Explore data science using Python, statistical techniques, EDA, NumPy, Pandas, Scikit Learn, and Statsmodel libraries and take your first step toward becoming a data scientist or a machine learning engineer.
About This Video
In Detail
In this course, you will learn …
Explore data science using Python, statistical techniques, EDA, NumPy, Pandas, Scikit Learn, and Statsmodel libraries and take your first step toward becoming a data scientist or a machine learning engineer.
About This Video
In Detail
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 optimization using hyperparameter tuning and grid search cross validation techniques, and more.
You will learn how to perform detailed data analysis using Python, statistical techniques, and exploratory data analysis, using various predictive modeling 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 also covers classification using decision trees, which include the Gini index and entropy measures and hyperparameter tuning. It covers the use of NumPy and Pandas libraries extensively for teaching exploratory data analysis. In addition, you will also explore advanced classification techniques and support vector machine predictions. There is also an introductory lesson included on Deep Neural Networks with a worked-out example on image classification using TensorFlow and Keras.
By the end of the course, you will learn some basic foundations of data science using Python.
Audience
This course is for Python, machine learning developers, data scientists, data analysts, and business analysts. This course will also be beneficial for aspiring data science professionals and machine learning engineers.
Exposure to programming languages will be useful.
Chapter 1 : Introduction to Data Science
Data Science Introduction and Use Cases
Data Science Roles and Lifecycle
Data Science Stages and Technologies
Data Science Technologies and Analytics
ML-Data and CRISP-DM
Chapter 2 : Statistical Techniques
Statistics and Experiments
Types of Data and Descriptive Statistics
Random Variables and Normal Distribution
Histograms and Normal Approximation
Central Limit Theorem
Probability Theory
Binomial Theory - Expected Value and Standard Error
Hypothesis Testing
Chapter 3 : Python for Data Science
Introduction to Python
Starting with Python with Jupyter Notebook
Python Variables and Conditions
Python Iterations 1
Python Iterations 2
Python Lists
Python Tuples
Python Dictionaries 1
Python Dictionaries 2
Python Sets 1
Python Sets 2
NumPy Arrays 1
NumPy Arrays 2
NumPy Arrays 3
Pandas Series 1
Pandas Series 2
Pandas Series 3
Pandas Series 4
Pandas DataFrame 1
Pandas DataFrame 2
Pandas DataFrame 3
Pandas DataFrame 4
Pandas DataFrame 5
Pandas DataFrame 6
Python User-Defined Functions
Python Lambda Functions
Python Lambda Functions and Date-Time Operations
Python String Operations
Chapter 4 : Exploratory Data Analysis (EDA)
Introduction to EDA
EDA Tools and Processes
EDA Project - 1
EDA Project - 2
EDA Project - 3
EDA Project - 4
EDA Project - 5
EDA Project - 6
EDA Project - 7
Chapter 5 : Machine Learning
Introduction to Machine Learning
Machine Learning Terminology
History of Machine Learning
Machine Learning Use Cases and Types
Role of Data in Machine Learning
Challenges in Machine Learning
Machine Learning Lifecycle and Pipelines
Regression Problems
Regression Models and Performance Metrics
Classification Problems and Performance Metrics
Optimizing Classification Metrics
Bias and Variance
Chapter 6 : Linear Regression
Linear Regression Introduction
Linear Regression - Training and Cost Function
Linear Regression - Cost Functions and Gradient Descent
Linear Regression - Practical Approach
Linear Regression - Feature Scaling and Cost Functions
Linear Regression OLS Assumptions and Testing
Linear Regression Car Price Prediction
Linear Regression Data Preparation and Analysis 1
Linear Regression Data Preparation and Analysis 2
Linear Regression Data Preparation and Analysis 3
Linear Regression Model Building
Linear Regression Model Evaluation and Optimization
Linear Regression Model Optimization
Chapter 7 : Logistic Regression
Logistic Regression Introduction
Logistic Regression - Logit Model
Logistic Regression - Telecom Churn Case Study
Logistic Regression - Data Analysis and Feature Engineering
Logistic Regression - Build the Logistic Model
Logistic Regression - Model Evaluation - AUC-ROC
Logistic Regression - Model Optimization
Logistic Regression - Model Optimization 2
Chapter 8 : Unsupervised Learning - K-Means Clustering
Unsupervised Learning - K-Means Clustering
K-Means Clustering Computation
K-Means Clustering Optimization
K-Means - Data Preparation and Modelling
K-Means - Model Optimization
Chapter 9 : Naive Bayes Probability Model
Naive Bayes Probability Model - Introduction
Naive Bayes Probability Computation
Naive Bayes - Employee Attrition Case Study
Naive Bayes - Model Building and Optimization
Chapter 10 : Classification using decision trees
Decision Tree - Model Concept
Decision Tree - Learning Steps
Decision Tree - Gini Index and Entropy Measures
Decision Tree - Hyperparameter Tuning
Decision Tree - Iris Dataset Case Study
Decision Tree - Model Optimization using Grid Search Cross Validation
Chapter 11 : Ensemble Methods – Random Forest
Random Forest - Ensemble Techniques Bagging and Random Forest
Random Forest Steps Pruning and Optimization
Random Forest - Model Building and Hyperparameter Tuning using Grid Search CV
Random Forest - Optimization Continued
Chapter 12 : Advanced Classification Techniques – Support Vector Machine
Support Vector Machine Concepts
Support Vector Machine Metrics and Polynomial SVM
Support Vector Machine Project 1
Support Vector Machine Predictions
Support Vector Machine - Classifying Polynomial Data
Chapter 13 : Dimensionality Reduction Using PCA
Principal Component Analysis - Concepts
Principal Component Analysis - Computations 1
Principal Component Analysis - Computations 2
Principal Component Analysis Practical
Chapter 14 : Introduction to Deep Learning
Introduction to Deep Learning