Course Overview
This Data Science program includes both case study as well as grooming sessions. We cover critical topics on Python programming, Machine Learning algorithms, along with practical projects which helps in better understanding. This course deals with preparing data for analysis and processing, performing advanced data analysis, and presenting the results to reveal patterns and enable stakeholders to draw informed conclusions
Advantage
This Professional Certificate from Handson has a comprehensive course curriculum covering Statistics, Machine Learning algorithms, key Programming Languages and more – with a great detail via our interactive learning model so that you are comfortable with any kinds of questions asked in job-interview. Upon successfully completing this course, you will be able to fast track your career in the field of interest and it will help you to kick start into an exciting profession in AI and Machine Learning.
What you'll learn
- Python Language
- Machine Learning
- Artificial Intelligence
Course Circullum
- What is Data Science?
- comparative study between Data Science and Big Data Analytics.
- Types of Data.
- The Data Science Lifecycle
- Data Acquisition and Preparation
- Data Modeling and Visualization
- Data Science Roles
- Benefits of Data Science
- Challenges of Data Science
- Business Use Cases for Data Science
- Concept of Analytics and Statistics
- Categories of Analytics
- Properties of Measurement
- Scales of Measurement
- Concept of Data visualization
- Measures of Central Tendency
- Measures of Dispersion
- Moments, Skewness and Kurtosis
- Concept of Correlation and Covariance
- Introduction to Probability Theory
- Probability Distributions
- Sampling and Estimation
- Testing of Hypothesis
- Introduction to python
- History of Python
- Internal & External IDLE
- Installation of Python &Anaconda
- Compiler & Interpreter
- Write your first program
- Data types, Input and output function
- Types of Operators
- Conditional Statement: if-else, if-elif-else, Nested if else
- Loop: While loop, For loop
- Nested while loop, Nested for loop Break, Continue and Pass
- Basic Data Types- Numeric & String
- Tuple and it’s operation
- List and it’soperation
- Dictionary and it’soperation
- Sets and It’soperation
- Basics Defining function
- Function call Return statement
- Function with parameter and without parameter
- Local and global variable
- Recursion, Anonymous (lambda) function
- User defined functions
- OOPS concepts Defining
- Class Creating object, Constructor
- Method vs function Calling methods
- Method Overriding, List of objects Inheritance
- Defining a file, Types offile and its operations
- Python read Files
- Python Write/Create Files
- Pickle Module
- Introduction to Numpy, Pandas, Matplotlib
- Array, Array indexing, Array operation
- Data frame, series, Groupby
- Missing values
- Box plot, Scatter plot, Chart styling
- Histogram, Bar chart etc.
- Group by plotting
- Concept of Supervised learning
- Concept of Unsupervised learning
- Concept of Reinforcement learning
- Simple Linear Regression
- Multiple Linear Regression
- Implementation of Linear Regression
- Advanced Topics: Normal Equation, Polynomial Regression, R-sq. Score
- Python Implementation
- Concept and Theory
- Sigmoid function
- Mathematical Concepts of Logistic Regression
- Binary and Multivariate Classification Problems
- Implementation of Logistic Regression
- K-Nearest Neighbors-Concept and Theory
- Implementation of K-Nearest Neighbors
- Support Vector Machine(SVM)-Concept and Theory
- Implementation of Support Vector Machine
- Naïve Bayes Classifier- Concept
- Implementation of Naïve Bayes Classifiert
- Decision Tree Classifier-Concept
- Implementation of Decision Tree Classifier
- Random Forest Classifier-Concept
- Implementation of Random Forest Classifier
- Dimensionality Reduction Problem- Curse of Dimensionality
- Principal Component Analysis(PCA)
- Implementation of PCA
- K-Means Clustering- Concept
- Implementation of K-Means Clustering
- Hierarchical Clustering- Concept
- Implementation of Hierarchical Clustering
- DBSCAN Clustering-Concept
- Implementation of DBSCAN Clustering
- Introduction of Deep Learning and Neural Network
- Types and Applications of Neural Network
- Skills required for Neural network
- Why Python is best for Neural Network
- Anaconda Installation: Spyder & Jupyter Notebook
- Introduction to Keras & Tensor Flow
- Installation of Keras & Tensor Flow
- ANN and Neuron Structure
- How does Neural Network Works?
- Practical Implementation of ANN
- Train-Test Splitting
- ANN model Training
- Activation Function
- Fit all the Layers
- Backpropagation
- Fitting to the training Dataset and finding Accuracy
- Image Reading and CNN Process
- Steps of CNN
- Conclusion of CNN Process
- Importing Required libraries
- Reading Cat & Dog Dataset
- Applying CNN layers
- Fitting the Dataset in Model
- Visualization of Accuracy and Loss
- Prediction with single image
- Introduction and Application
- Process of RNN, Types of RNN, Gradient Problem
- LSTM & GRU Explanation
- Steps of LSTM
- Creation of Data Structure with Time Steps
- LSTM layers
- Google Stock market prediction
Admission Process
Please call to admission counselor for course fees, registration fees, EMI fecilities,registration form and other formalities. Contact to admission counselor
+91-9831765780
+91-9830247087
Who can join?
Any graduate with knowledge of basic computing.
Requirment
1.Personal computer/laptop with webcam and microphone
2.Stable internet connections
Payment details
Bank Details:
KLMS HANDS-ON SYSTEMS PRIVET LIMITED
Account Number: 19700200000420
IFSC Code: BARB0SALTLA (5th letter is numeric zero)
UPI Payment: 9432257052@okbizaxis
This Course Include:
- Live Instructor-Led Course
- Project and Case Studies
- Certificate of completion
- Learn from Experts
- Placement Assistance
- Assistance for Global Certification
