Machine Learning & Deep Learning with Python | Hands-On AI

Name: Machine Learning & Deep Learning with Python | Hands-On AI
Rating: 4.4 (14 reviews)

Build real ML & neural network models with Python using practical projects, real datasets & case studies studies

Created bySubburaj Ramasamy

Last updated 5/2026

English

What you'll learn

Build Machine Learning models using Python from scratch with real datasets Machine Learning implementation from scratch
Apply supervised and unsupervised learning techniques to solve practical problems
Design and implement Neural Networks and Deep Learning models step-by-step
Work on hands-on projects that simulate real-world industry scenarios
Preprocess, analyze, and prepare data for effective model building
Evaluate and improve model performance using standard ML techniquesnterviews
Understand key algorithms like Regression, Classification, and Clustering in practice
Develop confidence to apply Machine Learning in academic, research, or industry projects
Gain practical exposure to AI workflows used by Machine Learning engineers
Transition from beginner to a job-ready Machine Learning practitioner
Learn from a course designed and refined through academic teaching and real project experience

Course content

7 sections • 55 lectures • 19h 52m total length

Introduction to Machine Learning16:23
What is Machine Learning? Goals, AI, Ml, DL, Fundamentals
Important Success Stories of AI13:16
1. AI in Healthcare: Diagnosing Diseases, 2. AI in Self-Driving Cars, 3. AI in Natural Language Understanding, 4. AI in Business and Customer Service, 5. AI in Gaming and Creativity,6. AI in Scientific Discovery, 7. AI in Retail and E-commerce, 8. Generative AI, 9. DeepMind’s Breast Cancer AI vs NHS Radiologists
Enablers (of AI Revolution)15:45
IBM PC, DARPA, why sudden explosion, Moore's law, GPU, enablers, cloud
Types of Machine Learning24:55
Machine Learning types, types of data, supervised learning, unsupervised learning, reinforcement learning, Exciting applications
Data Cleaning35:16
Data preprocessing tasks, Data Cleaning Tasks, Handling Missing Data, Handling Noisy Data, Install Anaconda Navigator, Launch Jupyter Notebook, numpy, matplotlib, pandas.DataFrame.iloc, Use of SimpleImputer
Data Visualization – Part 128:28
Why Data Collection & Analysis is Important? Data Visualization, Python Libraries for Data Visualization, File formats, Dataframe, Installing pandas, Integrated Development Environments (IDE), Reading head() and Tail of csv file
Data Visualization with Pandas26:28
pandas, line chart, bar chart - stacked horizontal, printing head and tail, histogram - horizontal, Box plot, scatter plot
Data Visualization using Matplotlib and Seaborn38:46
Three Data Visualization Libraries in Python 3, Finding Tail and column headers, Scatter Plot using matplotlib, histogram using pandas using wines data, bar chart using pandas and matplotlib using wines data, Histogram Using seaborn, Gaussian Kernel Density Estimate Inside the Plot, Countplot and barplot in Seaborn
Data Visualization using Plotly and Cufflinks24:36
Installing plotly and cufflinks, Plotting Histogram for R&D Spend, Pie Chart with Plotly Express

Iterative Process of Machine Learning22:16
Some Questions? 11 iterative Steps in Building a Machine Learning Models
Simple Linear Regression27:33
Simple Linear Regression, Regression Analysis, First Order Linear Model, Examples of Linear Regression, Learning, Model Representation, Regression model, Estimating the Coefficients, The Least Squares (Regression) Line, Error Variable: Required Conditions, The Normality of Epsilon, Assessing the Model, Sum of Squares for Error
Linear Regression Example 113:00
Program for Linear Regression - dataset position salaries
Linear Regression Example 29:29
Program for Linear Regression - 50_Start ups
Categorical Variables30:22
Purpose of Encoding Categorial Variables, Types of Encoding, Label Encoding, OneHotEncoding
Data Split and Feature Scaling25:32
Train-Test Data split, Feature scaling, Different types of Feature Scaling, Feature Scaling Methodology, StandardScaler
Gradient Descent25:30
hypothesis function for simple linear regression, model parameter estimation methodologies for machine learning, Cost Function, Gradient Descent Algorithm for estimating parameters, Training Rate, Gradient Descent For Linear Regression, Linear Regression Steps
Multiple Linear Regression27:24
Polynomial Linear Regression, Vectorized linear regression, Gradient Descent for Multiple Features, Feature Scaling, Mean Normalization, Learning Rate, Two Options for Parameter Estimation, Normal Equation
Multiple Linear Regression – Case Study22:29
Case studies on Multiple Linear Regression and Simple Linear Regression, Data: 50_Startups. Goodness of fit measures used to evaluate the performance of machine learning models, MSE, R Square
Polynomial Regression31:13
What is polynomial regression? Polynomial Function and Features
Analytical Solution19:25
What is normal equation? Goal of Linear Regression, Normal Equation vs. Gradient descent

Logistic Regression21:05
What is classification? Similarity, Binary Logistic Regression, Binary Classifier, Outcomes, Sigmoid function, Logistic Regression - Hypothesis Representation, LR intution, Decision boundary, Regularization, Modified Gradient Descent for Logistic Regression
Logistic Regression - Experimentation in Jupyter Notebook21:11
Logistic Regression steps, Fitting Logistic Regression to the Training set, Fit and Predict Methods, confusion matrix, how sklearn carries out logistic regression, Stochastic Gradient Descent (SGD), Comparison with batch gradient descent
Naive Bayes Classifier29:49
Support Vector Machine19:36
SVM Kernel trick20:19
Non-linear SVMs: Feature spaces, Kernel trick, Polynomial and other kernels, SVM with linear kernel, 4 types, SVM Regression
K nearest neighbors (KNN) Classification & Regression21:23
Euclidean Distance, Minkowski Distance, illustrating k nearest neighbors, Choosing k, Implementing KNN

Classification and Regression Tree (CART) with Entropy39:35
Decision Trees, Types of Decision Trees, Terminology, Spliting and Pruning, Decision Tree Learning, Algorithm to generate classification and regression trees, Sklearn.DecisionTreeClassifier, Entropy, Algorithm for creating Decision Tree Classifier, Classifier Implementation, Tree generated, Multi-class classification, Multiclass Classification Tree, Iris Dataset Decision Tree Regression,
Decision Trees based on gini criterion27:25
Gini Impurity, Calculation of gini impurity, Tree Diagram for split based on gini, Tree Diagram for split based on entropy
Ensemble Learning – Random Forest24:51
Ensemble methods, Ensemble Philosophy, Ensemble Approaches, BAGGing, Random Forest Models, Random Forest Implementation, Classification Report,
Grid Search for Parameter Tuning14:48
Grid Search for Optimal parameters, Examples with customer churn dataset, Iris dataset
Ensemble Learning – Boosting21:47
popular boosting algorithms, Boosting strategy, AdaBoost, Gradient Boosting

Introduction to clustering23:13
Clustering algorithms, Applications, Good Clustering examples, different types of distance measures, Measuring (dis)similarity, Categorical Variables and distances
K Means Clustering28:46
K-Means Clustering approach, examples, Implementation using Jupyter Notebook
Elbow Method to Determine K18:10
Elbow method, cost, inertia, Parameters of KMeans, Random Initialization Trap, K-Means++, Applying to dataset
K Medoids Clustering25:29
K medoids clustering with examples
Dendrogram and Hierarchical Clustering21:59
Hierarchical Clustering, hc Algorithm, Constructing Dendrogram, Value of Dendrogram, TwoTypes of Hierarchical Clustering, Constructing Dendrogram, Agglomerative Clustering (Bottom-Up), Divisive, threshold value,
Agglomerative Clustering18:37
Agglomerative algorithm, Agglomerative Clustering (Bottom-Up), Single Link Agglomerative Clustering, Complete Link Agglomerative Clustering, Average Link Agglomerative Clustering, Ward’s linkage, Hierarchical Clustering, Divisive Clustering (Top-Down)
Clustering Performance Evaluation13:17
Performance evaluation, Silhouette Score, Davies-Bouldin Index,

Bias – Variance13:19
Bias-Variance Trade-off, Low Bias, High Variance, High Bias, Low Variance, Balanced Bias and Variance, How to manage?
Bias – Variance Trade off11:51
Graphical Visualization of Bias and Variance, Bias and Variance Tradeoff
Curse of Dimensionality11:14
Problem with KNN Classifiers, Curse of Dimensionality illustration, General Problem in Machine Learning, Why Overfitting occurs? Dimensionality Reduction
Learning Curve11:22
Bad Learning Curve : High Bias, Bad Learning Curve : High Variance, ideal learning curve, relationship between bias and variance, Error and Noise
Parametric and non-parametric models11:15
Parametric Models, Non- Parametric Models, The Learning Process
Cross-validation and the Bootstrap21:01
Resampling, Test & Training Error, Validation Set Approach, Drawbacks, Leave-One-Out Cross-Validation, k-fold Cross-Validation, Bias – Variance Trade off, The Bootstrap, Parameter tuning, Hyper-parameter selection, Hyperparameters and cross-validation, k-fold Cross-Validation,
Case Studies13:17
Case Study 1 - k fold cross validation with Naive Bayes Classifier
Case Study 2 - k fold cross validation with SVM classifier
Dimensionality Reduction - Principal Component Analysis27:46
Dimensionality reduction Approaches, Principal Component Analysis (PCA), Steps for PCA, What PCA does? Steps in Jupyter Notebook, Finding contribution of each feature, Explained variance with all features
Linear Discriminant Analysis (LDA)15:09
Fundamentals of Linear Discriminant Analysis, Steps, PCA vs LDA, Carrying out LDA in Jupyter Note Book

Introduction11:52
•What are Artificial Neural Networks? Biological Motivation, Models of the Brain, Pigeons as art experts
Biological Motivation for ANN20:31
Biological Neuron, How the Human Brain learns, Brain and ANN, Properties of Artificial Neural Networks, characteristics of Biological Neural Networks
ANN Representation24:02
History, Neural Network Representation, Interesting Application of ANN, A Neuron Model, A Simple Neuron
ANN Types17:45
Pattern Recognition with example, A Complicated Perceptron, Different types of Neural Networks, Feed-forward networks, Feedback Networks, Network Layers, Recurrent Neural Networks
Perceptrons24:54
Perceptrons – How we Use? Feed Forward Networks, Activation Functions
Training ANN27:46
Network Structure, Network Parameters, Weights, Size of Training Data, Training: Back propagation algorithm, Steps Involved in ANN modeling, Gradient Descent, The Learning Process, Neural Network in Use, Modeling and Diagnosing the Cardiovascular System
ANN Case Study 123:15
This case study is on Binary classification with ANN.
ANN Case Study 2 – Multi-class classification16:19
Case study application of ANN to multi-class classification.

Requirements

No programming experience is needed. You will learn everything from the course.
You need a computer with Python 3 and Jupyter Note book installed.

Description

Build real-world Machine Learning & Deep Learning models with Python—through hands-on projects, practical datasets, and clear step-by-step guidance.

This intensive course is designed to help you become a confident Machine Learning practitioner by solving real-world problems relevant to industry and research.

Whether you are a student, engineer, or professional, you will gain the skills to apply ML techniques in your projects and develop job-ready expertise in AI and data science.

Created by an experienced professor and refined through classroom teaching and real project implementation, this course is practical, structured, and up-to-date.

This exemplary, engaging, enlightening and enjoyable course is organized as seven interesting modules, with abundant worked examples in the form of code executed on Jupyter Notebook.

By the end of this course, you will be able to build, evaluate, and apply Machine Learning models to real-world problems with confidence.

It is important that data is visualized before attempting to carryout machine learning and hence we start the course with a module on data visualization. This is followed by a full blown and enjoyable exposure to Regression covering simple linear regression, polynomial regression, multiple linear regression.

Regression is followed by extensive discussions on another important supervised learning algorithms on Classification. We carry out modeling using classification strategies such as logistic regression, Naive Bayes classifier, support vector machine, K nearest neighbor, Decision trees, ensemble learning, classification and regression trees, random forest and boosting - ada boost, gradient boosting.

From supervised learning we move on to discuss about unsupervised learning - clustering for unlabelled data. We study the hierarchical, k means, k medoids and Agglomerative Clustering. It is not enough to know the algorithms, but also strategies such as bias variance trade off and curse of dimensionality to be successful in this challenging field of current and futuristic importance. We also carry out Principal Component analysis and Linear discriminant analysis to deal with curse of dimensionality.

The last section leads the reader to deep learning through a lucid introduction to Artificial Neural network (ANN) and back propagation algorithm for estimating weights of feed forward network. Before we close, we take up 2 case studies- one on binary classification and another on multi-class classification using ANN, to give a feel of deep learning.

Who this course is for:

Engineers employed in Data Science
Professionals engaged in Machine Learning, Deep Learning and Artificial intelligence
Students and teachers of the subject in universities and colleges

Machine Learning & Deep Learning with Python | Hands-On AI

What you'll learn

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Course content

Introduction to Machine Learning and Data Visualization9 lectures • 3hr 44min

Linear Regression11 lectures • 4hr 14min

Classification Part 16 lectures • 2hr 13min

Classification part 25 lectures • 2hr 8min

Clustering7 lectures • 2hr 30min

Model Selection Strategies9 lectures • 2hr 16min

Artificial Neural Networks & Intro’ to Deep Learning8 lectures • 2hr 46min

Requirements

Description

Who this course is for: