Boost Your Career with Best Data Science & Machine Learning Course Institute in Delhi

 
Coding Bytes is the Best Data Science & Machine Learning Training, Course, Certification Institute in Delhi, Our Data Science & Machine Learning Course leads the chart among the best Data Science & Machine Learning Training Courses of Delhi NCR.

 

  • Data Science & Machine Learning Course Fees: Rs. 28999/-
  • Data Science & Machine Learning Course Duration: 6 months
  • 100% Placement Assistance
  • 24*7 Expert Support
  • Online & Offline Mode
  • Affordable Fees
  • Easy EMI
  • Live Projects Experience

 

Request a Call Back!

Data Science & Machine Learning

Course Highlights

  • Introduction to Data Science
  • Python with Data Science
  • Matplotlib Module
  • Scipy
  • Scikit
  • Machine Learning
  • Data Science & Machine Learning
  • understanding Machine Learning
  • Theano
  • Trensorflow
  • Importance
  • Artificial intelligence

Data Science & Machine Learning Course Details

1) Introduction to Data Science

  • What is Data Science?
  • Applications and Use Cases of Data Science
  • Data Science vs. Machine Learning vs. AI
  • Roadmap to Become a Data Scientist

2) Python for Data Science

    • Python Basics: Variables, Data Types, and Operators
    • Control Flow Statements (Loops and Conditionals)
    • Functions and Modules
    • Object-Oriented Programming in Python
    • Working with Libraries: NumPy, Pandas, Matplotlib, Seaborn
3) Data Handling and Preprocessing
  • Importing and Exporting Data (CSV, Excel, JSON, SQL)
  • Handling Missing Values and Outliers
  • Data Cleaning and Transformation
  • Feature Engineering and Selection
  • Exploratory Data Analysis (EDA)
4) Statistics and Probability for Data Science
  • Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation
  • Probability Theory and Distributions (Normal, Binomial, Poisson)
  • Hypothesis Testing and Confidence Intervals
  • Correlation and Covariance

 

5) Data Visualization
  • Introduction to Data Visualization
  • Matplotlib and Seaborn for Plotting
  • Advanced Visualization Techniques (Heatmaps, Pairplots, Violin Plots)
  • Interactive Visualizations using Plotly
6) Machine Learning Basics
  • Introduction to Machine Learning
  • Supervised vs. Unsupervised Learning
  • Regression vs. Classification
  • Performance Metrics (Accuracy, Precision, Recall, F1-score)
7) Supervised Learning
  • Linear Regression and Multiple Regression
  • Logistic Regression
  • Decision Trees and Random Forest
  • Support Vector Machines (SVM)
  • Hyperparameter Tuning and Model Evaluation
8) Unsupervised Learning
  • K-Means Clustering
  • Hierarchical Clustering
  • Principal Component Analysis (PCA)
  • Anomaly Detection
9) Deep Learning (Introduction)
  • Basics of Neural Networks
  • Introduction to TensorFlow and Keras
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)

 

10) Natural Language Processing (NLP)
  • Text Preprocessing (Tokenization, Lemmatization, Stopwords)
  • Sentiment Analysis
  • Named Entity Recognition (NER)
  • Transformer Models (BERT, GPT Overview)
11) Big Data and Cloud Computing
  • Introduction to Big Data
  • Apache Spark for Data Science
  • Cloud Platforms (AWS, GCP, Azure) for Data Science
  • Deploying Machine Learning Models on Cloud
12) Matplotlib Module
  • Introducation
  • Environment Setup
  • Anaconda Distribution
  • Juypter Notebook
  • Pyplot API
  • Simple Plot
  • PyLab Module
  • Axes Class
  • Figure Class
  • Multiplots
  • Subplots() Function
  • Subplot2grid() Function
  • Grids
  • Formatting Axes
  • Twin Axes
  • Bar Plot
  • Histogram
  • Pie Chart
  • Scatter Plot
  • Contour Plot
  • Quiver Plot
  • Box Plot
  • Violin Plot
  • 3-dimensional Plotting
  • 3D Contour Plot
  • 3D Wireframe Plot
  • 3D Surface Plot
  • Working With Text
  • Mathematical Expressions
  • Working with Images
  • Transforms.
13) Scipy
  • Introducation
  • Environment Setup
  • Basic Functionality
  • Cluster
  • Constants
  • FFT pack
  • Integrate
  • Interpolate
  • Input and Output
  • Linalg
  • Ndimage
  • Optimize
  • Stats
  • CSGraph
  • Spatial
  • ODR
  • Special Package
14) Scikit
  • Introducation
  • Modelling Process
  • Data Representation
  • Estimator API
  • Conventions
  • Linear Modeling
  • Extended Linear Modeling
  • Grandient Descent
  • Support Vector Machine
  • Anomaly Detection
  • K-MearestNeighbours
  • KNN Learning
  • Classification with Naïve Bayes
  • Decision Trees
  • Rendomized Decision Trees
  • Boosting Methods
  • Clustering Methods
  • Clustering Performance Evaluation
  • Dimensionality Reduction using PCA
15) Theano
  • Introducation
  • Installation
  • A Trivial Theano Expression
  • Expression for matrix Multiplication
  • Computational Graph
  • Data types
  • Variables
  • Shared Variables
  • Functions
16) Trensorflow
  • Introducation
  • Installation
  • Understanding Artificial Intelligence
  • Mathematical Foundations
  • Machine Learning Learning& Deep Learning
  • TensorFlow Basics
  • Convolutional Neural Networks
  • Recurrent Neual Networks
  • TensorBoard Visualization
  • TensorFlow- WordEmbedding
  • Single Layer Perceptron
  • Linear Regression
  • TFLearn and its installation
  • CNN and RNN Difference
  • Keras
  • Distributed Computing
  • Exporting, Multi-Layer Perceptron Learing
  • Hidden Layers of Perceptron
  • Optimizers
  • XOR Implementation
  • Gradient Descent Optimization
  • Forming Graphs
  • Image Recongnition using TensorFlow
17) Projects and Case Studies
  • End-to-End Data Science Project

    17) Projects and Case Studies

 

17) Projects and Case Studies
  • End-to-End Data Science Project

     

Enroll Now !

Small Batches

Mentoring By Experts

Flexible Schedule

i

Learn By Doing

Goal Oriented