Companies Home Search Profile

Learn Python for Data Science & Machine Learning from A-Z

Focused View

Juan E. Galvan,Ahmed Wael

22:54:16

6 View
  • 1 - Who is This Course For.mp4
    02:43
  • 2 - Data Science Machine Learning Marketplace.mp4
    06:55
  • 3 - Data Science Job Opportunities.mp4
    04:24
  • 4 - Data Science Job Roles.mp4
    10:23
  • 5 - What is a Data Scientist.mp4
    17:00
  • 6 - How To Get a Data Science Job.mp4
    18:39
  • 7 - Data Science Projects Overview.mp4
    11:52
  • 8 - Why We Use Python.mp4
    03:14
  • 9 - What is Data Science.mp4
    13:24
  • 10 - What is Machine Learning.mp4
    14:22
  • 11 - Machine Learning Concepts & Algorithms.mp4
    14:42
  • 12 - What is Deep Learning.mp4
    09:44
  • 13 - Machine Learning vs Deep Learning.mp4
    11:09
  • 14 - What is Programming.mp4
    06:03
  • 15 - ImportingPythonData.pdf
  • 15 - PythonBasics.pdf
  • 15 - Why Python for Data Science.mp4
    04:35
  • 16 - JupyterNotebook.pdf
  • 16 - What is Jupyter.mp4
    03:54
  • 17 - What is Google Colab.mp4
    03:27
  • 18 - Python Variables Booleans and None.mp4
    11:47
  • 19 - Getting Started with Google Colab.mp4
    09:07
  • 20 - Python Operators.mp4
    25:26
  • 21 - Python Numbers & Booleans.mp4
    07:47
  • 22 - Python Strings.mp4
    13:12
  • 23 - Python Conditional Statements.mp4
    13:53
  • 24 - Python For Loops and While Loops.mp4
    08:07
  • 25 - Python Lists.mp4
    05:10
  • 26 - More about Lists.mp4
    15:08
  • 27 - Is it possible to have autocomplete in a notebook in Google Colab.txt
  • 27 - Python Tuples.mp4
    11:25
  • 28 - Python Dictionaries.mp4
    20:19
  • 28 - Python Official Docs on Dictionaries.txt
  • 29 - Python Sets.mp4
    09:41
  • 30 - Compound Data Types & When to use each one.mp4
    12:58
  • 31 - Python Functions.mp4
    14:23
  • 32 - Object Oriented Programming in Python.mp4
    18:47
  • 33 - Intro To Statistics.mp4
    07:11
  • 34 - Descriptive Statistics.mp4
    06:35
  • 35 - Measure of Variability.mp4
    12:19
  • 36 - Measure of Variability Continued.mp4
    09:35
  • 37 - Measures of Variable Relationship.mp4
    07:37
  • 38 - Inferential Statistics.mp4
    15:18
  • 39 - Measure of Asymmetry.mp4
    01:57
  • 40 - Sampling Distribution.mp4
    07:34
  • 41 - What Exactly is Probability.mp4
    03:44
  • 42 - Expected Values.mp4
    02:38
  • 43 - Relative Frequency.mp4
    05:15
  • 44 - Hypothesis Testing Overview.mp4
    09:09
  • 45 - Intro NumPy Array Data Types.mp4
    12:58
  • 45 - NumPyBasics.pdf
  • 46 - NumPy Arrays.mp4
    08:21
  • 47 - NumPy Arrays Basics.mp4
    11:36
  • 48 - NumPy Array Indexing.mp4
    09:10
  • 49 - NumPy Array Computations.mp4
    05:53
  • 50 - Broadcasting.mp4
    04:32
  • 51 - Introduction to Pandas.mp4
    15:52
  • 51 - Pandas.pdf
  • 51 - PandasBasics.pdf
  • 52 - Introduction to Pandas Continued.mp4
    18:05
  • 53 - Data Visualization Overview.mp4
    24:49
  • 54 - Different Data Visualization Libraries in Python.mp4
    12:48
  • 55 - Python Data Visualization Implementation.mp4
    08:27
  • 56 - Introduction To Machine Learning.mp4
    26:03
  • 56 - SupervisedLearning.pdf
  • 57 - Exploratory Data Analysis.mp4
    13:06
  • 58 - Feature Scaling.mp4
    07:41
  • 59 - Data Cleaning.mp4
    07:43
  • 60 - Feature Engineering.mp4
    06:11
  • 61 - Linear Regression Intro.mp4
    08:17
  • 62 - Gradient Descent.mp4
    05:59
  • 63 - Linear Regression Correlation Methods.mp4
    26:33
  • 64 - Linear Regression Implementation.mp4
    05:06
  • 65 - Logistic Regression.mp4
    03:22
  • 66 - KNN Overview.mp4
    03:01
  • 66 - knnipynb.zip
  • 67 - parametric vs nonparametric models.mp4
    03:28
  • 68 - EDA on Iris Dataset.mp4
    22:08
  • 68 - Sklearn Toy Datasets.txt
  • 69 - The KNN Intuition.mp4
    02:16
  • 70 - Implement the KNN algorithm from scratch.mp4
    11:45
  • 71 - Compare the result with the sklearn library.mp4
    03:47
  • 72 - Hyperparameter tuning using the crossvalidation.mp4
    10:47
  • 73 - Stanford Demo KNN Decision Boundary.txt
  • 73 - The decision boundary visualization.mp4
    04:55
  • 74 - MIT example.txt
  • 74 - Manhattan vs Euclidean Distance.mp4
    11:21
  • 75 - Feature scaling in KNN.mp4
    06:01
  • 76 - Curse of dimensionality.mp4
    08:09
  • 77 - KNN use cases.mp4
    03:32
  • 78 - KNN pros and cons.mp4
    05:32
  • 79 - Decision Trees Section Overview.mp4
    04:11
  • 79 - decisiontreesipynb.zip
  • 80 - Adult Dataset.txt
  • 80 - EDA on Adult Dataset.mp4
    16:53
  • 80 - adultdata.csv
  • 80 - adultnames.csv
  • 80 - adulttest.csv
  • 81 - What is Entropy and Information Gain.mp4
    21:50
  • 82 - The Decision Tree ID3 algorithm from scratch Part 1.mp4
    11:33
  • 83 - The Decision Tree ID3 algorithm from scratch Part 2.mp4
    07:35
  • 84 - The Decision Tree ID3 algorithm from scratch Part 3.mp4
    04:07
  • 85 - ID3 Putting Everything Together.mp4
    21:23
  • 86 - Evaluating our ID3 implementation.mp4
    16:51
  • 87 - Categorical feature in Treebased classifiers.txt
  • 87 - Compare with Sklearn implementation.mp4
    08:52
  • 87 - Passing categorical data to Sklearn Decision Tree.txt
  • 88 - Visualizing the tree.mp4
    10:15
  • 89 - Plot the features importance.mp4
    05:51
  • 90 - Decision Trees Hyperparameters.mp4
    11:39
  • 91 - Pruning.mp4
    17:11
  • 92 - Optional Gain Ration.mp4
    02:49
  • 93 - Decision Trees Pros and Cons.mp4
    07:31
  • 94 - Project Predict whether income exceeds 50Kyr Overview.mp4
    02:33
  • 95 - Ensemble Learning Section Overview.mp4
    03:46
  • 95 - ensemblelearningipynb.zip
  • 96 - Ensemble Learning Example.txt
  • 96 - What is Ensemble Learning.mp4
    13:06
  • 97 - What is Bootstrap Sampling.mp4
    08:25
  • 98 - What is Bagging.mp4
    05:20
  • 99 - OutofBag Error OOB Error.mp4
    07:47
  • 100 - Implementing Random Forests from scratch Part 1.mp4
    22:34
  • 101 - Implementing Random Forests from scratch Part 2.mp4
    06:10
  • 102 - Compare with sklearn implementation.mp4
    03:41
  • 103 - Random Forests HyperParameters.mp4
    04:23
  • 104 - Random Forests Pros and Cons.mp4
    05:25
  • 105 - What is Boosting.mp4
    04:41
  • 106 - AdaBoost Part 1.mp4
    04:10
  • 107 - AdaBoost Part 2.mp4
    14:33
  • 108 - SVM Outline.mp4
    05:16
  • 108 - svmipynb.zip
  • 109 - SVM intuition.mp4
    11:38
  • 110 - Hard vs Soft Margins.mp4
    13:25
  • 111 - C hyperparameter.mp4
    04:17
  • 112 - Kernel Trick.mp4
    12:18
  • 113 - SVM Kernel Types.mp4
    18:13
  • 113 - SVM RBF Visualization.txt
  • 113 - SVM with polynomial kernel visualization.txt
  • 114 - SVM with Linear Dataset Iris.mp4
    13:35
  • 115 - SVM with Nonlinear Dataset.mp4
    12:50
  • 116 - SVM with Regression.mp4
    05:51
  • 117 - Kaggle Gender Recognition by Voice and Speech Analysis.txt
  • 117 - Project Voice Gender Recognition using SVM.mp4
    04:26
  • 118 - UnsupervisedLearning.pdf
  • 118 - Unsupervised Machine Learning Intro.mp4
    20:22
  • 119 - Unsupervised Machine Learning Continued.mp4
    20:48
  • 120 - Data Standardization.mp4
    19:05
  • 121 - PCA Section Overview.mp4
    05:12
  • 121 - pcaipynb.zip
  • 122 - What is PCA.mp4
    09:36
  • 123 - PCA Drawbacks.mp4
    03:31
  • 124 - Linear Algebra Refresher.txt
  • 124 - PCA Algorithm Steps Mathematics.mp4
    13:12
  • 124 - Principal components eigenvectors.txt
  • 125 - Covariance Matrix vs SVD.mp4
    04:58
  • 125 - SVD details.txt
  • 126 - PCA Main Applications.mp4
    02:50
  • 127 - Compression Ratio.txt
  • 127 - PCA Image Compression.mp4
    27:00
  • 127 - cat.zip
  • 128 - PCA Data Preprocessing.mp4
    14:31
  • 129 - PCA Biplot and the Screen Plot.mp4
    17:27
  • 129 - USArrests.csv
  • 130 - PCA Feature Scaling and Screen Plot.mp4
    09:29
  • 131 - PCA Supervised vs Unsupervised.mp4
    04:55
  • 132 - PCA Visualization.mp4
    07:31
  • 133 - Creating A Data Science Resume.mp4
    06:45
  • 134 - Data Science Cover Letter.mp4
    03:33
  • 135 - How to Contact Recruiters.mp4
    04:20
  • 136 - Getting Started with Freelancing.mp4
    04:13
  • 137 - Top Freelance Websites.mp4
    05:35
  • 138 - Personal Branding.mp4
    04:02
  • 139 - Networking Dos and Donts.mp4
    03:45
  • 140 - Importance of a Website.mp4
    02:56
  • Description


    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

    Who is this for?


  • Students who want to learn about Python for Data Science & Machine Learning
  • What You Need to Know?


  • Students should have basic computer skills
  • Students would benefit from having prior Python Experience but not necessary
  • More details


    Description

    Learn Python for Data Science & Machine Learning from A-Z

    In 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 INTRO

    This 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 Python

    • Data Science Industry and Marketplace

    • Data Science Job Opportunities

    • How To Get a Data Science Job

    • Machine Learning Concepts & Algorithms

    2: PYTHON DATA ANALYSIS/VISUALIZATION

    This section gives you a full introduction to the Data Analysis and Data Visualization with Python with hands-on step by step training.

    • Python Crash Course

    • NumPy Data Analysis

    • Pandas Data Analysis

    3: MATHEMATICS FOR DATA SCIENCE

    This section gives you a full introduction to the mathematics for data science such as statistics and probability.

    • Descriptive Statistics

    • Measure of Variability

    • Inferential Statistics

    • Probability

    • Hypothesis Testing

    4:  MACHINE LEARNING

    This section gives you a full introduction to Machine Learning including Supervised & Unsupervised ML with hands-on step-by-step training.

    • Intro to Machine Learning

    • Data Preprocessing

    • Linear Regression

    • Logistic Regression

    • K-Nearest Neighbors

    • Decision Trees

    • Ensemble Learning

    • Support Vector Machines

    • K-Means Clustering

    • PCA

    5: STARTING A DATA SCIENCE CAREER

    This section gives you a full introduction to starting a career as a Data Scientist with hands-on step by step training.

    • Creating a Resume

    • Creating a Cover Letter

    • Personal Branding

    • Freelancing + Freelance websites

    • Importance of Having a Website

    • Networking

    By 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.

    Who this course is for:

    • Students who want to learn about Python for Data Science & Machine Learning

    User Reviews
    Rating
    0
    0
    0
    0
    0
    average 0
    Total votes0
    Focused display
    Juan E. Galvan
    Juan E. Galvan
    Instructor's Courses
    Hi I'm Juan. I've been an Entrepreneur since grade school. My background is in the tech space from Digital Marketing, E-commerce, Web Development to Programming. I believe in continuous education with the best of a University Degree without all the downsides of burdensome costs and inefficient methods. I look forward to helping you expand your skillsets.
    Hello, I'm Ahmed Wael. I have been a developer for 5 years now in the field of Machine Learning, Deep Learning, AI, Computer Vision, and Data Visualization.This experience is both theoretical and practical.I have taught and mentored hundreds of students from many countries with different levels of knowledge.Based on the feedback I got from my students, I was inspired to create courses that anyone can reach! I really want to help students learn the most complex concepts very easily.I am looking forward to interacting and engaging with all of you. I will be available every day to answer any of your questions and even add more materials per your request.
    Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.
    • language english
    • Training sessions 140
    • duration 22:54:16
    • English subtitles has
    • Release Date 2024/05/21

    Courses related to Python

    Courses related to Machine Learning

    Courses related to Data Science

    Courses related to Pandas

    Courses related to NumPy