Learn Practical Machine Learning using Python

Concepts and Projects based learning for aspiring Machine Learning Professionals

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Last Updated: 2024-03-28 05:03:50 English Course Type: Self Learning

Machine Learning Core Concepts in Detail
Understand use-case scenarios for applying Machine Learning
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
Exploratory Data Analysis Techniques
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 Machine Learning Engineer or Data Scientist? if yes, then this course is for you.

In this course, you will learn about core concepts of Machine Learning, use cases, role of Data, 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 build Classification Models using a range of Algorithms, Regression Models and Clustering Models. You will learn the scenarios and use cases of deploying Machine Learning 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 Machine Learning

Types of Machine Learning Algorithms

Use cases of Machine Learning

Role of Data in Machine Learning

Understanding the process of Training or Learning

Understanding Validation and Testing

Introduction to Python

Setting up your 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

Cross Validation

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 Project

Introduction to Deep Learning

Some exposure to Programming Languages will be useful

Aspiring Machine Learning Engineers
Aspiring Data Science Professionals

Section 1 : Introduction to Machine Learning 12 Lectures 02:12:44
Lecture 1 :
Introduction to Machine Learning Preview
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 2 : Statistical Techniques 8 Lectures 01:42:25
Lecture 1 :
Types of Data and Descriptive Statistics
Lecture 2 :
Random Variables and Normal Distribution
Lecture 3 :
Histograms and Normal Approximation
Lecture 4 :
Central Limit Theorem
Lecture 5 :
Probability Theory
Lecture 6 :
Binomial Theory - Expected Value and Standard Error
Lecture 7 :
Hypothesis Testing
Lecture 8 :
Statistics and Experiments
Section 3 : Python for Data Science and Machine Learning 28 Lectures 05:07:06
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 8 Lectures 01:35:36
Lecture 1 :
Exploratory Data Analysis
Lecture 2 :
Tools and Processes of EDA
Lecture 3 :
EDA-Project-1
Lecture 4 :
EDA-Project-2
Lecture 5 :
EDA-Project-3
Lecture 6 :
EDA-Project-4
Lecture 7 :
EDA-Project-5
Lecture 8 :
EDA-Project-6
Section 5 : Linear Regression 13 Lectures 02:47:27
Lecture 1 :
Linear Regression Introduction
Lecture 2 :
Training and Cost Function
Lecture 3 :
Cost Functions and Gradient Descent
Lecture 4 :
Linear Regression - Practical Approach
Lecture 5 :
Feature Scaling and Cost Functions
Lecture 6 :
OLS Assumptions and Testing
Lecture 7 :
Car Price Prediction
Lecture 8 :
Data Preparation and Analysis 1
Lecture 9 :
Data Preparation and Analysis 2
Lecture 10 :
Data Preparation and Analysis 3
Lecture 11 :
Model Building
Lecture 12 :
Model Evaluation and Optmization
Lecture 13 :
Model Optimization
Section 6 : Logistic Regression 8 Lectures 01:47:03
Lecture 1 :
Logistic Regression Introduction
Lecture 2 :
Logit Model
Lecture 3 :
Telecom Churn Case Study
Lecture 4 :
Data Analysis and Feature Engineering
Lecture 5 :
Build the Logistic Model
Lecture 6 :
Model Evaluation - AUC-ROC
Lecture 7 :
Model Optimization
Lecture 8 :
Model Optimization
Section 7 : Naive Bayes Classification Algorithom 4 Lectures 01:05:38
Lecture 1 :
Naive Bayes Probability Model
Lecture 2 :
Naive Bayes Probability Computation
Lecture 3 :
Employee Attrition Case Study
Lecture 4 :
Model Building and Optmization
Section 8 : Decision Tree Algorithm 6 Lectures 00:59:35
Lecture 1 :
Decision Tree - Model Concept
Lecture 2 :
Decision Tree - Learning Steps
Lecture 3 :
Gini Index and Entropy Measures
Lecture 4 :
Pruning and Hyperparameter Tuning
Lecture 5 :
Iris Dataset Case Study
Lecture 6 :
Model Optimization using Grid Search Cross Validation
Section 9 : Random Forest Ensemble Algorithm 4 Lectures 01:06:57
Lecture 1 :
Ensemble Techniques Bagging and Random Forest
Lecture 2 :
Random Forest Steps Pruning and Optimization
Lecture 3 :
Model Building and Hyperparameter Tuning using Grid Search CV
Lecture 4 :
Optimization Continued
Section 10 : Support Vector Machine 5 Lectures 00:46:24
Lecture 1 :
Support Vector Machine Concepts
Lecture 2 :
Support Vector Machine Metrics and Polynomial SVM
Lecture 3 :
84 Support Vector Machine Project 1
Lecture 4 :
Support Vector Machine Predictions
Lecture 5 :
Support Vector Machine - Classifying Polynomial Data
Section 11 : Dimensionality Reduction - Principle Component Analysis (PCA) 4 Lectures 00:50:16
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 12 : Unsupervised Learning with K-Means 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 13 : Deep Learning 1 Lectures 00:00:00
Lecture 1 :
Introduction to Deep Learning

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Manas Dasgupta,

1896 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.