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What you'll learn?
- 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
Course Overview
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
Pre-requisites
- Some exposure to Programming Languages will be useful
Target Audience
- Aspiring Data Science Professionals
- Aspiring Machine Learning Engineers
Curriculum 111 Lectures 22:52:02
-
Section 1 : Introduction to Data Science
- 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
- 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
- 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)
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Lecture 1 :
- Introduction to Deep Learning
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Instructor
1834 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.
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