Learn Data Science Practically using Python

Apply Data Science using Python, Statistical Techniques, EDA, Numpy, Pandas, Scikit Learn, Statsmodel Libraries

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Last Updated: 2024-04-02 08:04:56 English Course Type: Self Learning

Data Science Core Concepts in Detail
Data Science Use Cases, Life Cycle and Methodologies
Exploratory Data Analysis (EDA)
Statistical Techniques
Detailed coverage of Python for Data Science and Machine Learning
Regression Algorithm - Linear Regression
Classification Problems and Classification Algorithms
Unsupervised Learning using K-Means Clustering
Dimensionality Reduction Techniques (PCA)
Feature Engineering Techniques
Model Optimization using Hyperparameter Tuning
Model Optimization using Grid-Search Cross Validation
Introduction to Deep Neural Networks

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:

Introduction to Data Science

Use Cases and Methodologies

Role of Data in Data Science

Statistical Methods

Exploratory Data Analysis (EDA)

Understanding the process of Training or Learning

Understanding Validation and Testing

Python Language in Detail

Setting up your DS/ML Development Environment

Python internal Data Structures

Python Language Elements

Pandas Data Structure – Series and DataFrames

Exploratory Data Analysis (EDA)

Learning Linear Regression Model using the House Price Prediction case study

Learning Logistic Model using the Credit Card Fraud Detection case study

Evaluating your model performance

Fine Tuning your model

Hyperparameter Tuning for Optimising our Models

Cross-Validation Technique

Learning SVM through an Image Classification project

Understanding Decision Trees

Understanding Ensemble Techniques using Random Forest

Dimensionality Reduction using PCA

K-Means Clustering with Customer Segmentation

Introduction to Deep Learning

Bonus Module: Time Series Prediction using ARIMA

Some exposure to Programming Languages will be useful

Aspiring Data Science Professionals
Aspiring Machine Learning Engineers

Section 1 : Introduction to Data Science 5 Lectures 00:46:03
Lecture 1 :
Data Science Introduction & Use Cases Preview
Lecture 2 :
Data Science Roles & Lifecycle
Lecture 3 :
Data Science Stages & Technologies
Lecture 4 :
Data Science Technologies and Analytics
Lecture 5 :
ML-Data and CRISP-DM
Section 2 : Statistical Techniques 8 Lectures 02:08:16
Lecture 1 :
Statistics and Experiments
Lecture 2 :
Types of Data and Descriptive Statistics
Lecture 3 :
Random Variables and Normal Distribution
Lecture 4 :
Histograms and Normal Approximation
Lecture 5 :
Central Limit Theorem
Lecture 6 :
Probability Theory
Lecture 7 :
Binomial Theory - Expected Value and Standard Error
Lecture 8 :
Hypothesis Testing
Section 3 : Python for Data Science 28 Lectures 05:47:53
Lecture 1 :
Introduction to Python
Lecture 2 :
Starting with Python with Jupyter Notebook
Lecture 3 :
Python Variables and Conditions
Lecture 4 :
Python Iterations 1
Lecture 5 :
Python Iterations 2
Lecture 6 :
Python Lists
Lecture 7 :
Python Tuples
Lecture 8 :
Python Dictionaries 1
Lecture 9 :
Python Dictionaries 2
Lecture 10 :
Python Sets 1
Lecture 11 :
Python Sets 2
Lecture 12 :
Numpy Arrays 1
Lecture 13 :
Numpy Arrays 2
Lecture 14 :
Numpy Arrays 3
Lecture 15 :
Pandas Series 1
Lecture 16 :
Pandas Series 2
Lecture 17 :
Pandas Series 3
Lecture 18 :
Pandas Series 4
Lecture 19 :
Pandas DataFrame 1
Lecture 20 :
Pandas DataFrame 2
Lecture 21 :
Pandas DataFrame 3
Lecture 22 :
Pandas DataFrame 4
Lecture 23 :
Pandas DataFrame 5
Lecture 24 :
Pandas DataFrame 6
Lecture 25 :
Python User Defined Functions
Lecture 26 :
Python Lambda Functions
Lecture 27 :
Python Lambda Functions and Date-Time Operations
Lecture 28 :
Python String Operations
Section 4 : Exploratory Data Analysis (EDA) 8 Lectures 01:53:06
Lecture 1 :
EDA Tools and Processes
Lecture 2 :
EDA Project - 1
Lecture 3 :
EDA Project - 2
Lecture 4 :
EDA Project - 3
Lecture 5 :
EDA Project - 4
Lecture 6 :
EDA Project - 5
Lecture 7 :
EDA Project - 6
Lecture 8 :
EDA Project - 7
Section 5 : Machine Learning 12 Lectures 01:47:05
Lecture 1 :
Introduction to Machine Learning
Lecture 2 :
Machine Learning Terminology
Lecture 3 :
History of Machine Learning
Lecture 4 :
Machine Learning Use Cases and Types
Lecture 5 :
Role of Data in Machine Learning
Lecture 6 :
Challenges in Machine Learning
Lecture 7 :
Machine Learning Life Cycle and Pipelines
Lecture 8 :
Regression Problems
Lecture 9 :
Regression Models and Perforance Metrics
Lecture 10 :
Classification Problems and Performance Metrics
Lecture 11 :
Optmizing Classificaton Metrics
Lecture 12 :
Bias and Variance
Section 6 : Linear Regression 13 Lectures 02:21:18
Lecture 1 :
Linear Regression Introduction
Lecture 2 :
Linear Regression - Training and Cost Function
Lecture 3 :
Linear Regression - Cost Functions and Gradient Descent
Lecture 4 :
Linear Regression - Practical Approach
Lecture 5 :
Linear Regression - Feature Scaling and Cost Functions
Lecture 6 :
Linear Regression OLS Assumptions and Testing
Lecture 7 :
Linear Regression Car Price Prediction
Lecture 8 :
Linear Regression Data Preparation and Analysis 1
Lecture 9 :
Linear Regression Data Preparation and Analysis 2
Lecture 10 :
Linear Regression Data Preparation and Analysis 3
Lecture 11 :
Linear Regression Model Building
Lecture 12 :
Linear Regression Model Evaluation and Optmization
Lecture 13 :
Linear Regression Model Optimization
Section 7 : Logistic Regression 8 Lectures 01:59:28
Lecture 1 :
Logistic Regression Introduction
Lecture 2 :
Logistic Regression - Logit Model
Lecture 3 :
Logistic Regression - Telecom Churn Case Study
Lecture 4 :
Logistic Regression - Data Analysis and Feature Engineering
Lecture 5 :
Logistic Regression - Build the Logistic Model
Lecture 6 :
Logistic Regression - Model Evaluation - AUC-ROC
Lecture 7 :
Logistic Regression - Model Optimization
Lecture 8 :
Logistic Regression - Model Optimization
Section 8 : Unsupervised Learning - K-Mean Clustering 5 Lectures 01:23:53
Lecture 1 :
Unsupervised Learning - K-Mean Clustering
Lecture 2 :
K-Means Clustering Computation
Lecture 3 :
K-Means Clustering Optimization
Lecture 4 :
K-Means - Data Preparation and Modelling
Lecture 5 :
K-Means - Model Optimization
Section 9 : Naive Bayes Probability Model 4 Lectures 01:05:38
Lecture 1 :
Naive Bayes Probability Model - Introduction
Lecture 2 :
Naive Bayes Probability Computation
Lecture 3 :
Naive Bayes - Employee Attrition Case Study
Lecture 4 :
Naive Bayes - Model Building and Optmization
Section 10 : Classfication using Decision Trees 6 Lectures 00:59:35
Lecture 1 :
Decision Tree - Model Concept
Lecture 2 :
Decision Tree - Learning Steps
Lecture 3 :
Decision Tree - Gini Index and Entropy Measures
Lecture 4 :
Decision Tree - Hyperparameter Tuning
Lecture 5 :
Decision Tree - Iris Dataset Case Study
Lecture 6 :
Decision Tree - Model Optimization using Grid Search Cross Validation
Section 11 : Ensemble Methods - Random Forest 4 Lectures 01:06:57
Lecture 1 :
Random Forest - Ensemble Techniques Bagging and Random Forest
Lecture 2 :
Random Forest Steps Pruning and Optimization
Lecture 3 :
Random Forest - Model Building and Hyperparameter Tuning using Grid Search CV
Lecture 4 :
Random Forest - Optimization Continued
Section 12 : Advanced Classification Techniques - Support Vector Machine 5 Lectures 00:27:58
Lecture 1 :
Support Vector Machine Concepts
Lecture 2 :
Support Vector Machine Metrics and Polynomial SVM
Lecture 3 :
Support Vector Machine Project 1
Lecture 4 :
Support Vector Machine Predictions
Lecture 5 :
Support Vector Machine - Classifying Polynomial Data
Section 13 : Dimensionality Reduction using PCA 4 Lectures 01:04:52
Lecture 1 :
Pricipal Component Analysis - Concepts
Lecture 2 :
Principal Component Analysis - Computations 1
Lecture 3 :
Principal Component Analysis - Computations 2
Lecture 4 :
Principal Component Analysis Practicals
Section 14 : Introduction to Deep Learning 1 Lectures 00:00:00
Lecture 1 :
Introduction to Deep Learning

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Manas Dasgupta,

1909 Course Views

3 Courses

I hold a Master's Degree (MSc) from Liverpool John Moores University (LJMU), UK on Artificial Intelligence and Machine Learning (AI/ML). My specialization and research areas are Natural Language Processing (NLP) using Deep Learning Methods such as Siamese Networks, Encoder-Decoder techniques, various Language Embedding methods such as BERT, areas such as Supervised Learning on Semantic Similarity and so on. My expertise area also encompass an array of Machine Learning and Data Science / Predictive Analytics areas including various Supervised, Unsupervised and Clustering methods. I have > 20 Years of experience in the IT Industry, mostly with the Financial Services domain. Starting as a Developer to being an Architect for a number of Years to Leadership position. The key focus and passion is to increase technical breadth and innovation.