Machine Learning Math Simplified
The math behind machine learning, simplified: derivatives and the chain rule, eigenvalues and eigenvectors, matrix and eigen decomposition, SVD, PCA and tensor operations.
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9 videos4.8 h
Data analysis with Python from scratch: Jupyter, a Python refresher, NumPy, Pandas, Matplotlib and Seaborn.

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Learn Data Analysis From Scratch Using Python (Numpy, Pandas, Matplotlib, seaborn)
12:07Learn Data Analysis From Scratch Step By Step With Real Time Project - Course IntroductionOverview
4:2901 Course Pre RequisiteHands-on
6:1602 Ipython InterpreterHands-on
12:25Jupyter NotebookHands-on
13:3404 Python Refresher Basic DataTypesHands-on
15:1905 Python Refresher Collection Types ListsHands-on
6:2406 Python Refresher Collection Types DictionariesHands-on
6:3607 Python Refresher Collection Types SetsHands-on
7:3208 Python Refresher Collection Types TuplesHands-on
13:5809 Python Refresher Collection Types FunctionsHands-on
12:4410 Python Refresher Classes And ObjectsHands-on
3:2711 Part II Overveiw Numpy Core Concept For Data AnalysisHands-on
3:4012 What Is Numpy And Why To Use NumpyHands-on
14:5613 Array RevisitedHands-on
18:0614 Ways To Create Arrays In NumpyHands-on
12:4715 Numpy Array InternalHands-on
8:3016 DataTypes And CastingHands-on
11:4617 Slicing And Indexing Numpy ArraysHands-on
10:4018 Numpy Array OperationsHands-on
6:5119 BroadcastingHands-on
6:3020 VectorizationHands-on
2:28Pandas For Data AnalysisHands-on
2:57What is PandasHands-on
9:15Creating DataFrames in PandasHands-on
17:13DataFrames BasicsHands-on
14:29GroupByHands-on
14:01Handling Missing DataHands-on
5:46AggregationHands-on
8:33Window FunctionsHands-on
8:54TransformHands-on
3:59FilterHands-on
15:58Join , Merge And ConcatenateHands-on
3:55Apply MethodHands-on
6:10DataFrame ReshapeHands-on
2:55Calculate Frequency DistributionHands-on
2:40Data Analysis Core Concepts - IntroductionHands-on
2:57What is DataHands-on
5:22All About DataSetHands-on
12:30Types Of Variables DataTypesHands-on
4:39Descriptive Statistics Concepts - Types Of StatisticsHands-on
5:42Understand Normal DistributionHands-on
3:48Central Tendency Mean Median ModeHands-on
5:34Measure Of VariabilityHands-on
2:32Z Score - Standardized ScoreHands-on
10:06Modality Skewness KurtosisHands-on
5:24Matplotlib IntroductionHands-on
5:34Matplotlib ArchitectureHands-on
10:20Parameters Of PlotHands-on
10:10Seaborn Plot OverviewHands-on
6:05Types Of Plot By Purpose - IntroductionHands-on
18:38Plot Type - Correlation : Scatter PlotHands-on
10:24Plot Type - Correlation : Scatter Plot With Best Fit LineHands-on
4:35Plot Type - Correlation : Counts PlotHands-on
12:18Plot Type - Correlation Distribution : Marginal BoxplotHands-on
7:17Plot Type - Correlation : Correlogram HeatmapHands-on
5:36Plot Type - Correlation : Pairwise PlotHands-on
10:18Plot Type - Deviation Plot : Diverging BarsHands-on
8:06Plot Type - Deviation Plot : Diverging Dot PlotHands-on
13:42Plot Type - Ranking Plot : Ordered Bar PlotHands-on
8:48Plot Type - Ranking Plot : Dot PlotHands-on
10:07Plot Type - Distribution Plot : Histogram for Continuous VariableHands-on
3:34Plot Type - Distribution Plot : Histogram for Categorical VariableHands-on
5:51Plot Type - Distribution Plot : Density PlotHands-on
7:32Plot Type - Distribution Plot : Box PlotHands-on
8:40Plot Type - Composition Plot : Pie ChartHands-on
6:07Plot Type - Composition Plot : Tree MapHands-on
11:17Plot Type - Composition Plot : Bar ChartHands-on
9:02Plot Type - Change Plot : Time Series PlotHands-on
6:44Plot Type - Change Plot : Time Series Decomposition PlotHands-on
4:20Exploratory Data Analysis Workflow IntroductionHands-on
4:19Lets Understand The Big PictureHands-on
4:21What is Exploratory Data AnalysisHands-on
10:39EDA - Step 1 : Variable IdentificationHands-on
16:13EDA - Step 2 : Univariate AnalysisHands-on
7:43EDA - Step 3 - Concept : Bi-Variate AnalysisHands-on
14:13EDA - Step 4 : Missing values treatmentHands-on
5:04EDA - Step 5 : Outlier Detection and TreatmentHands-on
11:16EDA - Step 6 : Variable transformationHands-on
6:51EDA - Step 7 : Variable creationHands-on
1:41What Next ?Hands-onThe math behind machine learning, simplified: derivatives and the chain rule, eigenvalues and eigenvectors, matrix and eigen decomposition, SVD, PCA and tensor operations.
9 videos4.8 h
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