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Python for Data Science

Learning Objectives Introduction of Python Fundamentals and OOPs Concepts and NumPy,Pandas,Matplotlib and Seaborn Foundations


  • Introduction to Jupyter Notebook
  • Data Types in Python
  • Data Structures in Python
  • Controlstructures,Loops and Conditional statements in Python
  • Python Functions
  • Basics of Object-Oriented Programming in Python
  • Python Packages and Modules
  • Multi-dimensional arrays (numpy arrays) in Python
  • Create and use NumPy Arrays.
  • NumPy array operations
  • Pandas series and dataframes
  • Loading and writing files using Pandas
  • Data manipulation using dataframes
  • Data aggregation using Pandas
  • Introduction to data visualization
  • Various types of plots – univariate and multivariate
  • Matplotlib plots
  • Seaborn plots
  • Data Wrangling

    Learning Objectives Introduction of Data Wrangling and Exploratory Data Analysis


  • Data loading from various sources
  • Summary statistics
  • Data exploration
  • Dealing with missing values
  • Reshaping the data
  • Filtering data for required conditions
  • Data Manipulation
  • Outlier detection and removal
  • Feature engineering
  • Discretization
  • Data transformation
  • Encoding techniques
  • Grouping and aggregation
  • Data normalization
  • Exploratory Data Analysis
  • Introduction to Statistics
  • Descriptive Statistics
  • Inferential Statistics
  • Probability and Expectations
  • Sampling Distribution
  • Univariate analysis
  • Bivariate analysis
  • Multivariate analysis
  • Data distribution
  • Patterns in the data
  • Basic plotting to extract meaningful information from data
  • Machine Learning

    Learning Objectives Introduction of Machine Learning and Supervised Learning ( Regression, Classification)


  • Introduction to ML problems
  • ML terminologies
  • ML project workflow
  • ML real life examples
  • Regression Modeling Introduction
  • Modeling concept(Regression Modeling)
  • Example problem - Housing price (Regression Modeling)
  • Error metric - SSE, MSE, R Squared(Simple Linear Regression)
  • Least Square algorithm (Simple Linear Regression)
  • Gradient Descent Algorithm (Simple Linear Regression)
  • Implementation using scikit-learn (Simple Linear Regression)
  • Dummy variables(Multiple Linear Regression)
  • Error metric - SSE, MSE, R Squared (Multiple Linear Regression)
  • Gradient Descent Algorithm (Multiple Linear Regression)
  • Feature Selection (Incremental)(Multiple Linear Regression)
  • Implementation using scikit-learn (Multiple Linear Regression)
  • Introduction to Classification Models
  • Error Metrics : Accuracy Score(Classification Modeling)
  • Confusion Matrix(Classification Modeling)
  • Type1 and Type 2 error(Classification Modelings)
  • Decision boundaries(Classification Modeling)
  • Discrete outcomes(Logistic Regression)
  • Logit function(Logistic Regression)
  • Probability scores(Logistic Regression)
  • Implementation using scikit-learn(Logistic Regression)
  • Entropy (DecisionTrees)
  • Using Entropy in classification(DecisionTrees)
  • Information Gain(DecisionTrees)
  • Tree pruning(DecisionTrees)
  • Implementation using scikit-learn(DecisionTrees)
  • Bias variance errors(Random Forests)
  • Ensembling(Random Forests)
  • Randomness in Random Forest(Random Forests)
  • Hyperparameters(Random Forests)
  • Implementation using scikit-learn(Random Forests)
  • Introduction to clustering(Cluster Modeling)
  • Distance measures(Cluster Modeling)
  • Error metrics(Cluster Modeling)
  • Analysing cluster outputs(Cluster Modeling)
  • Agglomerative method(Hierarchical Clustering)
  • Divisive method(Hierarchical Clustering)
  • Understanding Dendrogram(Hierarchical Clustering)
  • Cutting the dendrogram for obtaining clusters(Hierarchical Clustering)
  • Implementation using scikit-learn(Hierarchical Clustering)
  • Distance measures(K-Means Clustering)
  • Centroids and their importance(K-Means Clustering)
  • Steps involved in K-Means(K-Means Clustering)
  • Local optima problem(K-Means Clustering)
  • Implementation using scikit-learn(K-Means Clustering)
  • Principal component analysis(Dimensionality Reduction)
  • Orthogonal transformation(Dimensionality Reduction)
  • Feature selection using PCA(Dimensionality Reduction)
  • Deep Learning

    Learning Objectives Introduction of Deeep Learning and its application, Artificial Neural Networks,Convolutional Neural Networks (CNNs) and its Applications,Recurrent Neural Networks (RNNs) and its Applications,Long Short-Term Memory (LSTMs) and its application,Autoencoders and its application


  • Introduction to Deep Learning and its Applications
  • Basics of Linear Algebra
  • Basics of Calculus
  • Introduction of Artificial Neural Networks
  • Architecture – Layers, weights and neurons
  • Computation in a single neuron
  • Activation of neurons
  • Training (feed forward and back propagation steps)
  • Implementation using Keras
  • Introduction to computer vision
  • CNN architecture
  • Convolution filters
  • Loss function
  • Training CNN models
  • Image classification example using Keras
  • Introduction to sequence prediction problems
  • Types of sequence prediction problems
  • RNN architecture
  • Training RNN models
  • Time series prediction example using Keras
  • LSTM architecture
  • Various gates in a LSTMlayer
  • Training LSTM models
  • Time series prediction example using Keras
  • Introduction Autoencoders and its application
  • Non-Linear dimensionality reduction
  • Encoders and Decoders
  • Training an autoencoder
  • Denoising images using Autoencoders
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    AI and ML with Python Features

    Classroom Training

    96 hours of instructor-led classroom training. There will be weekdays and weekend batch to ensure the convenience of students

    Real-life case studies

    The training will include demonstrations on real-life examples and case studies in order to help you in understanding the concepts in a better way.


    At the end of every class, assignments based on the taught topic will be given to students.

    Lifetime Access

    Upon joining, you will be given lifetime access to LMS. Here you will find learning material, quizzes, and assignments related to the course.

    Expert Support

    In order to resolve your queries, Thruskills team will provide you support and guidance of experts.


    After the end of the course, Thruskills certifies you as a Python Full Stack Developer based on the project you submit


    Why should I learn Python?

    With the knowledge of Python, you can develop various web applications, command line interfaces, mobile apps and a lot more. It is a good career move for people interested in making a career in web development.

    What are the skills needed to master Python

    Basic high school mathematics –Linear Algebra, Probability theory, basic calculus and basic statistics.

    What is the career progression and opportunities after learning Python?

    After completion of the Python course, you will find a good number of job opportunities.

    What is the price of this Python training?

    Python Training costs ₹36,000+GST, where you get to learn from a real-time expert with hands-on examples. You also get lifetime access to the course materials, 24x7 support to solve queries & doubts.

    How can a beginner learn Python?

    The curriculum has been designed by our veteran developers according to the industry requirements. This will help both experienced and beginners to learn from basics to advanced level. Your instructor will conduct classes where you will be guided through all the concepts along with the hands-on example.

    What is the average salary for a Python developer?

    According to Payscale, the average salary of a Python developer in India is around ₹5.01 Lakhs per annum