Ön Koşul
Temel Python programlama bilgisi ve gerekli veri analizinin temelleri, temel istatistik ve matematik bilgisine sahip olunması tavsiye edilmektedir.
Eğitim Hakkında
Python ile Makine Öğrenimi kursu, denetimli, denetimsiz ve güçlendirmeden oluşan üç ana makine öğrenimi algoritması türü hakkında derinlemesine bir anlayış sunarak, ekiplerinize konuşlandırılabilir makine öğrenimi modelleri oluşturmaya yönelik ciddi bir başlangıç ve pratik yaklaşım sağlamaya odaklanır. Makine Öğrenimi eğitimi ayrıca, Doğal Dil İşleme (NLP) ve Yapay Sinir Ağları gibi ileri düzey Makine Öğrenimi konularını da tanıtır.
Kimler içindir?
▪ Geliştiriciler
▪ Veri Mimarları
▪ Bir Analist ekibiyle ilgilenen Teknik Liderler
▪ Tahmine Dayalı Analitikte uzmanlık kazanmak isteyen Veri Analisti
Sertifika:
Eğitimlerimize %80 oranında katılım gösterilmesi ve eğitim müfredatına göre uygulanacak sınav/projelerin başarıyla tamamlanması durumunda, eğitimin sonunda dijital ve QR kod destekli “BT Akademi Başarı Sertifikası” verilmektedir.
Eğitim İçeriği
▪ 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
▪ Types of ML algorithms
▪ Labelled Dataset
▪ Training and Testing Data
▪ Importing the Libraries
▪ Importing the Dataset
▪ Demo: Creating a machine model
▪ 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
▪ Confusion Matrix
▪ Precision, Recall
▪ F1-score
▪ RoC, AuC
▪ n-fold cross validation
▪ Measuring classifier performance
▪ Overfitting
▪ Ensemble Learning
▪ Bagging and Boosting
▪ 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
▪ 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
▪ What is reinforcement learning
▪ Applications of reinforcement learning
▪ An Example use case
▪ Components of RL
▪ Approachs to RL
▪ RL algorithms
▪ Deep reinforcement learning
▪ What is NLP?
▪ Why NLP
▪ Applications of NLP
▪ Components of NLP
▪ NLP techniques
▪ Why deep learning?
▪ Neural networks
▪ Applications of neural networks
▪ Biological Neuron vs Artificial Neuron
▪ Artificial Neural networks, layers
Neden Bu Eğitimi Almalısınız ?
▪ 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
Önemli Notlar
Program ücretlerine KDV dahil değildir.
Paylaş: