Download Machine Learning For Chemists: Practical Applications 2025
Machine Learning for Chemists: Practical Applications video course download, Use Data Science to solve chemistry problems: predict molecular properties, classify compounds, and resolve spectra. Machine Learning is transforming the way chemists analyze data, predict properties, and accelerate discoveries in various sub-disciplines of Chemistry. This course is designed to give you the practical skills and confidence to apply machine learning directly to chemical problems.
Starting from the basics, you will learn how to preprocess chemical datasets, explore molecular descriptors, and choose the right algorithms for prediction and classification. Step by step, you will apply regression, classification, and clustering methods to real-world chemical examples. You will gain hands-on experience with Python libraries such as NumPy, pandas, matplotlib, and scikit-learn—without being overwhelmed by unnecessary theory.
What you’ll learn
- Understand the fundamentals of machine learning and how they apply to chemical problems. Confidently handle real-world chemical datasets for ML applications.
- Select and implement supervised and unsupervised learning algorithms for chemistry. Apply regression, classification, and clustering methods to chemical data.
- Interpret model performance using evaluation metrics relevant to chemical research. Visualize chemical datasets and model predictions effectively
- Use Python libraries (scikit-learn, pandas, numpy) for ML tasks in chemistry, Apply ML to predict molecular properties such as Solubility, HOMO-LUMO gap
- Gain practical skills to integrate ML into chemistry. Use ML-driven insights to support research publications and projects.
- Separate the spectra (infra-red spectra) of each compound from a mixture of spectra using Machine learning approaches
Course content
- Section 1: Introduction
- Lecture 1 Introduction
- Lecture 2 Machine Learning (Theory Lecture)
- Lecture 3 Key Concepts of Machine Learning (Target, model training)
- Lecture 4 Machine Learning Types, a graphical presentation
- Section 2: Supervised Learning: Predicitn boiling points of alcohols through Machine Learni
- Lecture 5 Regression method of supervised learning (Theory)
- Lecture 6 Linear Regression example of Boiling Pont of alcohols explained
- Lecture 7 Linear Regression Model equation
- Lecture 8 Analyzing dataset of boiling points of alcohols in NotePad
- Lecture 9 Placing the dataset file in Google Drive and opening Google Drive
- Lecture 10 Setting up Google Colab Environment
- Lecture 11 Mounting Google Drive in Colab
- Lecture 12 Reading dataset of boiling point through Pandas and reading head.
- Lecture 13 Deta Preprocessing approaches
- Lecture 14 Cleaning the dataset before ML
- Lecture 15 Feature Engineering
- Lecture 16 Feature Selection
- Lecture 17 plotting boiling point against possible features (no ML)
- Lecture 18 Importing libraries for supervised machine learning
- Lecture 19 Defining features and Target
- Lecture 20 Completing the code for predicting boiling points
- Lecture 21 Model evaluation Metrices (Theory)
- Lecture 22 Evaluating the model and plotting the results
- Lecture 23 Effect of test size on the performance of ML Model
- Lecture 24 Graph of predicted vs actual boiling points
- Lecture 25 Residual Plot
- Lecture 26 Predicting bp of an unkonw alcohol
- Lecture 27 Model II of Regression (Ridge Model): with standard Scalar for boiling Point
- Lecture 28 Robust Scalar for Ridge Model and coefficients
- Lecture 29 Outliers and How to handle them
- Lecture 30 Model III: Lasso Model of Regression for predicing Boiling Points of Alchohols
- Lecture 31 Cross Validation (Theory)
- Lecture 32 K-fold Cross Validation In Machine Learning
- Lecture 33 Scalars (minmax, standard and Robust) and effect on coefficients of features
- Section 3: Predicting solubliity of compounds using Random Forest model of supervised ML
- Lecture 34 Installing RdKit library in Google Colab
- Lecture 35 Importing Libraries required for the problem
- Lecture 36 Extracting features as descriptors for molecules using smiles in RdKit
- Lecture 37 Loading dataset and preprocessing the data
- Lecture 38 Grid search and the best grid values for the problem
- Lecture 39 Model evaluation and the performance
- Lecture 40 Plotting the results (feature importance, residual frequency, etc)
- Section 4: Supervised Machine Learning for Classification of Compounds
- Lecture 41 Supervised Learning Classification (Theory) I
- Lecture 42 Supervised Learning Classification (Theory) II
- Lecture 43 Classification of compounds example explained
- Lecture 44 Predicting the classification of compounds (importing Libraries)
- Lecture 45 Evaluation of the model throgh Confusion Matrix
- Section 5: Unsupervised Learning: Part 1: Clustering of samples from Wine data
- Lecture 46 Dimensionality Reduction (Principal component analysis PCA)
- Lecture 47 Clustering example explained in theory
- Lecture 48 Analyzing Wine data
- Lecture 49 Unsupervised learning Clustering import libraries
- Lecture 50 Clustering, Plotting PCA1 against PCA2
- Lecture 51 Silhoutte Score
- Lecture 52 DBSCAN Approach for clustering
- Section 6: Unsupervised Learning for blind signal separation
- Lecture 53 Blind Signal Separation example explanation
- Lecture 54 FAST ICA 1: importing libraries and data of mixture spectrum in python
- Lecture 55 FAST ICA II: Completing the code for independent component analysis
- Lecture 56 Plotting two extracted spectrum from the mixture spectrum in parallel
- Lecture 57 Plotting all extracted spectra from the mixture spectrum
- Lecture 58 Identifying cyclohexane by comparing the extracted spectrum with the reference
- Lecture 59 Identifying all components of the mixture by comparison with the reference spect
- Lecture 60 Plotting all four spectra in one figure
- Lecture 61 Plotting all four spectra in one figure (2nd approach)
Who this course is for:
- Graduate students and researchers in chemistry or chemical engineering
- Data scientists and ML engineers interested in scientific applications
- Academic researchers wanting to integrate machine learning into chemical research
- Computational chemistry professionals seeking to expand their ML toolkit
- Laboratory managers implementing data-driven approaches to experimental design
Course details
- Video quality: MP4 | Video: h264, 1280 × 720
- Audio quality: Audio: AAC, 44.1 KHz, 2 Ch
- Last updated 9/2025
- Video duration: 3h 58m
- Number of lessons: 6 sections, 61 lectures
- Language: Language: English
- Compressed file size: 2.3 GB
