Practical Data Science using Python
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- Curriculum
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Are you aspiring to become a Data Scientist or Machine Learning Engineer? if yes, then this course is for you.
In this course, you will learn about core concepts of Data Science, Exploratory Data Analysis, Statistical Methods, role of Data, Python Language, challenges of Bias, Variance and Overfitting, choosing the right Performance Metrics, Model Evaluation Techniques, Model Optmization using Hyperparameter Tuning and Grid Search Cross Validation techniques, etc.
You will learn how to perform detailed Data Analysis using Pythin, Statistical Techniques, Exploratory Data Analysis, using various Predictive Modelling Techniques such as a range of Classification Algorithms, Regression Models and Clustering Models. You will learn the scenarios and use cases of deploying Predictive models.
This course covers Python for Data Science and Machine Learning in great detail and is absolutely essential for the beginner in Python.
Most of this course is hands-on, through completely worked out projects and examples taking you through the Exploratory Data Analysis, Model development, Model Optimization and Model Evaluation techniques.
This course covers the use of Numpy and Pandas Libraries extensively for teaching Exploratory Data Analysis. In addition, it also covers Marplotlib and Seaborn Libraries for creating Visualizations.
There is also an introductory lesson included on Deep Neural Networks with a worked-out example on Image Classification using TensorFlow and Keras.
Course Sections:
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Introduction to Data Science
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Use Cases and Methodologies
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Role of Data in Data Science
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Statistical Methods
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Exploratory Data Analysis (EDA)
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Understanding the process of Training or Learning
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Understanding Validation and Testing
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Python Language in Detail
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Setting up your DS/ML Development Environment
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Python internal Data Structures
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Python Language Elements
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Pandas Data Structure – Series and DataFrames
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Exploratory Data Analysis (EDA)
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Learning Linear Regression Model using the House Price Prediction case study
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Learning Logistic Model using the Credit Card Fraud Detection case study
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Evaluating your model performance
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Fine Tuning your model
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Hyperparameter Tuning for Optimising our Models
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Cross-Validation Technique
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Learning SVM through an Image Classification project
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Understanding Decision Trees
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Understanding Ensemble Techniques using Random Forest
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Dimensionality Reduction using PCA
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K-Means Clustering with Customer Segmentation
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Introduction to Deep Learning
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Bonus Module: Time Series Prediction using ARIMA
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15Introduction to Python
This attachment contains the code and data for the entire course.
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16Starting with Python with Jupyter Notebook
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17Python Variables and Conditions
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18Python Iterations 1
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19Python Iterations 2
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20Python Lists
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21Python Tuples
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22Python Dictionaries 1
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23Python Dictionaries 2
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24Python Sets 1
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25Python Sets 2
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26Numpy Arrays 1
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27Numpy Arrays 2
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28Numpy Arrays 3
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29Pandas Series 1
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30Pandas Series 2
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31Pandas Series 3
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32Pandas Series 4
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33Pandas DataFrame 1
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34Pandas DataFrame 2
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35Pandas DataFrame 3
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36Pandas DataFrame 4
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37Pandas DataFrame 5
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38Pandas DataFrame 6
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39Python User Defined Functions
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40Python Lambda Functions
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41Python Lambda Functions and Date-Time Operations
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42Python String Operations
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52Introduction to Machine Learning
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53Machine Learning Terminology
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54History of Machine Learning
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55Machine Learning Use Cases and Types
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56Role of Data in Machine Learning
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57Challenges in Machine Learning
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58Machine Learning Life Cycle and Pipelines
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59Regression Problems
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60Regression Models and Perforance Metrics
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61Classification Problems and Performance Metrics
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62Optmizing Classificaton Metrics
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63Bias and Variance
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64Linear Regression Introduction
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65Linear Regression - Training and Cost Function
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66Linear Regression - Cost Functions and Gradient Descent
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67Linear Regression - Practical Approach
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68Linear Regression - Feature Scaling and Cost Functions
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69Linear Regression OLS Assumptions and Testing
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70Linear Regression Car Price Prediction
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71Linear Regression Data Preparation and Analysis 1
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72Linear Regression Data Preparation and Analysis 2
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73Linear Regression Data Preparation and Analysis 3
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74Linear Regression Model Building
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75Linear Regression Model Evaluation and Optmization
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76Linear Regression Model Optimization
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77Logistic Regression Introduction
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78Logistic Regression - Logit Model
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79Logistic Regression - Telecom Churn Case Study
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80Logistic Regression - Data Analysis and Feature Engineering
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81Logistic Regression - Build the Logistic Model
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82Logistic Regression - Model Evaluation - AUC-ROC
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83Logistic Regression - Model Optimization
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84Logistic Regression - Model Optimization