"Python for Machine Learning” is an immersive course designed to equip you with the skills and knowledge needed to harness the power of Python in the field of machine learning. Whether you're a beginner or an experienced coder, this course provides a comprehensive introduction to Python's applications in machine learning, covering everything from data preprocessing to model deployment.

The Python for Machine Learning course offers comprehensive coverage of Python programming for machine learning and artificial intelligence applications. It is designed to equip learners with the essential skills required to harness the power of Python in the field of machine learning. This course covers topics such as data preprocessing, supervised and unsupervised learning, deep learning, and more. Whether you are a novice or an experienced programmer, this course provides the knowledge and tools to excel in the exciting world of machine learning.


USD 30,800 /-
USD 36,300 /-

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Course Highlights

Data Preprocessing: Learn how to prepare and clean data for machine learning tasks.

Supervised Learning: Master algorithms for classification and regression tasks.

Unsupervised Learning: Dive into clustering and dimensionality reduction techniques.

Deep Learning: Explore neural networks and deep learning frameworks like TensorFlow and PyTorch.

Real-world Projects: Apply your knowledge to practical machine learning projects.

Model Evaluation: Understand how to assess the performance of machine learning models.

Ensemble Methods: Discover techniques like bagging and boosting for model improvement.

Natural Language Processing: Work with text data and language processing tools.

Hands-on Labs: Gain practical experience through hands-on coding exercises.

Free Certificate: Receive a certificate upon course completion.



After you complete the course with Skillfloor, you will receive a Python for Machine Learning Certification, validating your expertise in using Python for machine learning applications. This certification is a valuable addition to your resume and a testament to your proficiency in this high-demand field. Achieving certification in Python for Machine Learning is a significant milestone for individuals aspiring to thrive in the rapidly evolving field of artificial intelligence and data science. This certification attests to your proficiency in utilizing Python's powerful libraries and frameworks, such as TensorFlow, sci-kit-learn, and Keras, to create, train, and deploy machine learning models. It highlights your ability to work with data, perform feature engineering, and implement various machine-learning algorithms to solve real-world problems. Earning this certification not only solidifies your expertise in Python but also positions you as a capable machine-learning practitioner. With a Python for Machine Learning certification, you demonstrate your readiness to contribute to cutting-edge projects and harness the transformative potential of machine learning across industries, from healthcare to finance and beyond.



Top 10 Reasons For Choosing this Course

Machine Learning Mastery: Acquire the skills to excel in the booming field of machine learning.

Python Proficiency: Harness the power of Python, the preferred language for ML.

Practical Experience: Apply what you learn through hands-on projects.

Expert Instruction: Learn from industry professionals with real-world experience.

Flexible Learning: Study at your own pace and convenience.

Career Advancement: Open doors to lucrative career opportunities in data science and AI.

Certification: Receive a recognized certification upon course completion.

Community: Join a supportive community of learners and mentors.

Real-World Applications: Gain skills applicable to real-world problem-solving.

Future-Proof Skills: Stay relevant in the ever-evolving tech landscape.


Course Curriculum

  •  Introduction to Python programming language
     Introduction to Machine Learning and its categories
     Overview of popular Machine Learning Libraries in Python

  •  Reading data from different file formats
     Cleaning and preprocessing data using Pandas
     Data visualization using Matplotlib and Seaborn

  •  Introduction to supervised learning
     Linear regression and multiple linear regression
     Classification with Knearest neighbours, decision trees, random forests, and logistic regression
     Model Evaluation using different metrics (accuracy, precision, recall, f1score)

  •  Introduction to unsupervised learning
     Clustering: KMeans, Hierarchical and DBSCAN
     Dimensionality Reduction: PCA and tSNE

  •  Neural networks and deep learning
     Structure of artificial neural networks
     Convolutional Neural Networks (CNNs) for computer vision
     Recurrent Neural Networks (RNNs) for natural language processing

  •  Hyperparameter tuning of Machine Learning models
     Model performance evaluation and Crossvalidation
     Ensemble modelling and Stacking

  •  Saving and loading models using different libraries
     Building Machine Learning pipelines for deployment
     Deploying models to production systems
     Building Machine Learning pipelines for deployment
     Deploying models to production systems

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