Skip to Content
Home └Data Science and Machine Learning
Data Science and Machine Learning

Data Science and Machine Learning

Analyse data and build, evaluate and deploy machine learning models with Python.

Delivered by Appsolworld as instructor-led online, classroom or 1-to-1 training, with hands-on online labs available 24x7.

Self-paced course
₹15,000
or start with ₹5,000 and pay the balance within 3 months
₹
Pay in full ₹15,000
  • ○ Lessons, videos and quizzes from ₹5,000
  • 🔒 Lab recordings at ₹15,000
  • 🔒 Certification exam at full payment
  • ○ Optional real lab: ₹5,000 per month (after full payment)
Valid 12 months from your first payment. Refund policy
Responsible XertApp
Last Update 10/25/2026
Completion Time 16 hours 42 minutes
Members 1
Career Courses Hot Skills AI & Machine Learning
  • Module 1 — Introduction to data science and machine learning (empty)
    12Lessons · 1 hr 36 min
    • 1.1 Video: What data science and machine learning are
    • 1.1 What data science and machine learning are
    • 1.2 Video: Machine learning compared with traditional programming
    • 1.2 Machine learning compared with traditional programming
    • 1.3 Video: Types of machine learning
    • 1.3 Types of machine learning
    • 1.4 Video: The machine learning workflow
    • 1.4 The machine learning workflow
    • 1.5 Video: Tools of the trade: Python, Jupyter and libraries
    • 1.5 Tools of the trade: Python, Jupyter and libraries
    • 1.L Lab: Your first data science notebook
      Lab
    • Module 1 Quiz: Introduction to Data Science and ML
      10 xp
  • Module 2 — Python for data science (empty)
    12Lessons · 1 hr 36 min
    • 2.1 Video: Python basics: variables, types and operators
    • 2.1 Python basics: variables, types and operators
    • 2.2 Video: Lists, tuples, sets and dictionaries
    • 2.2 Lists, tuples, sets and dictionaries
    • 2.3 Video: Control flow and functions
    • 2.3 Control flow and functions
    • 2.4 Video: NumPy arrays
    • 2.4 NumPy arrays
    • 2.5 Video: Pandas DataFrames
    • 2.5 Pandas DataFrames
    • 2.L Lab: Python data structures and Pandas
      Lab
    • Module 2 Quiz: Python for Data Science
      10 xp
  • Module 3 — Working with data (empty)
    12Lessons · 1 hr 36 min
    • 3.1 Video: Loading data from files, databases and APIs
    • 3.1 Loading data from files, databases and APIs
    • 3.2 Video: Exploring a dataset
    • 3.2 Exploring a dataset
    • 3.3 Video: Visualising data with Matplotlib and Seaborn
    • 3.3 Visualising data with Matplotlib and Seaborn
    • 3.4 Video: Statistics every data scientist needs
    • 3.4 Statistics every data scientist needs
    • 3.5 Video: Exploratory data analysis in practice
    • 3.5 Exploratory data analysis in practice
    • 3.L Lab: Exploring and visualising data
      Lab
    • Module 3 Quiz: Working with Data
      10 xp
  • Module 4 — Data preprocessing (empty)
    12Lessons · 1 hr 36 min
    • 4.1 Video: Handling missing data
    • 4.1 Handling missing data
    • 4.2 Video: Encoding categorical data
    • 4.2 Encoding categorical data
    • 4.3 Video: Training and test sets, and data leakage
    • 4.3 Training and test sets, and data leakage
    • 4.4 Video: Feature scaling
    • 4.4 Feature scaling
    • 4.5 Video: Preprocessing pipelines with scikit-learn
    • 4.5 Preprocessing pipelines with scikit-learn
    • 4.L Lab: A preprocessing pipeline
      Lab
    • Module 4 Quiz: Data Preprocessing
      10 xp
  • Module 5 — Regression (empty)
    12Lessons · 1 hr 36 min
    • 5.1 Video: Simple linear regression
    • 5.1 Simple linear regression
    • 5.2 Video: Multiple linear regression
    • 5.2 Multiple linear regression
    • 5.3 Video: Polynomial regression
    • 5.3 Polynomial regression
    • 5.4 Video: Decision tree and random forest regression
    • 5.4 Decision tree and random forest regression
    • 5.5 Video: Evaluating regression models and regularisation
    • 5.5 Evaluating regression models and regularisation
    • 5.L Lab: Predicting salaries and profits
      Lab
    • Module 5 Quiz: Regression
      10 xp
  • Module 6 — Classification (empty)
    12Lessons · 1 hr 36 min
    • 6.1 Video: Logistic regression
    • 6.1 Logistic regression
    • 6.2 Video: K-nearest neighbours
    • 6.2 K-nearest neighbours
    • 6.3 Video: Naive Bayes
    • 6.3 Naive Bayes
    • 6.4 Video: Support vector machines
    • 6.4 Support vector machines
    • 6.5 Video: Evaluating classifiers
    • 6.5 Evaluating classifiers
    • 6.L Lab: Will they buy? Logistic regression, KNN and SVM
      Lab
    • Module 6 Quiz: Classification
      10 xp
  • Module 7 — Trees, ensembles and model tuning (empty)
    12Lessons · 1 hr 36 min
    • 7.1 Video: Decision trees for classification
    • 7.1 Decision trees for classification
    • 7.2 Video: Random forests and boosting
    • 7.2 Random forests and boosting
    • 7.3 Video: Cross-validation and hyperparameter tuning
    • 7.3 Cross-validation and hyperparameter tuning
    • 7.4 Video: Imbalanced data
    • 7.4 Imbalanced data
    • 7.5 Video: Bias, variance and overfitting
    • 7.5 Bias, variance and overfitting
    • 7.L Lab: Trees, forests and tuning
      Lab
    • Module 7 Quiz: Trees, Ensembles and Tuning
      10 xp
  • Module 8 — Unsupervised learning: clustering and dimensionality reduction (empty)
    12Lessons · 1 hr 36 min
    • 8.1 Video: What clustering is for
    • 8.1 What clustering is for
    • 8.2 Video: K-means and the elbow method
    • 8.2 K-means and the elbow method
    • 8.3 Video: Hierarchical clustering
    • 8.3 Hierarchical clustering
    • 8.4 Video: Dimensionality reduction with PCA
    • 8.4 Dimensionality reduction with PCA
    • 8.5 Video: Customer segmentation in practice
    • 8.5 Customer segmentation in practice
    • 8.L Lab: Customer segments with K-means
      Lab
    • Module 8 Quiz: Clustering and Dimensionality Reduction
      10 xp
  • Module 9 — Natural language processing and deep learning (empty)
    12Lessons · 1 hr 36 min
    • 9.1 Video: Natural language processing basics
    • 9.1 Natural language processing basics
    • 9.2 Video: Bag of words, TF-IDF and text classification
    • 9.2 Bag of words, TF-IDF and text classification
    • 9.3 Video: Artificial neural networks
    • 9.3 Artificial neural networks
    • 9.4 Video: Training deep learning models
    • 9.4 Training deep learning models
    • 9.5 Video: Images and transfer learning
    • 9.5 Images and transfer learning
    • 9.L Lab: A spam filter with NLP and a neural network
      Lab
    • Module 9 Quiz: NLP and Deep Learning
      10 xp
  • Module 10 — Reinforcement learning and machine learning in production (empty)
    12Lessons · 1 hr 36 min
    • 10.1 Video: Reinforcement learning concepts
    • 10.1 Reinforcement learning concepts
    • 10.2 Video: Multi-armed bandits: UCB and Thompson sampling
    • 10.2 Multi-armed bandits: UCB and Thompson sampling
    • 10.3 Video: Saving and deploying models
    • 10.3 Saving and deploying models
    • 10.4 Video: Responsible and explainable AI
    • 10.4 Responsible and explainable AI
    • 10.5 Video: An end-to-end data science project
    • 10.5 An end-to-end data science project
    • 10.L Lab: Choosing the best ad with UCB, and saving a model
      Lab
    • Module 10 Quiz: Reinforcement Learning and Production
      10 xp
  • Final Certification (empty)
    1Lessons · 40 min
    • Certification Exam: Data Science and Machine Learning