Data Science & ML Using Python
Do you want to learn Data Science ML with Python at the best institute in Noida? Expert trainers teach Data Science ML using Python training classes with live projects at KASYFY in Noida. Undergraduates, graduates, working professionals, and freelancers will benefit from our Data Science Machine Learning with Python training program in Noida. We offer end-to-end Machine Learning with Python Domain training, as well as deeper dives, to help you build a successful career in any field.
This course is appropriate for both complete beginners with no prior programming experience and experienced developers who want to branch out into Data Science! Python is an interpreted, interactive, object-oriented, and general-purpose high-level programming language. Python is the most popular programming language in the IT industry right now. Python is now used in almost every aspect of IT, including Web development, cloud computing (AWS, Open Stack, VMware, Google Cloud, and so on), infrastructure automation, software testing, mobile testing, big data, and Hadoop, data science, and so on.
- Course Curriculam
- Why Python
- Application areas ofpython
- Python implementations
- Cpython
- Jython
- Ironpython
- Pypy
- Pythonversions
- Installingpython
- Python interpreter architecture
- Python byte code compiler
- Python virtual machine(pvm)
- Using interactive mode
- Using script mode
- General text editor and commandwindow
- Idle editor and idleshell
- Understanding print() function
- How to compile python programexplicitly
- Character set
- Keywords
- Comments
- Variables
- Literals
- Operators
- Reading input fromconsole
- Parsing string to int, float
- If statement
- If else statement
- If elif statement
- If elif else statement
- Nested if statement
- While loop
- For loop
- Nested loops
- Pass, break and continuekeywords
- Int, float, complex, bool,nonetype
- Str, list, tuple,range
- Dict, set, frozenset
- What is string
- String representations
- Unicode string
- String functions, methods
- String indexing andslicing
- String formatting
- Creating and accessinglists
- Indexing and slicinglists
- List methods
- Nested lists
- List comprehension
- Creating tuple
- Accessing tuple
- Immutability of tuple
- How to create a set
- Iteration over sets
- Python set methods
- Python frozenset
- Creating a dictionary
- Dictionary methods
- Accessing values fromdictionary
- Updating dictionary
- Iterating dictionary
- Dictionary comprehension
- Defining a function
- Calling a function
- Types offunctions
- Function arguments
- Positional arguments, keywordarguments
- Default arguments, non-defaultarguments
- Arbitrary arguments, keyword arbitraryarguments
- Function return statement
- Nested function
- Function as argument
- Function as return statement
- Decorator function
- Closure
- Map(), filter(), reduce(), any()functions
- Anonymous or lambdafunction
- Why modules
- Script v/smodule
- Importingmodule
- Standard v/s third partymodules
- Why packages
- Understanding pip utility
- Introduction to filehandling
- File modes
- Functions and methods related to filehandling
- Understanding with block
- Procedural v/s object orientedprogramming
- OOP principles
- Defining a class &objectcreation
- Object attributes
- Inheritance
- Encapsulation
- Polymorphism
- Difference between syntax errors andexceptions
- Keywords used in exceptionhandling
- try, except, finally, raise,assert
- Types of exceptblocks
- Need of regularexpressions
- Re module
- Functions /methods related toregex
- Meta characters &specialsequences
- Introduction to tkinterprogramming
- Tkinter widgets
- Tk, label, Entry, Textbox,Button
- Frame, messagebox, filedialogetc
- Layout managers
- Event handling
- Displaying image
- Multi-processing v/s Multi-threading
- Need of threads
- Creating child threads
- Functions /methods related tothreads
- Thread synchronization andlocking
- Database Concepts
- What is DatabasePackage?
- Understanding DataStorage
- Relational Database (RDBMS)Concept
- SQLbasics
- DML, DDL & DQL
- DDL: create, alter, drop
- SQLconstraints:
- Not null, unique,
- Primary & foreign key, compositekey
- Check, default
- DML: insert, update, delete andmerge
- DQL : select
- Select distinct
- SQLwhere
- SQLoperators
- SQLlike
- SQL orderby
- SQLaliases
- SQLviews
- SQLjoins
- Inner join
- Left (outer) join
- Right (outer) join
- Full (outer) join
- Mysql functions
- Stringfunctions
- Char_length
- Concat
- Lower
- Reverse
- Upper
- Numericfunctions
- Max, min, sum
- Avg, count,abs
- Date functions
- Curdate
- Curtime
- Now
- Sample or population
- Measures of central tendency
- Arithmetic mean
- Harmonic mean
- Geometric mean
- Mode
- Quartile First quartile
- Second quartile(median)
- Third quartile
- Standard deviation
- Introduction to probability
- Conditional probability
- Normal distribution
- Uniform distribution
- Exponential distribution
- Right & left skeweddistribution
- Random distribution
- Centrallimittheorem
- Normality test
- Mean test
- T-test
- Z-test
- ANOVA test
- Chi square test
- Correlation and covariance
- Difference between list and numpyarray
- Vector and matrixoperations
- Array indexing andslicing
- Labeled and structureddata
- Series and dataframe objects
- From excel
- From csv
- From html table
- at &iat
- loc&iloc
- head() & tail()
- describe()
- groupby()
- crosstab()
- boolean slicing /query()
- Map(), apply()
- Combining data frames
- Adding/removing rows &columns
- Sorting data
- Handling missing values
- Handling duplicacy
- Handling data error
- Scatter plot, lineplot, barplot
- Histogram, pie chart,
- Jointplot, pairplot, heatmap
- Outlier detection usingboxplot
- Traditional v/s Machine LearningProgramming
- Real life examples based onML
- Steps of MLProgramming
- Data Preprocessing revised
- Terminology related toML
- Classification
- Regression
- Math behind KNN
- KNN implementation
- Understanding hyperparameters
- Math behind KNN
- KNN implementation
- Understanding hyperparameters
- Math behind regression
- Simple linear regression
- Multiple linear regression
- Polynomial regression
- Boston price prediction
- Cost or loss functions
- Mean absolute error
- Mean squared error
- Root mean squared error
- Least square error
- Regularization
- Theory of logistic regression
- Binary and multiclass classification
- Implementing titanic dataset
- Implementing iris dataset
- Sigmoid and softmax functions
- Theory of SVM
- SVM Implementation
- kernel, gamma, alpha
- Theory of decision tree
- Node splitting
- Implementation with iris dataset
- Visualizing tree
- Random forest
- Bagging and boosting
- Voting classifier
- Cross validation
- Grid and random search for hyper parameter tuning
- Content based technique
- Collaborative filtering technique
- Evaluating similarity based on correlation
- Classification-based recommendations
- K-means clustering
- Hierarchical clustering
- Elbow technique
- Silhouette coefficient
- Dendogram
- Install nltk
- Tokenize words
- Tokenizing sentences
- Stop words customization
- Stemming and lemmatization
- Feature extraction
- Sentiment analysis
- CountVectorizer
- TfidfVectorizer
- Naive bayes algorithms
- Reading images
- Understanding gray scale image
- Resizing image
- Understanding haar classifiers
- Face, eyes classification
- How to use webcam in open cv
- Building image data set
- Capturing video
- Face classification in video
- Creating model for gender prediction
- What is artificial neural network (ANN)?
- How neural network works?
- Perceptron
- Multilayer perceptron
- Feedforward
- Back propagation
- What is deep learning?
- Deep learning packages
- Deep learning applications
- Building deep learning environment
- Installing tensor flow locally
- Understanding google colab
- What is tensorflow?
- Tensorflow 1.x v/s tensorflow 2.x
- Variables, constants
- Scalar, vector, matrix
- Operations using tensorflow
- Difference between tensorflow and numpy operations
- Computational graph
- What does optimizers do?
- Gradient descent (full batch and min batch)
- Stochastic gradient descent
- Learning rate , epoch
- What does activation functions do?
- Sigmoid function,
- Hyperbolic tangent function (tanh)
- ReLU –rectified linear unit
- Softmax function
- Vanishing gradient problem
- Using scikit implementation
- Using tensorflow
- Understanding mnist dataset
- Initializing weights and biases
- Gradient tape
- Defining loss/cost function
- Train the neural network
- Minimizing the loss by adjusting weights and biases
- SGD with momentum
- RMSprop
- AdaGrad
- Adam
- Dropout layers and regularization
- Batch normalization
- What is keras?
- Keras fundamental for deep learning
- Keras sequential model and functional api
- Solve a linear regression and classification problem with example
- Saving and loading a keras model
- Introduction to CNN
- CNN architecture
- Convolutional operations
- Pooling, stride and padding operations
- Data augmentation
- Building,training and evaluating first CNN model
- Model performance optimization
- Auto encoders for CNN
- Transfer learning and object detection using pre-trained CNN models
- LeNet
- AlexNet
- VGG16
- ResNet50
- Yolo algorithm
- What is word embedding?
- Word2vec embedding
- CBOW
- Skipgram
- Keras embedding layers
- Visualize word embedding
- Google word2vec embedding
- Glove embedding
- Introduction to RNN
- RNN architecture
- Implementing basic RNN in tensorflow
- Need for LSTM and GRU
- Deep RNN/LSTM/GRU
- Text classification using LSTM
- Prediction for time series problem
- Seq-2-seq modeling
- Encoder-decoder model
- Introduction to GAN
- Generator
- Discriminator
- Types of GAN
- Implementing GAN using neural network
- Text to speech
- Speech to text
- Automate task using voice
- Voice search on web
- Stock Price Prediction Using LSTM
- Object Detection
- Attendance System Using Face Recognition
- Facial Expression and Age Prediction
- Neural Machine Translation
- Hand Written Digits& Letters Prediction
- Number Plate Recognition
- Gender Classification
- My Assistant for Desktop
- Cat v/s Dog Image Classification
Frequently Asked Questions (FAQ’s)
Anybody with prior domain knowledge in respective fields can get into Data Science & Ml Using Python.
Different payment options are available suiting your needs. We accept credit cards, debit cards cheque cash , netbanking, money wallets.
With tieup through our job consultancy firm, Kasyfy, we make sure each and every candidate in placed with a top tier firm.
With guaranteed results from last 17 Years, we make you stand out in the market with hands on knowledge and better understanding of current scenarios in the market, in turn helping your to automatically get placed with MNCs.
With approximate range between 2 to 3 months, there are more options like fast track training and online training options are also available. Fees is reasonable according to market and industry standards.
Training Highlights
- Customized course
- Live Projects
- Mock Test
- Multiple Training Modes
- Certified Trainer
- Official Course
- 24*7 Mentoring
