Python for Data Science & Machine Learning: Zero to Hero
- Description
- Curriculum
- FAQ
- Reviews
This machine learning course will provide you the fundamentals of how companies like Google, Amazon, and even Udemy utilize machine learning and artificial intelligence (AI) to glean meaning and insights from massive data sets. Glassdoor and Indeed both report that the average salary for a data scientist is $120,000. This is the standard, not the exception.
Data scientists are already quite desirable. It’s difficult to keep them on staff in today’s tight labor market. There is a severe shortage of people who possess the rare combination of scientific training, computer expertise, and analytical talents.
Today’s data scientists are held to the same standards as the Wall Street “quants” of the ’80s and ’90s. When the need arose for innovative algorithms and data approaches, physicists and mathematicians flocked to investment banks and hedge funds.
So, it’s no surprise that data science is rising to prominence as a promising career path in the modern day. It is analytic in focus, driven by code, and performed on a computer. As a result, it shouldn’t be a shock that the demand for data scientists has been growing steadily in the workplace for the past few years.
On the other hand, availability has been low. Obtaining the education and experience necessary to be hired as a data scientist is tough. And that’s why we made this course in the first place!
Each topic is described in plain English, and the course does its best to avoid mathematical notations and jargon. Once you have access to the source code, you can experiment with it and improve upon it. Learning and applying these algorithms in the real world, rather than in a theoretical or academic setting, is the focus of this course.
Each video will leave you with a new perspective that you can implement right away!
If you have no background in statistics, don’t let that stop you from enrolling in this course; we welcome students of all levels.
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1Welcome to the Python for Data Science & ML bootcamp!Video lesson
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2Python: A Brief OverviewVideo lesson
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3The Python Installation ProcedureVideo lesson
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4What Jupyter is?Video lesson
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5Set up Anaconda on Different Operating SystemsVideo lesson
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6How to integrate Python into Jupyter?Video lesson
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7Handling Directories in Jupyter NotebookVideo lesson
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8Input & OutputVideo lesson
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9Working with different datatypesVideo lesson
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10VariablesVideo lesson
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11Arithmetic OperatorsVideo lesson
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12Comparison OperatorsVideo lesson
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13Logical OperatorsVideo lesson
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14Conditional statementsVideo lesson
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15LoopsVideo lesson
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16Sequences Part 1: ListsVideo lesson
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17Sequences Part 2: DictionariesVideo lesson
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18Sequences Part 3: TuplesVideo lesson
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19Functions Part 1: Built-in FunctionsVideo lesson
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20Functions Part 2: User-defined FunctionsVideo lesson
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21Completing Library SetupVideo lesson
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22Library ImportingVideo lesson
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23Pandas: A Data Science LibraryVideo lesson
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24NumPy: A Data Science LibraryVideo lesson
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25NumPy vs. PandasVideo lesson
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26Matplotlib Library for Data ScienceVideo lesson
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27Seaborn Library for Data ScienceVideo lesson
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28Intro to NumPy arraysVideo lesson
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29Creating NumPy arraysVideo lesson
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30Indexing NumPy arraysVideo lesson
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31Array shapeVideo lesson
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32Iterating Over NumPy ArraysVideo lesson
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33Basic NumPy arrays: zeros()Video lesson
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34Basic NumPy arrays: ones()Video lesson
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35Basic NumPy arrays: full()Video lesson
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36Adding a scalarVideo lesson
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37Subtracting a scalarVideo lesson
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38Multiplying by a scalarVideo lesson
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39Dividing by a scalarVideo lesson
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40Raise to a powerVideo lesson
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41TransposeVideo lesson
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42Element-wise additionVideo lesson
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43Element-wise subtractionVideo lesson
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44Element-wise multiplicationVideo lesson
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45Element-wise divisionVideo lesson
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46Matrix multiplicationVideo lesson
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47StatisticsVideo lesson
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48What is a Python Pandas DataFrame?Video lesson
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49What is a Python Pandas Series?Video lesson
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50DataFrame vs SeriesVideo lesson
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51Creating a DataFrame using listsVideo lesson
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52Creating a DataFrame using a dictionaryVideo lesson
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53Loading CSV data into pythonVideo lesson
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54Changing the Index ColumnVideo lesson
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55InplaceVideo lesson
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56Examining the DataFrame: Head & TailVideo lesson
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57Statistical summary of the DataFrameVideo lesson
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58Slicing rows using bracket operatorsVideo lesson
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59Indexing columns using bracket operatorsVideo lesson
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60Boolean listVideo lesson
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61Filtering RowsVideo lesson
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62Filtering rows using & and | operatorsVideo lesson
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63Filtering data using loc()Video lesson
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64Filtering data using iloc()Video lesson
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65Adding and deleting rows and columnsVideo lesson
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66Sorting ValuesVideo lesson
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67Exporting and saving pandas DataFramesVideo lesson
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68Concatenating DataFramesVideo lesson
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69groupby()Video lesson
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70Introduction to Data CleaningVideo lesson
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71Quality of DataVideo lesson
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72Examples of AnomaliesVideo lesson
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73Median-based Anomaly DetectionVideo lesson
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74Mean-based anomaly detectionVideo lesson
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75Z-score-based Anomaly DetectionVideo lesson
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76Interquartile Range for Anomaly DetectionVideo lesson
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77Dealing with missing valuesVideo lesson
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78Regular ExpressionsVideo lesson
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79Feature ScalingVideo lesson
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80IntroductionVideo lesson
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81What is Exploratory Data Analysis?Video lesson
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82Univariate AnalysisVideo lesson
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83Univariate Analysis: Continuous DataVideo lesson
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84Univariate Analysis: Categorical DataVideo lesson
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85Bivariate analysis: Continuous & ContinuousVideo lesson
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86Bivariate analysis: Categorical & CategoricalVideo lesson
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87Bivariate analysis: Continuous & CategoricalVideo lesson
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88Detecting OutliersVideo lesson
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89Categorical Variable TransformationVideo lesson
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