Machine Learning with Python

  • Course level: All Levels


Self-Paced, Live Online & Classroom Enterprise Training

Machine Learning with python course delves deeper into the Machine Learning fundamental concepts further explaining Machine Learning algorithms and their implementation. Equip your team with the necessary skills to develop AI based intelligent applications for your organization with hands-on training in Machine Learning with Python.

What Will I Learn?

  • At the end of this Machine Learning with Python Certification course, you will be able to:
  • Appreciate the breadth & depth of ML applications and use cases in real-world scenarios.
  • Import and wrangle data using Python libraries and divide them into training and test datasets
  • Data preprocessing techniques, Univariate and Multivariate analysis, Missing values and outlier treatment etc
  • Implement linear and polynomial regression, understand Ridge and lasso Regression,
  • Implement various type of classification methods including SVM, Naive bayes, decision tree, and random forest
  • Interpret Unsupervised learning and learn to use clustering algorithms
  • Tuning of ML solutions, Bias-variance tradeoff, Minibatch, and Shuffling, Overfitting avoidance
  • Basics of Neural Networks, Perceptron, MLP
  • Build real-world solutions using MLP

Topics for this course


Introduction to Machine Learning?

What is ML? Applications of ML Why ML? Uses of ML Machine learning methods Machine learning algorithms(Regression, Classification, Clustering, Association) A brief introduction python libraries

Creating a Machine Learning Model?

Types of ML algorithms Labelled Dataset Training and Testing Data Importing the Libraries Importing the Dataset Demo: Creating a machine model

Data Preparation and Exploration?

What is data? What is information? Analyzing data to fetch the information Entropy, Information gain Data exploration and preparation Univariate, bivariate, and multivariate analysis Correlation Chi-Square, Z-test, T-test, ANOVA Categorical Data Feature Scaling Dimensionality Reduction outliers


What is regression? Applications of regression Types of regression Fitting the regression line Simple linear regression Simple linear regression in python Polynomial regression Polynomial regression in python Gradiant Descent Cost function Regularization Demo: Perform regression on a real world dataset Ridge and lasso Regression


How is classification used? Applications of classification Logistic Regression, Sigmoid function Decision tree K-Nearest Neighbors (K-NN) SVM Naive Bayes Understand limitations of linear classifer and evaluate abilities of non-linear classifiers using a data set

Evaluation of Classification Models?

Confusion Matrix Precision, Recall F1-score RoC, AuC n-fold cross validation Measuring classifier performance Overfitting Ensemble Learning Bagging and Boosting

Unsupervised Learning – Clustering?

Application of Unsupervised learning, examples, and applications Clustering Hierarchical Clustering in Python, Agglomerative and Divisive techniques Measuring the distanvce between two clusters k-means algorithm Limitations of K-means clustering SSE and Distortion measurements Demo: Agglomerative Hierarchical clustering

Dimensionality Reduction?

What is dimensionality reduction? Applications of dimensionality reduction Feature selection Feature extraction Dimensionality reduction via Principal component analysis Eigenvalue and Eigenvectors Hands on PCA on MNSIT data

Reinforcement Learning?

What is reinforcement learning Applications of reinforcement learning An Example use case Components of RL Approachs to RL RL algorithms Deep reinforcement learning

Introduction to Natural Language Processing (NLP)?

What is NLP? Why NLP Applications of NLP Components of NLP NLP techniques

Introduction to Deep Learning?

Why deep learning? Neural networks Applications of neural networks Biological Neuron vs Artificial Neuron Artificial Neural networks, layers

About the instructor

5.00 (1 ratings)

79 Courses

12 students

Machine Learning with Python

Material Includes

  • Enterprise Reporting
  • Lifetime Access
  • CloudLabs
  • 24x7 Support
  • Real-time code analysis and feedback
  • 100% Money Back Guarantee


  • Basic Python programming knowledge and fundamentals of data analysis required
  • Basic knowledge of statistics and mathematics is good to have

Target Audience

  • Data Analyst who want to gain expertise in Predictive Analytics
  • Developers
  • Data Architects
  • Tech Leads handling a team of Analysts