Python for Machine Learning: The Complete Beginner's Course
- Description
- Curriculum
- FAQ
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To understand how organizations like Google, Amazon, and even Udemy use machine learning and artificial intelligence (AI) to extract meaning and insights from enormous data sets, this machine learning course will provide you with the essentials. According to Glassdoor and Indeed, data scientists earn an average income of $120,000, and that is just the norm!
When it comes to being attractive, data scientists are already there. In a highly competitive job market, it is tough to keep them after they have been hired. People with a unique mix of scientific training, computer expertise, and analytical abilities are hard to find.
Like the Wall Street “quants” of the 1980s and 1990s, modern-day data scientists are expected to have a similar skill set. People with a background in physics and mathematics flocked to investment banks and hedge funds in those days because they could come up with novel algorithms and data methods.
That being said, data science is becoming one of the most well-suited occupations for success in the twenty-first century. It is computerized, programming-driven, and analytical in nature. Consequently, it comes as no surprise that the need for data scientists has been increasing in the employment market over the last several years.
The supply, on the other hand, has been quite restricted. It is challenging to get the knowledge and abilities required to be recruited as a data scientist.
In this course, mathematical notations and jargon are minimized, each topic is explained in simple English, making it easier to understand. Once you’ve gotten your hands on the code, you’ll be able to play with it and build on it. The emphasis of this course is on understanding and using these algorithms in the real world, not in a theoretical or academic context.
You’ll walk away from each video with a fresh idea that you can put to use right away!
All skill levels are welcome in this course, and even if you have no prior statistical experience, you will be able to succeed!
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1What is Machine Learning?Video lesson
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2Applications of Machine LearningVideo lesson
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3Machine learning MethodsVideo lesson
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4What is Supervised learning?Video lesson
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5What is Unsupervised learning?Video lesson
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6Supervised learning vs Unsupervised learningVideo lesson
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7Course MaterialsText lesson
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8IntroductionVideo lesson
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9Python libraries for Machine LearningVideo lesson
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10Setting up PythonVideo lesson
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11What is Jupyter?Video lesson
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12Anaconda Installation Windows Mac and UbuntuVideo lesson
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13Implementing Python in JupyterVideo lesson
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14Managing Directories in Jupyter NotebookVideo lesson
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15Introduction to regressionVideo lesson
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16How Does Linear Regression Work?Video lesson
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17Line representationVideo lesson
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18Implementation in python: Importing libraries & datasetsVideo lesson
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19Implementation in python: Distribution of the dataVideo lesson
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20Implementation in python: Creating a linear regression objectVideo lesson
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21Understanding Multiple linear regressionVideo lesson
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22Implementation in python: Exploring the datasetVideo lesson
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23Implementation in python: Encoding Categorical DataVideo lesson
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24Implementation in python: Splitting data into Train and Test SetsVideo lesson
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25Implementation in python: Training the model on the Training setVideo lesson
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26Implementation in python: Predicting the Test Set resultsVideo lesson
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27Evaluating the performance of the regression modelVideo lesson
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28Root Mean Squared Error in PythonVideo lesson
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29Introduction to classificationVideo lesson
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30K-Nearest Neighbors algorithmVideo lesson
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31Example of KNNVideo lesson
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32K-Nearest Neighbours (KNN) using pythonVideo lesson
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33Implementation in python: Importing required librariesVideo lesson
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34Implementation in python: Importing the datasetVideo lesson
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35Implementation in python: Splitting data into Train and Test SetsVideo lesson
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36Implementation in python: Feature ScalingVideo lesson
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37Implementation in python: Importing the KNN classifierVideo lesson
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38Implementation in python: Results prediction & Confusion matrixVideo lesson
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39Introduction to decision treesVideo lesson
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40What is Entropy?Video lesson
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41Exploring the datasetVideo lesson
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42Decision tree structureVideo lesson
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43Implementation in python: Importing libraries & datasetsVideo lesson
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44Implementation in python: Encoding Categorical DataVideo lesson
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45Implementation in python: Splitting data into Train and Test SetsVideo lesson
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46Implementation in python: Results prediction & AccuracyVideo lesson
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47IntroductionVideo lesson
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48Implementation stepsVideo lesson
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49Implementation in python: Importing libraries & datasetsVideo lesson
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50Implementation in python: Splitting data into Train and Test SetsVideo lesson
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51Implementation in python: Pre-processingVideo lesson
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52Implementation in python: Training the modelVideo lesson
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53Implementation in python: Results prediction & Confusion matrixVideo lesson
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54Logistic Regression vs Linear RegressionVideo lesson
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55Introduction to clusteringVideo lesson
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56Use casesVideo lesson
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57K-Means Clustering AlgorithmVideo lesson
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58Elbow methodVideo lesson
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59Steps of the Elbow methodVideo lesson
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60Implementation in pythonVideo lesson
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61Hierarchical clusteringVideo lesson
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62Density-based clusteringVideo lesson
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63Implementation of k-means clustering in pythonVideo lesson
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64Importing the datasetVideo lesson
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65Visualizing the datasetVideo lesson
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66Defining the classifierVideo lesson
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673D Visualization of the clustersVideo lesson
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683D Visualization of the predicted valuesVideo lesson
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69Number of predicted clustersVideo lesson
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70IntroductionVideo lesson
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71Collaborative Filtering in Recommender SystemsVideo lesson
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72Content-based Recommender SystemVideo lesson
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73Implementation in python: Importing libraries & datasetsVideo lesson
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74Merging datasets into one dataframeVideo lesson
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75Sorting by title and ratingVideo lesson
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76Histogram showing number of ratingsVideo lesson
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77Frequency distributionVideo lesson
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78Jointplot of the ratings and number of ratingsVideo lesson
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79Data pre-processingVideo lesson
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80Sorting the most-rated moviesVideo lesson
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81Grabbing the ratings for two moviesVideo lesson
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82Correlation between the most-rated moviesVideo lesson
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83Sorting the data by correlationVideo lesson
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84Filtering out moviesVideo lesson
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85Sorting valuesVideo lesson
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86Repeating the process for another movieVideo lesson
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87Quiz TimeQuiz

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