Machine Learning Course with Python
Categories: Machine Learning, Python
About Course
Hi guys! I am Siddhardhan. I work in the field of Data Science and Machine Learning. It all started with my curiosity to learn about Artificial Intelligence and the ability of AI to solve several Real Life Problems. I worked on several Machine Learning & Deep Learning projects involving Computer Vision.
I am on this journey to empower as many students & working professionals as possible with the knowledge of Machine Learning and Artificial Intelligence.
In this full (68.42hrs) course, you will learn everything from python programming to mathematics to machine learning by creating more than 10 real world projects.
Join Now for Free.
Course By – “Siddhardhan”
What Will You Learn?
- You will learn fundamentals of Machine Learning
- You will learn differences about Machine Learning, Artificial Intelligence, Deep Learning, etc.
- You will learn Python programming language from beginning for Machine Learning
- You will learn about Python Libraries used for Machine Learning
- You will learn about data collection and processing for Machine Learning
- You will learn Mathematics used for Machine Learning
- You will learn about Machine Learning Models and learn to use them as well as create them from scratch
- You will learn to validate Machine Learning Models
- You will learn to create and deploy Machine Learning Models
Course Content
Machine Learning Basics
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Machine Learning Course Curriculum
09:24 -
1.1 AI vs Machine Learning vs Deep Learning | AI vs ML vs DL
05:35 -
1.2. Supervised vs Unsupervised vs Reinforcement Learning | Types of Machine Learning
07:04 -
1.3. Supervised Learning | Types of Supervised Learning
06:13 -
1.4. Unsupervised Learning | Clustering and Association Algorithms in Machine Learning
08:03 -
1.5. What is Deep Learning | Deep Learning Tutorial | Deep Learning Simplified
08:40
Python Basics for Machine Learning
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2.1. Google Colaboratory for Python | Getting started with Google Colaboratory | Google Colab basics
10:17 -
2.2. Python Basics | Python Tutorial For Beginners | Learn Python Programming from Scratch
23:04 -
2.3. Python Basic Data Types | Python Tutorial | int float string complex boolean
20:40 -
2.4. Python Special data types | List Tuple Set Dictionary
27:03 -
2.5. Operators in Python | Python Tutorial |Arithmetic Assignment Comparison Logical Identity Member
19:29 -
2.6. if else statement in Python | if else | if elif else | nested if statement
13:59 -
2.7. Loops in Python | For Loop in Python | While Loop in Python
15:50 -
2.8. Functions in Python
15:12
Important Python Libraries for Machine Learning
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3.1. Complete Numpy Tutorial in Python | Numpy Arrays
45:52 -
3.2. Complete Pandas Tutorial in Python | Pandas Dataframe Tutorial
47:04 -
3.3. Matplotlib Tutorial in Python
30:54 -
3.4. Seaborn Tutorial in Python
35:56
Data Collection and Pre-Processing
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4.1. Where to Collect Data For Machine Learning?
13:26 -
4.2. Importing Datasets through Kaggle API
14:29 -
4.3. Handling Missing Values in Machine Learning | Imputation | Dropping
21:58 -
4.4. Data Standardization | Data Preprocessing
20:14 -
4.5. Label Encoding | Data Pre-Processing
19:18 -
4.6. Train Test Split | Splitting the dataset to Training and Testing data
12:32 -
4.7. How to Handle imbalanced Dataset | Data Pre-Processing
19:10 -
4.8. Feature extraction of Text data using Tfidf Vectorizer | Data Preprocessing
11:57 -
4.9. Numerical Dataset Pre-Processing – Use Case
20:35 -
4.10. Text Dataset Pre-Processing – Use Case
36:21
Mathematics for Machine Learning
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5.0. Mathematics for Machine Learning – Introduction
06:05 -
5.1.1. Linear Algebra – Vectors
10:19 -
5.1.2. Vector Operations – Part 1 | Mathematics for Machine Learning | Linear Algebra
13:43 -
5.1.3. Vector Operations – in Python – Part 1 | Math for Machine Learning | Linear Algebra
19:53 -
5.1.4. Vector Operations – Part 2 | Dot Product | Cross Product | Projection of vector | Math for ML
10:24 -
5.1.5. Vector Operations – in Python – Part 2 | Dot Product | Cross Product | Projection of vector
18:59 -
5.1.6. Matrix – Basics | Math for Machine Learning | Linear Algebra
14:58 -
5.1.7. Working with Matrix in Python | Mathematics for Machine Learning | Linear Algebra
19:28 -
5.1.8. Matrix Operations – Addition, Subtraction, Multiplication | Mathematics for Machine Learning
19:56 -
5.1.9. Matrix Operations in Python | Mathematics for Machine Learning | Linear Algebra
32:25 -
5.2.1. Statistics for Machine Learning | Machine Learning course
08:58 -
5.2.2. Basics of Statistics | Types of Data in Statistics | Statistics for Machine Learning
13:40 -
5.2.3. Types of Statistics | Descriptive and Inferential Statistics | Machine Learning Course
14:05 -
5.2.4. Types of statistical studies | Statistics for Machine Learning | Machine Learning course
12:24 -
5.2.5. Population and Sample | Sampling techniques | Statistics for Machine Learning
23:38 -
5.2.6. Measure of Central Tendencies – Mean, Median, Mode | Statistics for Machine Learning
16:32 -
5.2.7. Measure of Variability – Range, Variance, Standard Deviation | Math for Machine Learning
12:54 -
5.2.8. Percentiles and Quantiles | Statistics for Machine Learning | Machine Learning Course
08:56 -
5.2.9. Correlation and Causation | Statistics for machine learning | Machine Learning Course
13:43 -
5.2.10. Hypothesis Testing | Null Hypothesis and Alternative Hypothesis | Math For Machine Learning
10:12 -
5.3.1. Probability for Machine Learning | Machine Learning Course
08:27 -
5.3.2. Basics of Probability | Probability for Machine Learning | Machine Learning Course
10:13 -
5.3.3. Random Variables and its types | Discrete Random Variables | Continuous Random Variables
09:41 -
5.3.4. Probability Distribution for Random Variable | Machine Learning Course
10:07 -
5.3.5. Normal Distribution or Gaussian Distribution | Skewness | Probability for Machine Learning
09:47 -
5.3.6. Poisson Distribution | Probability for Machine Learning
10:38
Machine Learning Models
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6.1. What is a Machine Learning Model?
21:07 -
6.2. Supervised Learning Models
08:10 -
6.3. Unsupervised Learning Models
06:54 -
6.4. How to choose the right Machine Learning Model | Model Selection | Cross Validation
14:23 -
6.5. Overfitting in Machine Learning | Causes for Overfitting and its Prevention
14:14 -
6.6. Underfitting in Machine Learning | Causes for Underfitting and its Prevention
08:47 -
6.7. Bias Variance Tradeoff | Machine Learning
18:49 -
6.8. Loss Function in Machine Learning
14:19 -
6.9. Model Evaluation in Machine Learning | Accuracy score | Mean Squared Error
15:48 -
6.10. Model Parameters and Hyperparameters | Weights & Bias | Learning Rate & Epochs
32:43 -
6.11. Gradient Descent in Machine Learning
26:16
Building Machine Learning Models from Scratch
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7.1.1. Linear Regression – Intuition | Machine Learning Models
29:24 -
7.1.2. Linear Regression – Mathematical Understanding
20:50 -
7.1.3. Gradient Descent for Linear Regression
19:06 -
7.1.4. Building Linear Regression from scratch in Python
49:48 -
7.1.5. Implementing Linear Regression from scratch in Python
01:04:09 -
7.2.1. Logistic Regression – Intuition | Machine Learning Course
22:31 -
7.2.2. Math behind Logistic Regression | Machine Learning Models
17:31 -
7.2.3. Loss Function and Cost Function for Logistic Regression
29:13 -
7.2.4. Gradient Descent for Logistic Regression
19:52 -
7.2.5. Building Logistic Regression from scratch in Python
01:05:18 -
7.2.6. Implementing Logistic Regression from scratch in Python
29:16 -
7.3.1. Support Vector Machine Classifier – Intuition
15:52 -
7.3.2. Math behind Support Vector Machine Classifier
31:55 -
7.3.3. Support Vector Machine – Kernels
19:52 -
7.3.4. Loss Function for Support Vector Machine Classifier – Hinge Loss
22:18 -
7.3.5. Gradient Descent for Support Vector Machine Classifier
18:28 -
7.3.6. Building Support Vector Machine Classifier from scratch in Python
01:05:03 -
7.3.7. Implementing Support Vector Machine Classifier from Scratch in Python
58:21 -
7.4.1. Lasso Regression – Intuition
21:05 -
7.4.2. Math Behind Lasso Regression
22:15 -
7.4.3. Gradient Descent for Lasso Regression
18:40 -
7.4.4. Building Lasso Regression from Scratch in Python
53:42 -
7.5.1. K-Nearest Neighbors (KNN) – intuition
17:42 -
7.5.2. Math behind K-Nearest Neighbors (KNN)
14:48 -
7.5.3. Calculating Euclidean and Manhattan distance in Python
23:09 -
7.5.4. K-Nearest Neighbors Classifier from Scratch in Python | KNN Classifier
50:15 -
7.5.5. Implementing K-Nearest Neighbors Classifier from Scratch in Python | KNN Classifier
28:48 -
7.6.1. Decision tree – intuition
17:16 -
7.6.2. Entropy, Information Gain & Gini Impurity – Decision Tree
18:23
Cross Validation; Hyperparameter Tuning; Model Selection
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Module 8. Introduction – Cross Validation; Hyperparameter tuning; Model Evaluation
04:59 -
K Fold Cross Validation | Cross Validation in Machine Learning
17:06 -
8.2. Cross Validation – Python implementation | cross_val_score | Cross Validation in Sklearn
47:20 -
8.3. Hyperparameter Tuning – GridSearchCV and RandomizedSearchCV
13:36 -
8.4. GridSearchCV and RandomizedSearchCV – Python implementation | Hyperparameter Tuning
40:09 -
8.5. Model Selection in Machine Learning | How to choose the right Machine Learning model
15:35 -
8.6. Model Selection in Machine Learning with Python | Choosing the right Machine Learning model
01:05:08 -
8.7. Accuracy Score and Confusion Matrix – Concept & Python implementation | Model Evaluation in ML
32:01 -
8.8. Precision, Recall, F1 score | Model Evaluation
32:12 -
8.9. Precision, Recall, F1 Score – Python Implementation | Model Evaluation in Machine Learning
26:33
Machine Learning Projects
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Project 1 : SONAR Rock vs Mine Prediction with Python | End To End Python Machine Learning Project
49:33 -
Project 2: Diabetes Prediction using Machine Learning with Python | End To End Python ML Project
58:11 -
Project 3. House Price Prediction using Machine Learning with Python | Machine Learning Project
56:28 -
Project 4. Fake News Prediction using Machine Learning with Python | Machine Learning Projects
01:11:50 -
Project 5. Loan Status Prediction using Machine Learning with Python | Machine Learning Project
01:08:55 -
Project 6. Wine Quality Prediction using Machine Learning with Python | Machine Learning Project
58:09 -
Project 7. Car Price Prediction using Machine Learning with Python | Machine Learning Projects
47:53 -
Project 8. Gold Price Prediction using Machine Learning with Python | Machine Learning Projects
40:55 -
Project 9. Heart Disease Prediction using Machine Learning with Python | Machine Learning Projects
42:55 -
Project 10. Credit Card Fraud Detection using Machine Learning in Python | Machine Learning Projects
49:35 -
Project 11. Medical Insurance Cost Prediction using Machine Learning with Python | ML Projects
01:05:09 -
Project 12. Big Mart Sales Prediction using Machine Learning with Python | Machine Learning Projects
01:17:11 -
Project 13. Customer Segmentation using K-Means Clustering with Python | Machine Learning Projects
49:47 -
Project 14. Parkinson’s Disease Detection using Machine Learning – Python | Machine Learning Project
01:09:58 -
Project 15. Titanic Survival Prediction using Machine Learning in Python | Machine Learning Project
01:13:12 -
Project 16. Calories Burnt Prediction using Machine Learning with Python | Machine Learning Projects
01:12:32 -
Project 17. Spam Mail Prediction using Machine Learning with Python | Machine Learning Projects
01:02:54 -
Project 18. Movie Recommendation System using Machine Learning with Python
01:15:03 -
Project 19. Breast Cancer Classification using Machine Learning | Machine Learning Projects
58:55 -
Deploy Machine Learning Model using Streamlit in Python | ML model Deployment
40:24 -
DL Project 1. Breast Cancer Classification with Neural Network | Deep Learning Projects in Python
01:21:07 -
Processing Image data in Python for Deep Learning Applications | Image Processing with Python
41:03 -
DL Project 2. MNIST Digit Classification with Neural Network | Deep Learning Projects in Python
01:30:45 -
Multiple Disease Prediction System using Machine Learning in Python | Streamlit Web App – Deployment
01:08:16 -
How to Deploy Machine Learning Model as an API in Python – FastAPI
43:25 -
Deploying ML model as Public API using FastAPI and Ngrok in Google Colaboratory
21:02 -
Deploying Machine Learning model as API on Heroku | FastAPI | Heroku | Python | ML
24:46 -
Deploying a Machine Learning web app using Streamlit on Heroku
18:21 -
DL Project 4. CIFAR – 10 Object Recognition using ResNet50 | Deep Learning Projects in Python
01:35:45 -
DL Project 5. Face Mask Detection using Convolutional Neural Network (CNN) – Deep Learning Projects
01:21:21 -
DL Project 5. Face Mask Detection using Convolutional Neural Network (CNN) – Deep Learning Projects
01:21:21
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