview

Section 2: Data Science & Machine Learning Concepts

Lecture 8 Why We Use Python?

Lecture 9 What is Data Science?

Lecture 10 What is Machine Learning?

Lecture 11 Machine Learning Concepts & Algorithms

Lecture 12 What is Deep Learning?

Lecture 13 Machine Learning vs Deep Learning

Section 3: Python For Data Science

Lecture 14 What is Programming?

Lecture 15 Why Python for Data Science?

Lecture 16 What is Jupyter?

Lecture 17 What is Google Colab?

Lecture 18 Python Variables, Booleans and None

Lecture 19 Getting Started with Google Colab

Lecture 20 Python Operators

Lecture 21 Python Numbers & Booleans

Lecture 22 Python Strings

Lecture 23 Python Conditional Statements

Lecture 24 Python For Loops and While Loops

Lecture 25 Python Lists

Lecture 26 More about Lists

Lecture 27 Python Tuples

Lecture 28 Python Dictionaries

Lecture 29 Python Sets

Lecture 30 Compound Data Types & When to use each one?

Lecture 31 Python Functions

Lecture 32 Object Oriented Programming in Python

Section 4: Statistics for Data Science

Lecture 33 Intro To Statistics

Lecture 34 Descriptive Statistics

Lecture 35 Measure of Variability

Lecture 36 Measure of Variability Continued

Lecture 37 Measures of Variable Relationship

Lecture 38 Inferential Statistics

Lecture 39 Measure of Asymmetry

Lecture 40 Sampling Distribution

Section 5: Probability & Hypothesis Testing

Lecture 41 What Exactly is Probability?

Lecture 42 Expected Values

Lecture 43 Relative Frequency

Lecture 44 Hypothesis Testing Overview

Section 6: NumPy Data Analysis

Lecture 45 Intro NumPy Array Data Types

Lecture 46 NumPy Arrays

Lecture 47 NumPy Arrays Basics

Lecture 48 NumPy Array Indexing

Lecture 49 NumPy Array Computations

Lecture 50 Broadcasting

Section 7: Pandas Data Analysis

Lecture 51 Introduction to Pandas

Lecture 52 Introduction to Pandas Continued

Section 8: Python Data Visualization

Lecture 53 Data Visualization Overview

Lecture 54 Different Data Visualization Libraries in Python

Lecture 55 Python Data Visualization Implementation

Section 9: Machine Learning

Lecture 56 Introduction To Machine Learning

Section 10: Data Loading & Exploration

Lecture 57 Exploratory Data Analysis

Section 11: Data Cleaning

Lecture 58 Feature Scaling

Lecture 59 Data Cleaning

Section 12: Feature Selecting and Engineering

Lecture 60 Feature Engineering

Section 13: Linear and Logistic Regression

Lecture 61 Linear Regression Intro

Lecture 62 Gradient Descent

Lecture 63 Linear Regression + Correlation Methods

Lecture 64 Linear Regression Implementation

Lecture 65 Logistic Regression

Section 14: K Nearest Neighbors

Lecture 66 KNN Overview

Lecture 67 parametric vs non-parametric models

Lecture 68 EDA on Iris Dataset

Lecture 69 The KNN Intuition

Lecture 70 Implement the KNN algorithm from scratch

Lecture 71 Compare the result with the sklearn library

Lecture 72 Hyperparameter tuning using the cross-validation

Lecture 73 The decision boundary visualization

Lecture 74 Manhattan vs Euclidean Distance

Lecture 75 Feature scaling in KNN

Lecture 76 Curse of dimensionality

Lecture 77 KNN use cases

Lecture 78 KNN pros and cons

Section 15: Decision Trees

Lecture 79 Decision Trees Section Overview

Lecture 80 EDA on Adult Dataset

Lecture 81 What is Entropy and Information Gain?

Lecture 82 The Decision Tree ID3 algorithm from scratch Part 1

Lecture 83 The Decision Tree ID3 algorithm from scratch Part 2

Lecture 84 The Decision Tree ID3 algorithm from scratch Part 3

Lecture 85 ID3 - Putting Everything Together

Lecture 86 Evaluating our ID3 implementation

Lecture 87 Compare with Sklearn implementation

Lecture 88 Visualizing the tree

Lecture 89 Plot the features importance

Lecture 90 Decision Trees Hyper-parameters

Lecture 91 Pruning

Lecture 92 [Optional] Gain Ration

Lecture 93 Decision Trees Pros and Cons

Lecture 94 [Project] Predict whether income exceeds $50K/yr - Overview

Section 16: Ensemble Learning and Random Forests

Lecture 95 Ensemble Learning Section Overview

Lecture 96 What is Ensemble Learning?

Lecture 97 What is Bootstrap Sampling?

Lecture 98 What is Bagging?

Lecture 99 Out-of-Bag Error (OOB Error)

Lecture 100 Implementing Random Forests from scratch Part 1

Lecture 101 Implementing Random Forests from scratch Part 2

Lecture 102 Compare with sklearn implementation

Lecture 103 Random Forests Hyper-Parameters

Lecture 104 Random Forests Pros and Cons

Lecture 105 What is Boosting?

Lecture 106 AdaBoost Part 1

Lecture 107 AdaBoost Part 2

Section 17: Support Vector Machines

Lecture 108 SVM Outline

Lecture 109 SVM intuition

Lecture 110 Hard vs Soft Margins

Lecture 111 C hyper-parameter

Lecture 112 Kernel Trick

Lecture 113 SVM - Kernel Types

Lecture 114 SVM with Linear Dataset (Iris)

Lecture 115 SVM with Non-linear Dataset

Lecture 116 SVM with Regression

Lecture 117 [Project] Voice Gender Recognition using SVM

Section 18: K-means

Lecture 118 Unsupervised Machine Learning Intro

Lecture 119 Unsupervised Machine Learning Continued

Lecture 120 Data Standardization

Section 19: PCA

Lecture 121 PCA Section Overview

Lecture 122 What is PCA?

Lecture 123 PCA Drawbacks

Lecture 124 PCA Algorithm Steps (Mathematics)

Lecture 125 Covariance Matrix vs SVD

Lecture 126 PCA - Main Applications

Lecture 127 PCA - Image Compression

Lecture 128 PCA Data Preprocessing

Lecture 129 PCA - Biplot and the Screen Plot

Lecture 130 PCA - Feature Scaling and Screen Plot

Lecture 131 PCA - Supervised vs Unsupervised

Lecture 132 PCA - Visualization

Section 20: Data Science Career

Lecture 133 Creating A Data Science Resume

Lecture 134 Data Science Cover Letter

Lecture 135 How to Contact Recruiters

Lecture 136 Getting Started with Freelancing

Lecture 137 Top Freelance Websites

Lecture 138 Personal Branding

Lecture 139 Networking Do's and Don'ts

Lecture 140 Importance of a Website

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